Plant Disease Prediction Using Convolutional Neural Network

Plant Disease Prediction Using Convolutional Neural Network

Yayati Vaidya1, Prerana Mandale2, Snehal Wayde3, Shraddha Otari4

1,2,3,4Students, Department of Computer Engineering,

SSBT’s College of Engineering and Technology, Bambhori, Jalgaon, India, 425001

Abstract – Plant diseases are a serious threat to agricultural production and food security around the world. Early and precise diagnosis of plant diseases is key to the success of preventing and controlling crop loss. In this research, we present a deep learning model using CNN to automatically identify different plant diseases from the leaf images. CNN’s work well in learning hierarchical spatial features, and represent a great fit for image-based classification. The model is trained and validated using a large dataset of labeled images of healthy and diseased plant leaves. The proposed system preprocesses the input images, extracts appropriate features and categories the features into its corresponding disease properties. The model is not only accurate and robust but also superior to the conventional machine learning methods. This platform also provides a scalable and cost-effective option for real-time disease diagnosis, allowing farmers and other agricultural professionals to act promptly. The use of these AI based tools in agriculture can boost the crop yield, reduce reliance on the interference of experts, and achieve sustainability in farming.

Keywords - Deep Learning, Plant Disease Prediction, CNN, Image-based diagnosis