COMPARATIVE ANALYSIS OF PLANT DISEASE DETECTION ALGORITHMS IN MACHINE LEARNING USING PYTHON

Abstract

The field of Artificial Intelligence is penetrated now in every place. This impact is also penetrated in Agriculture. In Plant diseases pose significant threats to global food security by reducing crop yields and quality. Traditional methods of disease detection and diagnosis are often labor-intensive, time-consuming, and require expertise. In recent years, advancements in machine learning techniques have provided promising solutions for automated plant disease detection, enabling early and accurate identification of diseases. This paper presents a comparative analysis of various machine learning algorithms for the detection of plant diseases. We review and compare the performance of several algorithms, including deep learning approaches such as Convolution Neural Networks (CNNs), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and K-Nearest Neighbors (KNN). The evaluation is based on criteria such as accuracy, speed, robustness, interpretability and scalability. We explore the challenges and limitations associated with existing algorithms, such as overfitting, limited dataset availability, and generalization issues across different plant species and diseases. Through this comparative analysis, we aim to provide insights into the strengths and weaknesses of different machine learning approaches for plant disease detection. This research contributes to the advancement of automated systems for early disease detection in crops, ultimately aiding in crop management practices, minimizing yield losses, and ensuring global food security.