| Summary: | Computer vision is a consistent and advanced technique for image processing, with the propitious outcome, and enormous potential. A computer vision has been strongly adopted in the heterogeneous domain including agriculture. During the study of existing research on the role of computer vision in fruits and vegetables among various horticulture products of agriculture fields it is noticed that, the existing survey paper has not focused properly on mathematical framework, feature descriptor, defect detection on multiple datasets of fruits and vegetables elaborately. This has motivated us to undertake an extensive survey. In this paper, we examine the paper broadly related to fruits and vegetables among various horticulture products of agriculture fields, specific model, data pre-processing, data analysis method and overall value of performance accuracy by using a particular performance metric. Moreover, we study the different type of disease present in various fruit and vegetable. We have also focused on the comparison of different machine learning approach with respect to different performance metrics on the same dataset. Thus, we have found that among all existing machine learning techniques SVM give better classification accuracy. A generalized framework to grade the quality and defect detection of multiple fruits and vegetables is also proposed in this survey. This paper covers the survey of ninety-eight papers closely related to computer vision in the agricultural field. By the survey, we have found that computer vision plays an important role and has a large potential to address the challenges related to the agricultural fields.
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