Abstract:
Mango leaf diseases suffer from a multiple challenge in diagnosis due to different types of
crops, changing agronomic disease indices and several environmental factors that influence
them. It is still hard to detect them early since extant methods depend on data restricted by
geography, thereby making them inefficient. This is essential for timely detection and
control of such diseases in order to prevent huge financial losses that result among farmers.
This research introduces an innovative strategy using deep learning and image processing
technologies towards that end. Using CNN (Convolutional Neural Network) model, the
study achieved a remarkably high 98.75% accuracy in separating healthy mango leaves
from those with diseases. Rigorous tests were carried out over a range of leaf conditions to
verify the effectiveness of the model. The technology has helped apply the algorithm onto
3000 leaf pictures which assist in identifying whether mango leaves are healthy or diseased
when illness starts thus this is early disease detection. This innovation does not simply
provide a timely and effective solution to enhance the management of diseases in mango
farming, but also sets a benchmark for comparable advances in the broader agricultural
milieu. The importance of this new technique in revolutionizing plant disease,
identification and prevention highlights its significance in agricultural development. By
overcoming geographical barriers and embracing state-of-the-art technology, this study
begins a fresh chapter in agricultural practices; It is hoped that it will help farmers enable
farmers to manage their diseases better and produce more sustainable crops
