Supporting technologies
Digital agriculture leverages advanced irrigation systems for precise water delivery, optimizing resource use and improving crop yields. AI-powered internet of things (IoT) platforms monitor crops for pests and diseases, enabling targeted interventions and reducing reliance on broad-spectrum pesticides. Real-time data analysis from sensors and drones inform crucial decisions regarding watering needs and pest control. While still at the experimental stage, technologies exploring weather modification, such as cloud seeding, aim to supplement natural rainfall and enhance agricultural productivity in water-stressed regions.
- Watering
In agriculture the use of advanced technologies and data-driven approaches can optimise irrigation practices, ensuring efficient water use and enhancing crop growth. This involves the integration of sensors, weather forecasts and automated irrigation systems to monitor soil moisture levels, weather conditions and plant water needs in real-time and providing precision watering devices such as GPS-based systems or watering robots. By delivering precise amounts of water only when and where it is needed, smart watering reduces water waste, lowers costs and supports sustainable farming practices, while improving crop yields and resilience to climate variability. Using precision irrigation, water savings of up to 44% have been reported.
Sensing and imaging
Modern irrigation relies on soil moisture and plant-water-status sensors to deliver water precisely where and when it’s needed. Capacitance probes buried at root depth measure volumetric water content, while canopy thermal imaging detects plant stress from inadequate hydration. Together, these tools create real-time moisture maps that prevent overwatering and water waste. Thermal cameras on field towers, for instance, can spot early wilting in hot spots and trigger local drip lines to restore optimal plant hydration.
Data processing and AI
Data platforms integrate sensor feeds, weather forecasts and crop models to run AI-driven irrigation schedules. Machine-learning algorithms learn each field’s water-use patterns and predict when moisture will fall below crop thresholds. These models then generate variable-rate irrigation (VRI) prescriptions, ensuring each zone receives exactly the volume it needs. Such systems may reduce water delivery by 20% in heavy-clay patches, for example, while boosting supply in sandy areas prone to rapid drainage.
Automation and robotics
Automated irrigation rigs - such as centre pivots and robotic drip units - use GPS guidance and flow-control valves to enact VRI plans without human intervention. Onboard flow meters and pressure sensors can adjust water pressure and nozzle activation in real time. Operators can monitor performance remotely and override settings if unexpected weather events occur. The result is efficient, hands-free watering that maximizes yield per drop.
- Influencing weather conditions
Influencing weather conditions in agriculture, often referred to as weather modification, involves techniques aimed at altering atmospheric conditions to benefit agricultural activities, such as cloud seeding, fog harvesting and localized heating or cooling. The impact of these practices is significant as they can help mitigate the adverse effects of climate variability and extreme weather events. The use of AI algorithms to optimise environmental parameters and drones for artificial rainmaking and cloud seeding has greatly increased technical innovation in this area.
Data processing and AI
Data aggregation from satellite imagery, ground‐based weather stations and real‐time sensor networks can feed AI models that predict optimal windows for weather interventions. Machine‐learning algorithms analyse historical seeding outcomes alongside current atmospheric profiles to identify the precise altitude and timing for dispersants. AI can then generate dynamic operation plans, coordinating multiple seeding units to work together and adapt to changing conditions mid‐flight. Over time, continuous learning refines these models, improving success rates and ensuring that each weather‐modification effort delivers maximum benefit with minimal resource use.
Drones
Drones can play a key role in modern weather‐influence operations by delivering seeding agents, like silver iodide or hygroscopic salts, directly into target cloud layers. Equipped with compact dispersal canisters, they fly predetermined flight paths based on real‐time atmospheric readings, ensuring precise release altitudes and coordinates. Their agility allows them to respond rapidly to evolving weather windows, deploying small, highly controlled payloads from places that manned aircraft or ground stations cannot reach.
- Pest control
Pest control in agriculture refers to the process of reducing crop losses due to pests and diseases, increasing food production, minimising chemical usage, ensuring safer food and preserving ecosystems. Effective pest control strategies include the use of chemical pesticides, biological control agents like beneficial insects, cultural practices such as crop rotation and integrated pest management (IPM) approaches that combine multiple methods for sustainable results. Digital advances in pest control cover GPS-guided sprayers that minimize pesticide use while maximising effectiveness, autonomous drones and land robots for targeted pesticide application, as well as image recognition algorithms to detect and classify pests using drone or sensor-captured data. It has been estimated that variable-rate application can achieve average pesticide (cost) reductions of 60-67% compared to conventional application methods.
Internet of things (IoT) real time pest monitoring
IoT devices (traps, sensors) transmit pest activity data to cloud systems for real-time analysis. When the system identifies irregular patterns, such as clustered defoliation or heat signatures of hive-invading insects, it alerts operators to the specific zone. As a result, interventions can be confined to small patches, reducing chemical usage and collateral damage.
Data processing and AI
AI-driven pest models can integrate trap counts, the migratory data of harmful insects, climate data and crop-stage information to forecast outbreak risks and optimise treatment timing. Machine learning calibrates these predictions by comparing actual infestation events with historical trends, continuously improving accuracy. The platform then suggests targeted pesticide or biocontrol releases in narrow windows when pests are most vulnerable. This data-guided approach enhances efficacy and minimizes pesticide resistance development, as well as pesticide runoff.
Automation and robotics
Robotic sprayers and ground vehicles equipped with precision nozzles apply biopesticides or pheromone disruptors directly to infested plants or patches of land. Guided by GPS and onboard pest-detection sensors, robots only target the affected foliage, avoiding blanket treatments. Some systems even mount mechanical applicators, such as sticky traps or vacuum collectors, that remove pests without chemicals. These autonomous units patrol fields on routine schedules, ensuring continuous pest suppression with minimal human oversight.
Drones
Drones can rapidly survey fields with high-resolution red, green and blue (RGB) imagery and multispectral cameras to detect pest outbreaks by identifying leaf damage patterns and unusual canopy reflectance. Once hotspots are mapped, drones equipped with precision micro-spray nozzles can target those specific areas with minimal drift, applying fungicides, insecticides or biological pest control agents such as nematodes only where needed rather than blanket-spraying entire fields. This approach not only reduces chemical use and environmental exposure but also slows the development of resistance by concentrating applications on actively infested zones. In vineyards and orchards, unmanned aerial vehicles (UAVs) can fly under the canopy to deliver precise spot treatments along vine rows, saving both time and input costs compared to traditional ground rigs.