Abstract

Worldwide productivity of crops is seriously threatened by crop leaf diseases, which can result in large crop losses and negative economic effects. Effective disease management and crop protection depend on the early and precise detection and classification of these illnesses. Machine learning approaches have gained popularity recently due to their ability to automate procedures related to illness diagnosis and classification. An overview of the several machine learning–based methods used for crop leaf disease diagnosis and classification is provided in this review paper. We go over the basic ideas and methods of the machine learning algorithms that are applied here, as well as their advantages and disadvantages. Additionally, we examine and contrast the results of several machine learning approaches published in the literature, emphasizing the critical elements affecting their efficacy. Ultimately, we pinpoint the present obstacles and forthcoming research avenues to promote progress in this domain.

Keywords: Machine learning, convolutional neural network (CNN), transfer learning, support vector machine (SVM), random forest, deep learning

INTRODUCTION

New peaks and achievements have been established in the healthcare, transportation, business analytics, and agricultural fields as a result of the current surge in advancements in computer vision, neural networks, and machine learning [1]. India’s economy relies heavily on agriculture. To stay alive in this ever-changing climate and surroundings, automation and improvements to traditional techniques of disease detection are essential. To address the crop’s supply and demand problems, the agricultural sector requires radical improvements. Maximum agricultural output may be achieved by regular crop monitoring and rapid disease diagnosis if a crop is contaminated. In India, cotton farmers frequently face difficulties due to leaf diseases.

Keyworde: In vitro and in vivo methodologies, protein-protein interaction, databases on protein- protein interaction, experimental and computational methods, therapeutic targets

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