A Comparative Study on Deep Learning Models for Plant Disease Detection and Organic Solutions

Abstract

Abstract: Farming is essential to the economy of each and every country. Consistently, ranchers grow many harvests. Crop and forest products for both food and non-food products are included in agriculture. [ First, agriculture was an important part of the development of sedentary human civilization. One of the primary causes of crop devastation and inappropriate crop development is infection and disease. Several factors associated with plant disease diagnosis using deep learning techniques must be taken into consideration when developing a robust system for accurate disease management. This model utilizes profound learning and picture handling to distinguish plant sicknesses and prescribe natural answers for treating the plants. This model makes use of convolutional neural networks, which are crucial for visual imagery. Images are processed and libraries are extracted with Tensorflow. Keras is likewise utilized for highlighting extraction expectations and adjusting hyperparameters. In light of the kind of plant and sickness, this model additionally suggests natural arrangements like fertilizer, vermicompost, bonemeal, etc. Moreover, the model distinguishes bothers and proposes natural cures, for example, rejuvenating balms, cow manure, and neem glue to safeguard the harvest from harm. The project's objective is to reduce the economic and aesthetic damage caused by plant diseases and to provide farmers with an intuitive interface for organic plant cultivation. In order to provide the most precise Deep Learning results, this model looks for the ideal approach. Organic Solutions and a Model for Predicting Plant Diseases