Editor's Note: Sensors, as the "nerve endings of the information age," have penetrated every critical field of the socio-economic landscape. Since October 2025, China Electronics News has invited Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Jiusan Society Central Committee and Executive Vice Chairman of the China Sensor and IoT Industry Alliance, to open a column titled "Guo Yuansheng Explains Sensors." Focusing on eight major fields and scenarios: electric power, major equipment, intelligent manufacturing, smart agriculture, smart healthcare and big health, smart home appliances and consumer electronics, urban security, and low-altitude economy, articles such as "Sensors 'Stationed' on the Power Generation Side: The Cornerstone of Stable Operation in New Power Systems" and "Sensors for Energy Storage Clarify Three Core Development Directions" have been published successively, receiving widespread attention and high praise from readers. This publication is the second article in the smart agriculture field, focusing on agricultural machinery sensors, elaborating on their application prospects, industrial status, and future suggestions to build industrial consensus and promote industrial development.
Agricultural machinery, as the core carrier of the "execution end" in smart agriculture, is the key link in closing the loop of "perception-decision-execution." Unlike the standardized scenarios of industrial production, agricultural operations face a complex reality: significant morphological differences across the four major stages of plowing, sowing, managing, and harvesting; a wide variety of crop categories (grains, fruits, vegetables, root crops, cash crops, etc.); dispersed planting scales (coexistence of thousand-mu-scale bases and ten-mu smallholders); and prominent customized needs. Traditional agricultural machinery operations rely on manual experience, causing pain points such as "blind operations, insufficient precision, and poor adaptability," leading to resource waste, environmental pollution, and crop quality fluctuations due to experience-dependent harvest timing.
The emergence of intelligent perception technology provides core support for solving the above problems. By integrating various sensors into agricultural machinery, it achieves real-time and precise perception of data such as soil environment, crop status, operation parameters, and equipment safety, building a four-dimensional precise matching system of "crop-environment-machinery-sensor." This holds practical significance in promoting the transformation of agricultural production from "experience-driven" to "data-driven" and intelligent.
Current Development Status of Agricultural Machinery at Home and Abroad and Sensor Supporting Patterns
Types and Functions of Agricultural Machinery at Home and Abroad
Operation scenarios, planting scales, and crop characteristics form a multi-dimensional coverage system:
1. Classified by operation stage: Covers the entire agricultural production process, including tillage machinery (plows, rotary cultivators, ditchers, subsoilers, etc., with the core function of improving soil structure and laying the foundation for sowing); sowing and planting machinery (precision seeders, hill drop planters, transplanters, seedling raisers, etc., with the core function of ensuring precise placement of seeds/seedlings); field management machinery (variable rate fertilizers, plant protection UAVs, irrigators, cultivators, etc., with the core function of meeting the water, fertilizer, and pest/disease control needs during crop growth); and harvesting machinery (combine harvesters, fruit and vegetable pickers, threshers, sorters, etc., with the core function of efficient and lossless harvesting and improving product quality).
2. Classified by scale adaptation: Adapts to different planting scenario needs. Large-scale agricultural machinery (over 1,000 mu) features high efficiency and full automation as core characteristics, such as large combine harvesters and self-propelled variable rate fertilizers, with daily operation area ≥200 mu; medium-sized specialized machinery (around 100 mu) balances efficiency and flexibility, such as greenhouse water-fertilizer integrated machines and small plant protection machines, with daily operation area of 100-200 mu, adapting to facility agriculture and medium-sized planting bases; small portable machinery (under 10 mu) focuses on low cost and easy operation, such as manual precision seeders and portable pickers, meeting the dispersed planting needs of smallholders.
3. Classified by crop adaptation: Customized for the biological characteristics of different crops. Specialized machinery for grain crops (models adapted for rice, wheat, corn) focuses on large-scale and high efficiency, such as rice high-speed transplanters and corn combine harvesters; specialized machinery for fruit and vegetable crops (models adapted for tomatoes, citrus, strawberries) focuses on lossless operations and precise control, such as strawberry transplanters and citrus pickers; specialized machinery for root/cash crops (models adapted for yams, cotton, Chinese medicinal materials) focuses on directional operations and deep adaptation, such as yam-specific ditchers and cotton pickers.
Comparison of Digitalization and Intelligence Levels of Agricultural Machinery at Home and Abroad
1. Overseas Development Status (Taking Europe, America, and Japan as Examples)
Europe, America, and Japan, as powerhouses in agricultural mechanization, started early and have high levels of digitalization and intelligence in agricultural machinery. The core characteristics are "large-scale operations + full-process automation." At the technical level, large agricultural machinery generally supports fully automatic driving, multi-sensor fusion decision-making, and cross-platform data collaboration. For example, John Deere's smart agricultural machinery can achieve a closed loop of operation planning, real-time monitoring, and data analysis through cloud platforms. Representative models include the John Deere S790 combine harvester (supporting automatic navigation, real-time crop yield monitoring, and dynamic adjustment of threshing parameters), the Kubota smart transplanter (equipped with seedling height sensors and planting depth control systems, with a survival rate of over 98%), and the Case New Holland AFX8010 baler (integrating load sensors and automatic baling systems, improving operation efficiency by 30%). The core advantage lies in the deep integration of sensors with the original agricultural machinery manufacturer, resulting in high operation precision (error of key parameters ≤2%) and high automation degree (low frequency of manual intervention). Moreover, the algorithms have been verified by long-term large-scale planting, showing outstanding stability.
2. Domestic Development Status
China's digitalization and intelligence in agricultural machinery started later but is comprehensively catching up. At the technical level, large agricultural machinery has achieved main functions such as automatic driving and variable rate operations; medium-sized machinery focuses on facility agriculture and specialized operation scenarios; small machinery focuses on the implementation of low-cost perception solutions. Representative models include the Zoomlion PL2304 smart tractor (equipped with a BeiDou automatic driving system, path deviation ≤5cm, supporting integrated operations of variable rate fertilization and precision sowing), the DJI T40 plant protection UAV (integrating visual obstacle avoidance and flow closed-loop control, with spraying uniformity error ≤5%), and the XAG P100 agricultural unmanned vehicle (supporting multi-scenario switching for sowing, fertilization, and plant protection, with a daily operation area of up to 500 mu). The main characteristics are adapting to the domestic planting scenarios of "coexistence of large, medium, and small scales, and complex crop categories," accelerating the domestic substitution of sensors, with costs only 1/2 to 1/3 of similar foreign products, strong policy support (such as agricultural machinery purchase subsidies and smart agriculture pilot projects), and rapidly increasing market acceptance.
3. Comparison of Gaps and Advantages
The core advantages of foreign agricultural machinery intelligence are concentrated in multi-sensor fusion algorithms (such as the collaborative perception accuracy of LiDAR + vision + spectrum), core component precision (such as MEMS chip measurement accuracy reaching ±0.01cm), and ecological closed loops (deep collaboration of machinery-sensor-cloud platform). The core competitiveness of domestic machinery is reflected in scenario adaptability (such as small machinery for hilly and mountainous areas, modular equipment for greenhouses), policy support (the national rural revitalization strategy and smart agriculture special policies), and cost control (the cost-performance advantage of domestic sensors and machinery). In addition, China has more scenario advantages in the application of technologies such as BeiDou positioning and IoT (Internet of Things) communication. For example, the adaptability of BeiDou RTK positioning in complex terrains is superior to GPS.
Sensor Technology, Types, Functions, and Supporting Status of Agricultural Machinery at Home and Abroad
1. Comparison of Core Technical Paths
(1) Foreign Technical Characteristics (Mainly Europe, America, and Japan): Foreign countries take "multi-sensor fusion + deep AI integration" as the core path, emphasizing full-process automation and high-precision control. At the perception technology level, it integrates various technologies such as LiDAR, hyperspectral imaging, inertial navigation, and machine vision to form a three-in-one perception system of "environment-crop-equipment." For example, the "vision + spectrum + positioning" triple perception system equipped on John Deere machinery can capture crop growth, soil status, and machinery posture data in real time, supporting autonomous decision-making in complex scenarios (such as dynamically adjusting tillage depth and sowing amount).
Technical highlights are prominent: First, the application of high-precision MEMS chips is mature, with measurement accuracy reaching ±0.01cm and high long-term stability indicators. Second, edge computing algorithms are deeply integrated, with local data processing delay ≤100ms, avoiding the impact of cloud transmission delay on operation efficiency. For example, the seedling height recognition algorithm of transplanters can adjust planting depth in real time. Third, harsh environment design covers a working temperature range of -40℃~85℃, with a protection level of IP68, capable of resisting complex agricultural environments such as high temperature, high humidity, and dust, adapting to large-scale and high-intensity operation scenarios. Fourth, data synchronization is strong (error ≤0.5%), and algorithms are optimized for large-scale planting of single crops (such as special perception algorithms for wheat and corn), showing outstanding operation stability and enabling long-term continuous automated operations.
(2) Domestic Technical Characteristics: Domestic perception technology integrates core technologies such as BeiDou positioning, low-power IoT (Internet of Things), and modular sensing. For example, the "visual obstacle avoidance + flow closed-loop control" technology of DJI plant protection UAVs not only meets the needs of precise pesticide application but also controls equipment costs.
Technical characteristics are: First, the mass production of domestic MEMS sensors has achieved a breakthrough, with measurement accuracy reaching ±0.1cm, and scaled application has been realized. Second, the integration of BeiDou positioning and inertial navigation technology is mature, with positioning accuracy reaching ±2cm RTK, and stability in complex terrains (such as hilly and mountainous areas) is superior to similar foreign technologies. Third, low-power IoT communication technology is widely applied, with sensor battery life ≥6 months. For example, the application of LoRa/NB-IoT communication modules in small agricultural machinery sensors solves the power supply problem in remote areas.
2. Mainstream Types and Function Adaptation
(1) Classified by Technical Principles and Core Functions
| Technology Type | Foreign Products/Technology | Domestic Products/Technology | Core Function Implementation |
| Optical Perception (Hyperspectral / Machine Vision) | Hyperspectral camera (16 bands, 400-1000nm, spectral resolution ≤3nm) | Multispectral sensor (6 bands, 400-900nm, spectral resolution ≤15nm) | Crop growth assessment (index calculation), pest and disease identification (lesion features), maturity detection (color/spectral features), supporting variable rate fertilization/pesticide application decisions, adjusting fertilization amount based on crop growth differences, and precise pesticide application in pest and disease areas |
| Mechanical Perception (Pressure / Torque / Force) | Agricultural machinery torque sensor (measurement range 0-2000N・m, accuracy ±0.5% FS) | Agricultural machinery pressure sensor (measurement range 0-1000N・m, accuracy ±1% FS) | Monitor agricultural machinery load (such as tractor driveshaft torque), sowing pressure (to avoid seed damage), and picking force (to prevent fruit and vegetable damage), avoiding equipment overload or crop damage, and extending the service life of agricultural machinery |
| Positioning and Navigation (GPS / BeiDou + Inertial Navigation) | Positioning module (accuracy ±1cm, dynamic response time ≤0.5s) | Agricultural machinery positioning terminal (positioning accuracy ±2cm RTK, dynamic response time ≤1s) | Delineate operation boundaries, automatic driving (path planning and deviation correction), and operation area calculation, solving the problem of missed/repeated plowing, improving operation efficiency. For example, automatic driving of large tractors can reduce manual intervention by 80% |
| Environmental Perception (Temperature and Humidity / Soil Moisture / Fertility) | Soil moisture sensor (measurement range 0-100%vol, accuracy ±1%vol) | Soil fertility sensor (NPK measurement range 0-500mg/kg, accuracy ±5% FS) | Collect soil temperature, humidity, moisture, and fertility data, and meteorological parameters (temperature, humidity, wind speed), supporting precise irrigation (adjusting irrigation amount based on soil moisture) and fertilization plan formulation (variable rate fertilization based on fertility differences) |
| Flow / Counting (Flow / Rotational Speed / Yield) | Grain flow sensor (measurement range 0-500kg/h, accuracy ±2% FS) | Sowing count sensor (measurement range 0-1000 grains/min, accuracy ±1%) | Precisely control sowing amount (such as 2-3kg per mu for corn precision sowing), fertilization amount (flow control of variable rate fertilizer), and pesticide spraying amount (flow adjustment of plant protection UAV), measure operation yield, and realize production data traceability |
(2) Function Adaptation Differences: Foreign sensor functions focus on "large-scale precise execution," emphasizing deep optimization of single functions. For example, the real-time monitoring sensor for grain loss in combine harvesters can accurately measure the loss per mu and dynamically adjust threshing parameters; the dynamic regulation sensor for fertilization amount per mu in variable rate fertilizers is optimized for the uniformity needs of large-scale grain planting, with single functions but high precision and strong stability.
Domestic functions focus on "multi-scenario compatibility," balancing the needs of different crops and planting scales. For example, small agricultural machinery sensors can simultaneously support sowing depth monitoring and seed counting functions, meeting the multi-crop planting needs of smallholders; medium-sized agricultural machinery sensors integrate dual functions of "environmental perception + operation parameter monitoring." For example, the EC/pH sensor of the greenhouse water-fertilizer integrated machine simultaneously monitors nutrient solution concentration and acidity/alkalinity, adapting to the refined management needs of facility agriculture. In addition, domestic sensors pay more attention to ease of operation. For example, small sensors are equipped with mobile APP data viewing functions, which can be used without a professional technical background.
3. Supporting Status and Adaptation Characteristics
(1) Foreign Supporting Mode: Foreign countries mainly adopt "original factory deep integration + exclusive protocols." Sensors and agricultural machinery are developed and produced by the same enterprise, with limited compatibility of third-party sensors. For example, Case New Holland machinery prioritizes matching its own customized sensors, and the hyperspectral sensors of John Deere machinery are original factory exclusive products with low third-party sensor compatibility. The supporting characteristics are manifested as a closed-loop ecosystem of "machinery-sensor-cloud platform." Data interfaces use exclusive protocols (proprietary) and are not open to the outside world, supporting full-process automated operations. For example, the automatic linkage of "sensor data-transplanting parameters" in smart transplanters requires no manual intervention, enabling full automation from seedling height recognition to planting depth adjustment.
The adaptation scenarios are mainly large-scale grain planting of over 1,000 mu. The unit price of supporting sensors is high. For example, the unit price of 16-band hyperspectral sensors is ≥80,000 RMB, which is unaffordable for smallholders. They are mainly applied to large-scale operating entities such as large farms and agricultural cooperatives.
(2) Domestic Supporting Mode: The domestic supporting characteristics are manifested as modular design supporting cross-brand adaptation. Data interfaces are gradually unifying towards industry standards (such as LoRa/NB-IoT communication protocols and JSON data formats). "Sensor + machinery + APP" lightweight solutions are launched to lower the adaptation threshold. Adaptation scenarios fully cover large, medium, and small agricultural machinery and multi-crop planting. The unit price of high-end sensors (such as 16-band hyperspectral cameras) is about 30,000-50,000 RMB, only 1/2 to 1/3 of similar foreign products. The unit price of low-cost sensing modules for small agricultural machinery (such as simple depth sensors and flow sensing modules) is ≤800 RMB, adapting to the budgets of smallholders.
(3) Core Supporting Gaps: Although domestic agricultural machinery sensors are developing rapidly, there are still three core gaps: First, the high-end technology gap. Core components such as hyperspectral sensors (spectral resolution ≤5nm) and micro force sensors (accuracy ±0.01kg) still rely on imports. Domestic products have gaps in long-term stability and anti-interference. For example, the environmental adaptability of hyperspectral sensors (spectral stability under high temperature and high humidity) needs to be improved. Second, the category adaptation gap. There is a shortage of exclusive sensors for characteristic crops (such as Chinese medicinal materials and tropical fruits). For example, there is a shortage of dual-parameter sensors for moisture content + tenderness specifically for tea picking, and depth and spacing sensors specifically for Chinese medicinal material sowing. Third, the supporting standard gap. Data interfaces of agricultural machinery and sensors from different brands are not unified, leading to difficulties in cross-device data collaboration. For example, the sensor data of Brand A's fertilizer applicator cannot be directly connected to Brand B's plant protection UAV, affecting the full-process data closed loop.
Plowing-Sowing-Managing-Harvesting + Crop Categories: Digitalization of Agricultural Machinery and Sensor Adaptation Needs
Tillage Stage: Sensor Adaptation Needs by Crop Category
The core goal of the tillage stage is to achieve precise tillage depth, qualified soil fineness, and uniform plot flatness. The biological characteristics of different crops determine the differences in key operation parameters, thereby affecting sensor adaptation needs:
1. Grain Crops (Rice, Wheat, Corn)
The demand for large-scale planting of grain crops is prominent. The tillage stage needs to balance efficiency and uniformity. Digitalization needs focus on "uniform control of large-scale tillage depth and dynamic adjustment of soil compactness" to avoid uneven emergence and growth caused by inconsistent tillage depth. Core sensor configurations include: soil compactness sensor (measurement range 0-1000kPa, accuracy ±20kPa), which monitors soil compactness in real time. When compactness exceeds 300kPa, it links with agricultural machinery to adjust tillage depth and traction to avoid soil compaction; tillage depth sensor (measurement range 0-30cm, accuracy ±0.2cm), with rice fields adapted to 15-20cm, corn fields 20-25cm, and wheat fields 18-22cm, ensuring uniform depth across the whole plot; terrain slope sensor (measurement range 0-30°, accuracy ±0.1°), linking with the automatic driving system to adjust machinery posture and avoid uneven plots. Adapted machinery is mainly large combined soil preparers and rotary cultivators, supporting thousand-mu-scale operations.
2. Fruit and Vegetable Crops (Tomatoes, Strawberries, Citrus)
Fruit and vegetable crops have relatively shallow root systems (tomato root depth 20-30cm, strawberry root depth 10-15cm). The tillage stage needs to avoid root damage. Digitalization needs focus on "shallow tillage to avoid root damage and soil moisture linked to tillage intensity." Core sensor configurations include: shallow tillage depth sensor (measurement range 0-15cm, accuracy ±0.3cm), with tomato greenhouse tillage depth controlled at 8-10cm, strawberry greenhouse 5-8cm, and citrus orchard 10-15cm; soil moisture sensor (measurement range 0-100%vol, accuracy ±2%vol), which reduces tillage intensity when soil moisture is below 20%vol to avoid water loss caused by overly loose soil; soil fineness sensor (particle size identification range 0-5cm, identification accuracy ≥90%), ensuring qualified soil fineness and providing good conditions for seedling transplantation. Adapted machinery is mainly greenhouse rotary cultivators and orchard ditchers, balancing flexibility and precision.
3. Root/Cash Crops (Yams, Cotton)
Root crops (yam root depth 50-80cm) require directional ditching, and cash crops (cotton) require deep tillage to improve soil permeability. Digitalization needs focus on "precise directional ditching depth and soil fertility adapted to tillage methods." Core sensor configurations include: directional depth sensor (measurement range 0-100cm, accuracy ±0.5cm), with yam-specific ditcher tillage depth controlled at 60-70cm to ensure root growth space; soil fertility sensor (NPK measurement range 0-500mg/kg, accuracy ±5%FS), adjusting tillage methods based on soil fertility differences, using deep tillage (30-40cm) in low fertility areas and shallow tillage (20-25cm) in high fertility areas; soil salinity sensor (measurement range 0-20g/L, accuracy ±0.1g/L), targeting cotton planting in saline-alkali land, monitoring soil salinity, and linking with irrigation systems for salt washing operations during tillage. Adapted machinery is mainly specialized ditchers and deep tillers, emphasizing directional operations and depth control.
Sowing/Planting Stage: Sensor Adaptation Needs by Crop Category
The core goal of the sowing/planting stage is to achieve precise sowing/planting depth, uniform sowing amount/plant spacing, and high seedling survival rate. The significant differences in seed size and seedling morphology of different crops put forward personalized needs for sensor adaptation:
1. Grain Crops (Corn, Wheat)
The sowing amount of grain crops needs to be precise (2-3kg per mu for corn, 10-15kg per mu for wheat), and the uniformity of plant spacing affects yield. Data focuses on "precision sowing, uniform plant spacing, and dynamic adjustment of sowing amount." Core sensor configurations include: seed counting sensor (accuracy ±1%), enabling single-grain sowing for corn precision seeders to avoid resource waste and seedling competition caused by multi-grain sowing; plant spacing sensor (measurement range 0-50cm, accuracy ±0.2cm), controlling corn plant spacing at 25-30cm and wheat row spacing at 15-20cm to ensure ventilation and light transmission; GPS positioning sensor (±2cm RTK), delineating sowing boundaries to avoid missed/repeated sowing; soil moisture sensor (measurement range 0-100%vol, accuracy ±2%vol), with corn baseline sowing depth at 3-5cm. When soil moisture is below 18% vol, it dynamically adjusts the sowing depth to 3.5-6cm to improve the emergence rate. Adapted machinery is mainly large precision seeders, supporting large-scale and high-efficiency operations.
2. Fruit and Vegetable Crops (Tomatoes, Cucumber Seedlings)
Transplanting fruit and vegetable seedlings needs to avoid root damage. Planting depth and row spacing affect later growth. Data focuses on "planting depth adapted to seedling height and row spacing ensuring ventilation." Core sensor configurations include: seedling height sensor (measurement range 0-30cm, accuracy ±0.3cm), adjusting planting depth according to seedling height (when seedling height is 10-15cm, planting depth is 5-6cm); planting depth sensor (measurement range 0-15cm, accuracy ±0.2cm), ensuring uniform transplanting depth to avoid root rot caused by too deep or lodging caused by too shallow; row spacing sensor (measurement range 0-60cm, accuracy ±0.5cm), controlling tomato row spacing at 40-50cm and cucumber row spacing at 30-40cm to ensure ventilation, light transmission, and later field management; seedling survival rate monitoring sensor (image recognition accuracy ≥95%), providing real-time feedback on transplanting quality and timely adjusting operation parameters. Adapted machinery is mainly medium-sized greenhouse transplanters, balancing flexibility and refined operations.
3. Rare/Characteristic Crops (Chinese Medicinal Materials, Strawberries)
Seeds of such crops are expensive (e.g., Chinese medicinal material seeds cost over 1,000 RMB per kg) and seedlings are delicate. Digitalization needs focus on "precise control of sowing amount and avoiding seedling damage." Sensor configurations include: simple depth sensor (measurement range 0-8cm, accuracy ±0.5cm), controlling strawberry sowing depth at 0.5-1cm and Chinese medicinal material sowing depth at 1-2cm; seedling carrying capacity sensor (accuracy ±2%), monitoring the remaining amount of seedlings in real time and replenishing them in time to avoid missed planting; sowing pressure sensor (measurement range 0-5kg, accuracy ±0.1kg), controlling sowing/planting pressure to avoid damaging seeds or seedling roots; temperature sensor (measurement range 0-50℃, accuracy ±0.2℃), issuing a warning when soil temperature is below 15℃ to avoid the impact of low temperature on the emergence rate. Adapted machinery is mainly small manual planters and portable seeders, with flexible operations adapting to small-area planting.
Field Management Stage: Sensor Adaptation Needs by Crop Category
Its core goal is to achieve precise targeting of fertilization/pesticide application, adapt the water-fertilizer ratio to crop needs, and prevent and control pests and diseases early. The significant differences in nutritional needs and pest/disease types of different crops require targeted optimization for adaptation:
1. Grain Crops (Rice, Wheat)
Large-scale grain planting needs to balance efficiency and resource conservation. Digitalization needs focus on "variable rate fertilization, large-scale plant protection, and on-demand irrigation supply." Sensor configurations include: multispectral growth sensor (400-900nm, accuracy ±3%), evaluating crop growth through NDVI index and increasing fertilization amount in weak growth areas (adding 5-10kg of urea per mu); soil fertility sensor (NPK measurement range 0-500mg/kg, accuracy ±5%FS), dynamically monitoring soil nutrient changes and adjusting fertilization ratios (NPK ratio of 3:1:1 during rice tillering stage and 1:1:3 during filling stage); fertilization amount sensor (measurement range 0-800kg/h, accuracy ±2%FS), controlling the flow of variable rate fertilizers to ensure uniform fertilization; pesticide flow sensor (measurement range 0-10L/min, accuracy ±1% FS), adjusting pesticide spraying amount by plant protection UAV clusters according to pest/disease density; soil moisture sensor (multi-depth 20/40cm, accuracy ±2%vol), maintaining soil moisture at 30-40%vol in rice fields and 20-25%vol during wheat jointing stage for on-demand irrigation. Adapted machinery is mainly large variable rate fertilizers and plant protection UAV clusters, supporting thousand-mu-scale operations.
2. Fruit and Vegetable Crops (Strawberries, Grapes)
Precise control of water-fertilizer ratio and pest/disease prevention and control is required. Digitalization needs focus on "precise water-fertilizer ratio, leaf humidity warning for diseases, and directional fertilization." Sensor configurations include: EC/pH value (EC measurement range 0-10mS/cm, accuracy ±0.01mS/cm; pH measurement range 4.0-8.0, accuracy ±0.02), controlling strawberry nutrient solution EC value at 1.2-1.5mS/cm and pH value at 5.5-6.5; leaf humidity sensor (measurement range 0-100%RH, accuracy ±2%RH), warning of downy mildew and botrytis cinerea risks and starting ventilation equipment when leaf humidity continuously exceeds 85%RH; directional flow sensor (measurement range 0-5L/min, accuracy ±1%FS), applying directional fertilization by grape fertilizers according to vine distribution to improve fertilizer utilization; pest and disease spore catcher (sampling flow 10L/min, detection limit 1/m³), providing early warning for powdery mildew and downy mildew for precise pesticide application. Adapted machinery is mainly greenhouse water-fertilizer integrated machines and small directional plant protection machines, adapting to refined management of facility agriculture.
3. Root Crops (Yams, Radishes)
Root crops need to focus on ensuring root development, with nutritional needs mainly focusing on phosphorus and potassium fertilizers. Digitalization needs focus on "precise supply of phosphorus and potassium fertilizers and soil humidity to avoid root rot." Core sensor configurations include: soil fertility sensor (phosphorus and potassium specific, measurement range 0-500mg/kg, accuracy ±5%FS), requiring phosphorus and potassium content ≥150mg/kg during yam growth and dynamically adjusting fertilization amount; soil moisture sensor (multi-depth 40/60cm, accuracy ±2%vol), maintaining soil moisture at 20-25%vol to avoid root rot caused by excessive humidity; irrigation flow sensor (measurement range 0-50m³/h, accuracy ±1% FS), controlling irrigation amount to avoid flood irrigation; soil compactness sensor (measurement range 0-500kPa, accuracy ±10kPa), starting cultivators for soil loosening when compactness exceeds 250kPa to ensure root growth. Adapted machinery is mainly portable precision fertilizers and small irrigators, balancing directional operations and resource conservation.
Harvesting Stage: Sensor Adaptation Needs by Crop Category
The goal is to achieve lossless harvesting/picking, complete threshing, precise yield measurement, and maturity adaptation. The significant differences in harvesting methods and quality requirements of different crops require highlighting personalization in sensor adaptation:
1. Grain Crops (Rice, Wheat, Corn)
Grain crop harvesting needs to balance efficiency and threshing quality. Digitalization needs focus on "maturity identification, threshing intensity regulation, and loss rate monitoring." Core sensor configurations include: crop maturity sensor (maturity index 0-100%, accuracy ±3%), starting harvesting when rice maturity ≥90% and wheat ≥85%; threshing drum rotational speed sensor (measurement range 0-1500r/min, accuracy ±10r/min), controlling rice threshing speed at 800-1000r/min, wheat at 1000-1200r/min, and corn at 1200-1500r/min to avoid grain damage; grain loss sensor (measurement range 0-5kg/mu, accuracy ±0.2kg/mu), monitoring harvesting loss in real time and dynamically adjusting header height and drum speed; yield counting sensor (accuracy ±2%), measuring yield per mu in real time to realize production data traceability; humidity sensor (measurement range 0-30%, accuracy ±1%), monitoring grain moisture content and starting drying devices when it exceeds 15%. Adapted machinery is mainly large combine harvesters, supporting large-scale and high-efficiency harvesting.
2. Fruit and Vegetable Crops (Tomatoes, Citrus, Lychee)
Fruit and vegetable crop harvesting needs to ensure losslessness and quality. Digitalization needs focus on "lossless maturity detection, picking force control, and damage rate reduction." Sensor configurations include: fruit sugar content sensor (measurement range 5-25°Brix, accuracy ±0.2°Brix), picking when tomato sugar content ≥10°Brix, citrus ≥12°Brix, and lychee ≥15°Brix; color recognition sensor (accuracy ≥85%), judging maturity through fruit peel color to avoid picking unripe or overripe fruits; picking force sensor (measurement range 0-5kg, accuracy ±0.1kg), controlling the force of picking robotic arms, with tomato picking force ≤1kg, citrus ≤2kg, and lychee ≤0.5kg to avoid fruit damage; damage rate monitoring sensor (image recognition accuracy ≥90%), providing real-time feedback on picking quality and adjusting operation parameters; temperature measurement range 0-40℃, accuracy ±0.2℃, avoiding picking during high-temperature periods to ensure fruit shelf life. Adapted machinery is mainly medium-sized fruit and vegetable pickers and portable pickers, balancing efficiency and lossless needs.
3. Cash Crops (Cotton, Tea)
Cash crop harvesting needs to ensure purity and quality. Digitalization needs focus on "harvesting purity control, moisture content monitoring, and yield counting." Sensor configurations include: moisture content sensor (measurement range 0-30%, accuracy ±1%), harvesting when cotton moisture content ≤12% and tea ≤8% to avoid mildew; yield counting sensor (accuracy ±2%), measuring harvesting volume in real time to realize production data statistics; impurity recognition sensor (accuracy ≥90%), recognizing impurities such as leaves and straws during cotton harvesting for automatic sorting; tea tenderness sensor (spectral recognition accuracy ≥88%), distinguishing one bud and one leaf from one bud and two leaves to ensure tea quality; picking depth sensor (measurement range 0-10cm, accuracy ±0.5cm), controlling tea picking depth at 2-3cm to avoid damaging tea branches. Adapted machinery is mainly specialized harvesters and small lint removal/sorting machines, highlighting quality control and sorting functions.
Application Scenarios and Typical Cases
Large-scale Grain Planting: Full-process Application Scenarios of Large Agricultural Machinery Sensors
1. Application Objects
A corn planting base in Northeast China, with a planting area of 1,200 mu, adopts a large-scale and mechanized operation mode. The core needs are to improve operation efficiency, reduce labor costs, realize chemical fertilizer reduction, and increase yield.
2. Core Agricultural Machinery and Sensor Combinations
Tillage stage: Large combined rotary cultivator, equipped with soil compactness sensor (0-1000kPa, accuracy ±20kPa), tillage depth sensor (0-30cm, accuracy ±0.2cm), terrain slope sensor (0-30°, accuracy ±0.1°), linking with BeiDou automatic driving system (±2cm RTK).
Sowing stage: Large precision seeder, equipped with seed counting sensor (accuracy ±1%), plant spacing sensor (0-50cm, accuracy ±0.2cm), GPS positioning sensor (±2cm RTK), soil moisture sensor (0-100%vol, accuracy ±2%vol).
Management stage: Large variable rate fertilizer (multispectral growth sensor 400-900nm, accuracy ±3%; fertilization amount sensor 0-800kg/h, accuracy ±2%FS) + plant protection UAV cluster (equipped with pesticide flow sensor 0-10L/min, accuracy ±1%FS; obstacle avoidance radar 0-5m, accuracy ±1cm).
Harvesting stage: Combine harvester (maturity sensor accuracy ±3%; threshing drum rotational speed sensor 0-1500r/min, accuracy ±10r/min; grain loss sensor 0-5kg/mu, accuracy ±0.2kg/mu).
3. Technical Solutions
Build a full-process closed loop of "sensor-machinery-cloud platform": Before tillage, generate differentiated tillage plans through soil compactness and moisture sensor data (tillage depth of 25cm in high compactness areas and 22cm in normal areas); during sowing, GPS positioning delineates boundaries, seed counting sensors control single-grain sowing, and plant spacing sensors ensure uniform 28cm plant spacing; during the growth period, multispectral sensors monitor crop growth weekly to generate variable rate fertilization plans (adding 8kg of potassium fertilizer per mu in weak growth areas), and plant protection UAV clusters apply pesticides precisely based on pest/disease monitoring data; before harvesting, maturity sensors detect corn maturity, starting harvesting when it reaches 90%, and threshing drum speed is dynamically adjusted according to grain moisture content (speed of 1300r/min when moisture content is 12-15%).
4. Quantitative Results
Tillage depth uniformity error ≤3%, soil fineness ≥90% (particle size <5cm), laying a good foundation for sowing; sowing missed rate ≤0.5%, plant spacing uniformity error ≤2%, emergence rate increased from 85% in traditional planting to 98%; chemical fertilizer reduced by 40% (fertilization amount per mu decreased from 60kg to 36kg), pesticide usage reduced by 35%, and resource utilization efficiency significantly improved; harvesting loss rate ≤2%, 3 percentage points lower than traditional harvesters. Through precise fertilization, nutrient utilization rate is improved, and corn yield per mu increased from 650kg to 767kg (an increase of 18%); labor costs decreased by 90% (traditional planting requires 10 people per thousand mu, now only 1 person is needed to monitor equipment), daily operation area reaches 350 mu, and operation efficiency improved by 40%.
Facility Fruit and Vegetable Planting: Specialized Application Scenarios of Medium-sized Agricultural Machinery Sensors
1. Application Objects
A strawberry greenhouse planting base, with a planting area of 150 mu, adopts the facility agriculture mode. The core needs are to improve strawberry quality, reduce disease incidence, realize precise water-fertilizer control, and achieve lossless picking.
2. Core Agricultural Machinery and Sensor Combinations
Tillage stage: Greenhouse rotary cultivator equipped with depth sensor (0-15cm, accuracy ±0.3cm) and soil moisture sensor (0-100% vol, accuracy ±2%vol); Planting stage: Greenhouse transplanter equipped with seedling height sensor (0-30cm, accuracy ±0.3cm), planting depth sensor (0-15cm, accuracy ±0.2cm), and row spacing sensor (0-60cm, accuracy ±0.5cm); Management stage: Greenhouse water-fertilizer integrated machine (equipped with EC/pH value sensor, EC±0.01mS/cm, pH±0.02; irrigation flow sensor 0-50m³/h, accuracy ±1% FS) + small directional plant protection machine (equipped with leaf humidity sensor 0-100% RH, accuracy ±2%RH; pesticide flow sensor 0-5L/min, accuracy ±1% FS); Harvesting stage: Medium-sized strawberry picker equipped with fruit sugar content sensor (5-25°Brix, accuracy ±0.2°Brix), picking force sensor (0-5kg, accuracy ±0.1kg), and maturity recognition sensor (color recognition accuracy ≥85%).
3. Technical Solutions
Modular sensors adapt to the narrow space of greenhouses, and data linkage with mobile APP realizes semi-automated management: During tillage, adjust rotary cultivation intensity according to soil moisture (rotary cultivation depth of 8cm when moisture is 20-25%vol); during planting, the seedling height sensor identifies strawberry seedling height (10-12cm), automatically adjusts planting depth to 5cm, and the row spacing sensor controls 30cm row spacing; during the growth period, EC/pH sensors monitor nutrient solution concentration in real time, maintaining EC value at 1.2-1.5mS/cm and pH value at 5.5-6.5. The leaf humidity sensor monitors humidity, starting ventilation equipment and warning of botrytis cinerea risk when it exceeds 85%RH; during harvesting, the sugar content sensor detects strawberry sugar content ≥12°Brix, and after the color recognition sensor confirms maturity, the picker picks directionally with a force of 0.8kg to avoid damaging the fruit.
4. Quantitative Results
Strawberry planting survival rate ≥98%, an increase of 13 percentage points compared to traditional manual planting; water and fertilizer utilization rate increased by 55% (irrigation water per mu decreased from 80m³ to 36m³, fertilizer usage reduced by 50%); disease incidence decreased by 60% (incidence of botrytis cinerea and downy mildew decreased from 15% to 6%); picking damage rate ≤5%, 10 percentage points lower than traditional manual picking; product qualification rate increased from 85% to 98%, first-grade fruit rate (sugar content ≥12°Brix, no damage) increased from 60% to 85%, and average income per mu increased by 30%.
Smallholder Characteristic Planting: Lightweight Application Scenarios of Small Agricultural Machinery Sensors
1. Application Objects
A citrus smallholder orchard, with a planting area of 8 mu. The core needs are to reduce investment costs, simplify operations, improve citrus quality and yield, without requiring a professional technical background.
2. Core Agricultural Machinery and Sensor Combinations
Tillage stage: Portable ditcher equipped with depth sensor (0-20cm, accuracy ±0.5cm) and soil fertility sensor (NPK 0-500mg/kg, accuracy ±5% FS); Planting stage: Manual precision planter equipped with depth sensor (0-8cm, accuracy ±0.5cm); Management stage: Portable precision fertilizer (equipped with flow sensor 0-5L/min, accuracy ±5% FS; soil humidity sensor 0-100%vol, accuracy ±3%vol) + manual sprayer (equipped with simple pest and disease recognition sensor, accuracy ≥80%); Harvesting stage: Portable citrus picker equipped with maturity sensor (color recognition accuracy ≥85%) and picking depth sensor (0-10cm, accuracy ±0.5cm).
3. Technical Solutions
Low-cost adaptation to smallholder budgets and simplified operation design: During tillage, the ditching depth sensor controls the fertilization ditch depth to 15cm, and the soil fertility sensor detects soil phosphorus and potassium content, increasing potassium fertilizer application when it is below 100mg/kg; during planting, the depth sensor displays a planting depth of 5cm to ensure the root system of citrus seedlings spreads out; during the growth period, the soil humidity sensor monitors soil humidity, starting the fertilizer applicator when it is below 20%vol, applying phosphorus and potassium fertilizer mixture at a flow rate of 0.5L per plant. The pest and disease recognition sensor takes pictures of leaves through the mobile APP, automatically identifies pests such as aphids and red spiders, and pushes prevention and control suggestions; during harvesting, the maturity sensor identifies citrus color (orange-yellow), and after confirming maturity, the picking depth sensor controls the picking depth to avoid damaging branches.
4. Quantitative Results
Citrus fertilization precision error ≤8%, fertilizer waste reduced by 30% (fertilizer usage per mu decreased from 25kg to 17.5kg); picking damage rate decreased from 15% to 3%, and fruit integrity rate significantly improved; yield per mu increased from 400kg to 448kg (an increase of 12%), and first-grade fruit rate increased from 70% to 88%; total equipment investment is only 5,000 RMB, investment payback period ≤1.5 years, easy to operate, and can be used without professional technical training.
Cross-crop Adaptation: Flexible Application Scenarios of Sensors in Multi-variety Planting Bases
1. Application Objects
A multi-crop mixed planting base, with a planting area of 300 mu, covering corn (150 mu), tomatoes (100 mu), and yams (50 mu). The core need is flexible adaptation of sensors and agricultural machinery to avoid repeated investment and meet the personalized operation needs of different crops.
2. Core Solutions
Adopt a combination of modular sensors and general-purpose agricultural machinery, with the data platform supporting custom setting of crop parameters: Universal sensors: BeiDou positioning sensor (±2cm RTK), soil moisture sensor (0-100% vol, accuracy ±2%vol), flow sensor (0-50L/min, accuracy ±1%FS), which can be switched to adapt to different machinery; Specialized sensors: Corn-specific plant spacing sensor, tomato-specific EC/pH sensor, and yam-specific directional depth sensor, connected to general-purpose machinery through modular interfaces; Data platform: Supports custom operation parameters for corn, tomatoes, and yams (such as corn sowing depth 3-5cm, tomato planting depth 5-6cm, yam ditching depth 15-20cm). Sensor data automatically matches corresponding crop parameters to generate operation plans.
3. Result Highlights
Sensor reuse rate increased by 60%. Universal sensors can adapt to the tillage and management stages of 3 crops, avoiding repeated purchases and reducing equipment investment costs by 40%; cross-crop operation switching efficiency increased by 50%. Without replacing agricultural machinery, only replacing specialized modules and adjusting platform parameters can achieve rapid switching from corn sowing to tomato transplanting; operation accuracy of different crops meets standards: corn sowing uniformity error ≤2%, tomato water and fertilizer utilization rate increased by 50%, yam ditching depth error ≤1cm, all reaching the operation level of single-crop specialized equipment; adapts to multi-variety personalized needs, providing efficient and low-cost intelligent perception solutions for mixed planting bases.
Future Technological Breakthroughs and Industrial Prospects of Agricultural Machinery Sensors
Core Technological Breakthrough Directions
1. R&D of Crop-specific Sensors
Aiming at the biological characteristics of characteristic crops (Chinese medicinal materials, tropical fruits, tea, etc.) and niche crops, develop customized perception modules to fill the category gap. For example, develop seed germination rate monitoring sensors specifically for Chinese medicinal materials, dual-parameter sensors for maturity and hardness specifically for tropical fruits (such as mangoes), and integrated sensors for tenderness and moisture content specifically for tea, to meet the planting needs of characteristic agriculture. At the same time, aiming at the demand differences in different growth stages of crops, develop dynamically adapted sensors, such as temperature and humidity specific sensors for the rice seedling raising stage and grain plumpness monitoring sensors for the filling stage.
2. Multi-sensor Fusion and Intelligent Upgrading
Hardware integration: Develop "environment + operation + safety" multi-parameter integrated sensors, such as "soil moisture + fertility + pH value + temperature" four-in-one soil sensors and "crop growth + maturity + pest and disease" three-in-one optical sensors. The volume is reduced by 50% and the cost is reduced by 30%, reducing the number of agricultural machinery sensor deployments and improving data fusion efficiency; Algorithm empowerment: Sensors are built-in with AI edge computing chips to realize local response of crop recognition and operation decisions (delay ≤1s). For example, pest and disease sensors built-in with deep learning algorithms can directly output pest types, quantities, and prevention and control suggestions without cloud computing; automatic driving sensors built-in with path planning and obstacle recognition algorithms to improve operation stability in complex terrains; Cross-scenario adaptation: Develop adaptive sensors that can automatically adjust accuracy and collection frequency according to crop types and agricultural machinery types. For example, when the sensor detects that the operation scenario switches from corn fields to tomato greenhouses, it automatically adjusts the tillage depth measurement accuracy from ±0.2cm to ±0.3cm, and the collection frequency from 1 time/s to 1 time/5s to adapt to different scenario needs.
3. Improvement of Core Parameter Accuracy
Break through key application technologies such as hyperspectral, micro force sensors, and MEMS chips, with accuracy benchmarking international advanced levels. For example, the spectral resolution of hyperspectral sensors is improved from 10nm to 5nm, close to similar international products (4nm); the accuracy of micro force sensors is improved from ±0.1kg to ±0.01kg to meet the needs of lossless picking of fruits and vegetables; the measurement accuracy of MEMS chips is improved from ±0.1cm to ±0.05cm to ensure precise control of operation parameters. At the same time, improve the environmental adaptability of sensors, develop sensors resistant to high temperature (≥85℃), high humidity (≥95% RH), and corrosion, adapting to complex agricultural environments such as rainy south, dusty north, and saline-alkali land.
4. Data Collaboration Technology
Establish unified sensor data interface standards (such as formulating agricultural machinery sensor communication protocols and data format standards) to realize data interconnection and sharing among different brands of agricultural machinery and different types of sensors, solving the "data island" problem. Develop sensor data encryption and secure transmission technologies, adopt blockchain technology to realize operation data traceability, and ensure data authenticity and security. Build a "sensor-machinery-cloud-farmer" data collaboration platform to support real-time synchronization of sensor data, remote monitoring, and intelligent decision-making, realizing full-link data closed loops.
Industrial Development Prospects
1. Market Pattern
Domestic substitution is accelerating. It is expected that the localization rate of high-end agricultural machinery sensors will exceed 60% by 2027, and core components (such as MEMS chips and hyperspectral cameras) will achieve autonomous and controllable production. The market size of low-cost sensors for small agricultural machinery will grow at an annual rate of over 20%, and lightweight sensors adapting to the needs of smallholders will become a growth hotspot. At the same time, the integration trend of sensors and agricultural machinery is obvious. The market share of "agricultural machinery + sensor" packaged solutions will increase from the current 40% to 70%, forming an integrated solution of "perception-execution."
2. Promotion Paths
Policy level: Increase subsidies for core technology R&D, support enterprises in building pilot platforms, and reduce R&D risks; establish an adaptation standard system of "species-machinery-sensor" to regulate market order; give 10-20% procurement subsidies to large-scale planting bases purchasing domestic sensors, and give more than 50% subsidies to smallholders purchasing small agricultural machinery sensors to promote technology popularization.
Enterprise level: Launch "sensor + machinery + platform" packaged solutions, providing personalized services for different planting scales (providing full-process automation solutions for large-scale bases and low-cost lightweight solutions for smallholders); launch a "shared sensor + pay-per-use" mode for smallholders to lower the initial investment threshold; strengthen industry-university-research cooperation, jointly develop crop-specific sensors with agricultural research institutes, and improve scenario adaptability.
Technical level: Simplify operation interfaces, develop "foolproof" adaptation solutions (such as one-click switching of crop parameters and voice control), and lower the threshold for smallholders; optimize sensor power supply methods, promote self-power supply technologies such as solar energy and vibration energy, and solve the power supply problem in remote areas; develop sensor self-diagnosis and self-calibration functions to reduce maintenance costs.
3. Extension of Application Scenarios
Application scenarios will expand from traditional field/facility agriculture to special scenarios such as smart pastures (such as forage harvesting sensors and feed ratio sensors), facility fisheries (such as water quality monitoring sensors and feeding amount sensors), Chinese medicinal material planting (such as root growth sensors and active ingredient monitoring sensors), and urban agriculture (such as small sensors for balcony planting), forming full-agricultural-field coverage. At the same time, the integration of sensors with new agricultural machinery such as UAVs, unmanned vehicles, and robots will be more in-depth. For example, agricultural robots equipped with multiple sensors will realize full-process operations of autonomous sowing, fertilization, and picking.
4. Ultimate Goal
Build a full-link intelligent system of "crop-machinery-sensor-cloud" to realize autonomous decision-making and autonomous operations of agricultural machinery without manual intervention. For example, smart agricultural machinery can automatically formulate full-process plans for tillage, sowing, management, and harvesting based on soil data, crop data, and meteorological data collected by sensors, dynamically adjust operation parameters, and realize "unmanned" agricultural production. Ultimately, through continuous breakthroughs and industrial popularization of agricultural machinery sensor technology, it will support the high-quality development of agricultural modernization, ensure national food security, and promote the implementation of the rural revitalization strategy.
Conclusion
Agricultural machinery intelligent perception technology is the "last mile" for the implementation of smart agriculture. Its core value lies in breaking the "experience-driven" operation mode of traditional agricultural machinery. It is the key technology to realize the digital transformation of "full-scenario adaptation, full-link precision, and full-crop coverage." At present, domestic agricultural machinery sensors have the development foundation of "strong scenario adaptability, significant cost advantages, and accelerated localization." They have been widely used in scenarios such as large-scale grain planting, facility fruit and vegetable planting, and smallholder characteristic planting, achieving significant economic and social benefits.
Although still facing challenges such as high-end technology gaps, insufficient category adaptation, and non-unified standards, with the deep integration of sensors and technologies such as AI, IoT (Internet of Things), BeiDou positioning, and blockchain, future agricultural machinery sensors will evolve in the direction of "crop specialization, multi-sensor fusion, intelligent autonomy, and data collaboration." We firmly believe that driven by technological innovation, industrial collaboration, and policy support, agricultural machinery sensors will further break through performance limits and application boundaries, completely change traditional agricultural operation modes, and provide core support for ensuring food security, promoting rural revitalization, and realizing agricultural digital transformation, writing a new chapter in the high-quality development of agricultural modernization.
(The next issue of this column will feature "The Third Article in the Smart Agriculture Field - Forestry and Grassland Monitoring Sensors." Stay tuned!)
Author | Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Jiusan Society Central Committee, Executive Vice Chairman of the China Sensor and IoT Industry Alliance
Editor | Yang Pengyue Art Editor | Ma Liya Supervisor | Zhao Chen