Plant Disease Detection using Deep Learning
A technique to classify plant disease by images of the plants by using transfer learning models, which will be used to put out a comparative analysis to assert the viability of using the defined methods in order to visually categorize the plant diseases.
Abstract
Plants play an essential role in the ecosystem of our planet, and their significance is immeasurable. Detection of plant disease at an early stage is very important. It aids in minimizing crop losses, which can have severe consequences on the livelihoods of farmers as well as global food security. By identifying plant diseases early, the spread of the disease can be stopped, reducing crop losses. Detecting plant diseases accurately enables farmers and researchers to select the most effective treatment or management strategy, saving time, money, and resources by preventing the unnecessary use of pesticides or other interventions that may not work. The potential use of Deep Learning techniques, particularly transfer learning, for the detection of plant diseases could provide valuable support to farmers who may not have the resources or expertise to hire specialized experts. We are proposing a technique to classify plant disease by images of the plants by using transfer learning models. The paradigm is to use numerous models which then will be used to put out a comparative analysis where we put out different results to assert the viability of using the defined methods in order to visually categorize the plant diseases.