Crop Disease Identification and Treatment Recommendations Using ResNet50(CNN)

Crop disease distinguishing and treatment suggestion utilizing CNN revolutionizes rural hones by joining progressed innovation with conventional cultivating strategies this think about saddles the capabilities of profound learning to foresee trim infections from pictures and suggest fitting fertilizers in this way engaging agriculturists with exact experiences for successful trim administration utilizing convolutional neural systems (CNNs)for illness forecast and repetitive neural systems (RNNs) for treatment suggestion the ponder analyzes trim pictures and chronicled information to supply custom-made arrangements exchange learning procedures assist adjust pre-trained models to particular crops and natural conditions guaranteeing strong execution test discoveries illustrate the precision and unwavering quality of the proposed approach in foreseeing edit maladies and suggesting treatment the framework accomplishes tall exactness and review rates viably optimizing supplement administration and improving trim yields edit illness expectation and treatment proposal utilizing profound learning develops as a transformative arrangement for economical agribusiness by coordination profound learning advances into cultivating hones this approach prepares agriculturists with noteworthy bits of knowledge cultivating moved forward edit wellbeing asset proficiency and financial flexibility in rural communities.