Plant Disease Recognition Using Machine Learning and Deep Learning Classifiers

Abstract

Plant diseases are a major threat to agricultural production globally, resulting in decreased crop yields and financial difficulties. For these illnesses to be effectively managed, early and precise disease identification is essential. Through the use of both deep learning and conventional machine learning techniques, this work proposes a thorough method for classifying plant leaf diseases. The research makes use of a library of tagged plant leaf photos that includes both healthy and diseased leaves. The leaf photos are first processed using AlexNet, a deep convolutional neural network (CNN), to extract complex characteristics. The dataset is utilized to train the CNN model, and its high-level feature representations are applied to categorize diseases. For comparison analysis, classic machine learning techniques like Naive Bayes (NB) and K- Nearest Neighbors (KNN) are also used. To test these algorithms’ ability to identify between various plant diseases, they are applied to the derived characteristics. In the context of classifying plant diseases, the comparative study attempts to assess the benefits and drawbacks of both deep learning and traditional machine learning methodologies. The findings of this study offer insightful information about the effectiveness of several plant disease diagnosis methods. A multifaceted strategy to reliably diagnose plant diseases is provided by the integration of deep learning and machine learning techniques, assisting farmers and agricultural specialists in making timely disease management decisions. This study contributes to continuing attempts to lessen how plant diseases affect the sustainability of agriculture and the safety of the world’s food supply.