The agriculture sector is rapidly adopting new technologies to improve their farming quality and efficiency. Farmers and Agriculturalists are turning to Artificial Intelligence to help yield healthier crops, control pests, and monitor their fields and growing conditions.
Crop Disease Severity
It is critical to determine the type and extent of diseases in crops in a timely manner to prevent its spread. Traditional methods for agricultural protection rely on human attention to detect diseases and crop damage, which is unreliable. Computer Vision can help detect the type and severity of crop diseases quicker and more reliably. Farmers can then take swift action in curing the disease or quarantining/removing the part of the crop that was affected, saving them from considerable yield losses.
Weed Detection and Control
Volume spraying of herbicides for weed control fuels the formation of resistant varieties. Farmers also raise concerns about the increased exposure of their produce to these chemicals. Using Computer Vision to identify areas infested with weeds from crop imagery, AI can identify the precise regions where herbicides have to be sprayed. This reduces the chemical usage and concentrates it only to the required area, granting more nutrients for the crops and produces better yield, resulting in profitability.
Pest Detection and Control
Farmers lose billions yearly in agricultural losses due to pest infestations. Farm owners are now implementing Machine Learning for the detection and identification of these pests. Hi-resolution imagery of crops are taken as input for the AI model, which can accurately detect if there is a pest infestation, the infestation extent, the type of pest, and pest classification using Object Detection. Farmers can then use the right agrochemical pesticides to treat the infestation with reduced environmental impact.
Produce Quality Inspection
Manual quality inspection processes do not always maintain consistency in agricultural product quality. This makes it difficult for farmers to ensure that a satisfactory produce is available in the market. With the development of Computer Vision technology, automatic grading and quality inspection can be implemented. AI equipped scanners on QC lines can inspect produce for rot, disease, pests, mechanical damage and ensure the best quality product moves for commercial and consumer usage.
In large farming fields, regular surveillance of crops and plots is a resource intensive task. Farmers can incur various losses due to improper field monitoring. Produce can be affected due to fires, foreign body and vehicle intrusion, animal invasion among other reasons. High-definition images from airborne drones or sensor platforms, can create field maps across large acres of space. AI systems can automatically identify areas requiring immediate attention. Farmers can reduce costs in resource allocation and profit from increased crop yields.
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