Abstract:
The diagnosis of diseases in mango leaves presents a complex challenge,
compounded by the diversity of crop types, variability in agricultural disease
indicators, and a multitude of environmental factors. Early detection of these diseases
is particularly difficult, as existing methods often rely on data limited to specific
geographic areas, thus restricting their efficacy. The timely identification and
management of such diseases are crucial in averting substantial financial losses for
farmers. This research introduces an innovative approach, leveraging image
processing technology to detect diseases in mango leaves. The study employs a
Convolutional Neural Network (CNN) model, specifically the Proposed model, which
has shown remarkable accuracy of 97.92% in this context. This model was rigorously
tested on both normal and diseased mango leaves, showing its efficacy in
differentiating between healthy and unhealthy foliage. The application of this
algorithm to leaf images facilitates the categorization of mango tree leaves into
healthy or diseased categories, offering a timely and efficient solution for early
disease detection. This novel approach not only promises to enhance disease
management in mango cultivation but also sets a precedent for applying similar
techniques in broader agricultural contexts, potentially revolutionizing plant disease
diagnosis and prevention.
