Analysis of Machine Learning Models Used to Diagnose Rice Plant Diseases-A Review
Keywords:
Rice plant disease, Faster R-CNN, transfer learning, Resnet, convolution neural networksAbstract
As the Indian population is increasing at a faster rate, agricultural productivity also needs to be rapidly increased. Rice is considered the essential food crop in India. However, the rice crop tends to be easily affected by disease-causing agents which results in decreased yield. Though various challenging issues degrade crop productivity like pests, climate changes, and diseases, crop diseases remain the main problem in rice cultivation. Most of the diseases are introduced by or associated with bacteria or fungi and can affect the crop in almost all stages from nursery to harvesting. Conventionally, human vision-based approaches have been employed to detect leaf diseases. They require expert knowledge, a laborious, and expensive process. In addition, the accuracy of the human vision-based process is mainly based on the vision of the farmer or experts. To resolve the limitations of classical approaches, it is needed to design automated Machine Learning (ML) based classifier models. Earlier identification of Rice Plant Diseases (RPD) enables us to take preventive actions and reduce the loss of productivity. This article suggests that the use of machine learning methods in this field has made a significant difference in the farming and productivity sectors, particularly given the more recent Deep Learning, which appeared to have enhanced accuracy levels. This paper emphasizes the study of various diseases on Rice plants along with various methodologies that are adopted for the detection, classification, and prediction of disease early.
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