A Comprehensive Review on Crop Disease Classification using Machine Learning Technique

Abstract

Abstract: With migrating populations towards cities and global urbanization, there arises a need to automate certain sections of agriculture. Precision agriculture has emerged as an active areas of research in which computational and data processing techniques are being applied in the domain of agriculture. One of the most common and menacing problems which farmers face is crop diseases which result in malnourishing the actual crop and in some cases completely destroying the crop. Hence, machine learning and deep learning based techniques are being extensively studied and explored to design systems which can identify diseases among plants, automatically. Crop disease identification is a relatively challenging task keeping in mind the similarity among the textures of plants and the variations among plants of the same category, blurring and noise effects appearing in images typically captured by unmanned aerial vehicles (UAVs). This paper presents a comprehensive review on image pre-processing and machine learning based techniques for automated classification of crops diseases. The fundamental mechanism of noise removal along with noise sources in images have been cited and explained. The basics of machine learning based classifiers applied to image classification have also been discussed. Salient features of existing techniques along with the research gap have been clearly highlighted. The research gaps identified in existing work allows future researchers in leveraging the limitations of existing approaches and devising novel methods. Finally the evaluation metrics to evaluate the performance of existing work have been presented for comparative performance evaluation