Smart Agriculture: CNN-based Systems for Early Detection and Diagnosis of Plant Diseases and Pests

Pests impact agricultural production, prompting the need for efficient disease detection in plants. Traditional methods, reliant on manual observation, are time- consuming and imprecise. It uses the Convolutional Neural Network (CNN) for prediction of diseases. The CNNs enhance precision, offering farmers a more effective and streamlined method for disease detection, potentially transforming agricultural practicesUsing transfer learning and pre-trained models, the system successfully extracts information from photographs submitted via a user-friendly website. The CNN based algorithm analyzes these photos using powerful image pre-processing techniques, giving farmers and individuals precise insights into recognized diseases or pest infestations. Plant photos obtained with cellphones are processed using CNN, a specialized image recognition system. A CNN is an effective method in the AI, which scans the image and produces the maximum correct output. It is primarily used in image recognition tasks, where it processes input in the form of pixels. The website design not only allows for easy image submission but also provides results and probable therapy recommendations. This comprehensive solution, which combines cutting-edge CNN technology, transfer learning, and an easy-to-use web interface, enables users to proactively protect their crops, resulting in a more resilient and sustainable agricultural ecosystem.