A Survey on Different Plant Diseases Detection Using Machine Learning Techniques

Abstract

Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer’s profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively.

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“…Similarly, Cardellicchio et al [5] applied the YOLOV5 algorithm to detect the phenotypic features of tomato plants, enabling the monitoring of the tomato growth process and yield prediction. However, it is worth noting that object detection technology based on computer vision is also gradually being implemented in plant disease feature extraction [6]. In 2021, Wang et al [7] conducted a study of rice disease identification based on multi-model transfer learning.…”