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Abstract: The health and production of crops have a major impact on societal well-being, and agriculture is essential to maintaining global food security. Crop output and quality are greatly impacted by nutrient shortages and crop diseases. We offer a unique approach to this problem that makes use of the Raspberry Pi, an inexpensive, small, and energy-efficient single-board computer, to forecast crop illnesses and identify nutrient deficits in real time. Our solution provides farmers and other agricultural stakeholders with fast and accurate information for efficient crop management by combining computer vision, machine learning, and sensor technologies. The Raspberry Pi has many sensors to assess ambient and soil conditions in addition to a high-resolution camera for taking pictures. Using a machine learning model trained on a variety of datasets pertaining to agricultural illnesses and nutritional deficits, the system takes pictures of crop leaves and soil conditions. Preprocessing images, feature extraction, and convolutional neural network (CNN)-based categorization are important parts of our approach. The system can recognize evidence of nutrient deficits, such as nitrogen, phosphorus, and other deficiencies, as well as symptoms of a variety of crop illnesses, including fungal infections, bacterial blights, and viral diseases.
Prof. Sonali Badade (Mon,) studied this question.