Paper Contents
Abstract
The Arecanut plant is a vital crop in many regions, and its health is crucial for agricultural productivity and local economies. However, Arecanut plants are susceptible to various diseases that can significantly reduce yield and quality. This project aims to develop an intelligent, user-friendly system for detecting diseases in Arecanut plants using advanced machine learning techniques. Leveraging Convolutional Neural Networks (CNNs) and image processing, the system will analyze images of Arecanut leaves to identify disease symptoms based on visual patterns.The project includes a mobile-friendly user interface that allows farmers to upload images of their plantcrop, receive instant diagnoses, and obtain recommendations for disease management. By integrating environmental data, such as humidity and temperature, the model's accuracy is further enhanced. This solution not only supports farmers in early disease detection but also helps reduce pesticide usage by enabling targeted treatments, promoting sustainable farming practices. In essence, this project combines artificial intelligence with agricultural expertise to offer a scalable solution for Arecanut disease management, improving crop health and productivity in affected regions. Keywords: Convolutional Neural Networks , Arecanut Plant disease detection, user friendly.
Copyright
Copyright © 2024 Arpitha.S. This is an open access article distributed under the Creative Commons Attribution License.
