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AI in Agriculture
Jure Brence, January 2022
With the human population growing rapidly, the global needs for food production are increasing. Historically, the growth of food consumption has been in part matched by improved production efficiency due to technological progress. Today, great potential for increasing efficiency comes from advances in robotics, the Internet-of-Things (IoT) and of course, artificial intelligence.
But improving production output is not the only goal in agriculture where AI can help. We are increasingly aware of the devastating effects that expanding farmland and diminishing habitats have on ecosystems. Dramatically increasing food production efficiency and introducing new concepts, such as vertical farming, can help us limit the land surface humanity needs to sustain ourselves.
Furthermore, understanding the impacts of our activity on the soil and the ecosystems often requires a thorough analysis of highly complex systems. The proliferation of data collection in agriculture, such as from sensors and satellites, enables data-driven approaches towards a future of sustainable farming.
In today’s article, we take a look at some of the more promising or widespread applications of AI in agriculture.
Monitoring and managing crops is a vital task in farming, traditionally performed through human observation. Nowadays we can employ camera-equipped drones to survey crop fields. Analyzing the aerial data requires computer vision models that help track crop health, detect crop malnutrition, as well as provide estimates of crop yield. On the ground level, visual data from cameras is used to automatically and remotely determine the maturity of fruit and vegetables.
As important as monitoring is for crops, farm animals require even more attention and management. Computer vision models can use live camera feeds to identify individual animals and track their movements and behavior, allowing farmers to keep count and automatically detect trouble as it happens. Diseases and behavioral problems can be detected either directly, or by analyzing the historical data for a particular animal. Moving beyond vision, sensor data, as well as the measured properties of animal produce, such as milk, can be continuously analyzed to detect health or nutrition issues.
Managing soil through crop rotation, fertilization and pesticides is crucial to ensuring good crop production and quality. This is a complex task for humans, but well suited to AI-algorithms trained on large collections of historical data. However, productivity is not the only outcome AI can help optimize. Soil performs a number of crucial functions: water purification and regulation, carbon sequestration, biodiversity provision, as well as nutrient cycling. To ensure green and sustainable farming practices, farmers must pay attention to preserve and optimize all soil functions. Tools like Soil Navigator help farmers navigate the complicated landscape of soil management and soil function optimization through a combination of expert knowledge and AI models.
The battle against various crop-harming weeds, insects, mites, worms and snails is as old as agriculture itself, with farmers employing mechanical measures, i.e. tilling, chemical means, i.e. pesticides, as well as biological approaches, such as introducing natural predators. In modern days, there is a growing trend of limiting pesticide use as much as possible and seeking organic produce. Through the use of drone vision, combined with sensor data, AI applications can detect the most infected regions of a planting area and recommend the best measures to combat the infections, such as finding optimal pesticide mixtures. By ensuring the efficiency of applied measures and limiting their use to critical locations, both the ecological impact and the costs of pest control can be minimized.
Security and surveillance
Deer, wild hogs and other wild animals are a common nuisance in farming and can cause significant damage to crops, especially in cases of large and remote farms. AI can be used to combat this issue by training computer vision models to recognize wild animal breaches in surveillance camera footage and immediately alerting farmers. The same solutions can also protect against burglars and other human intruders.
Labor shortages affect many sectors, agriculture notwithstanding. It is no surprise that those advancing the cutting edge of farming technology are increasingly introducing robots to the fields. Machines like BoniRob rely on video recognition to tell weeds from crops as they roll through the fields, piloted by movement AI, and stamp out weeds. Some designs combine weeding with watering and analyze soil moisture content to deliver the optimal water load. Fertilizer distribution, seeding, pesticide spraying, the list of applications for intelligent robots goes on and on.
Price and yield forecasting
Modern farmers don’t just tend to fields, they are also businessmen. How much yield of each crop can we expect this season, based on the weather, soil properties and other data? What will the market demands be like? Understanding dynamics like these is crucial for strong price negotiations and successful farm management. Much like in other sectors, AI models can be of great help by identifying patterns in large collections of data and offering reliable predictions of future trends.
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