Detecting Plant Diseases With AI Deep Learning Techniques

Worldwide, plant diseases pose a severe threat to agricultural productivity, leading to monetary losses and food shortages. Improved crop health and successful response depend on timely and precise disease identification. This study presents the creation of a mobile application intended to identify plant diseases in the three main crops—corn, tomatoes, and potatoes. The program classifies plant leaves as either healthy or unhealthy by using a a convolutional neural network (CNN) trained on a huge photo collection. For easy deployment on Android devices, the model was transformed into TensorFlow Lite format after being developed using the user-friendly Teachable Machine platform. Users may take pictures directly or upload them from the gallery using the Android Studio-based software, and the CNN model that has already been trained will classify them in real time. The app recognizes the illness, lists certain symptoms, and suggests fertilizers and treatment techniques for leaves that are afflicted. The efficacy of CNN-based methods for plant disease diagnosis in mobile settings is demonstrated by this work. Crop health and agricultural output can be enhanced by the application, which gives farmers and agricultural experts an easy-to-use tool for early disease diagnosis.