Machine Vision Systems for Rice Diseases Detection: A Review

作者：Pardeep Seelwal, Alok Sharma · 发表于：2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · 年份：2022 · DOI：10.1109/icacite53722.2022.9823713 · 被引用次数：5 · 研究领域：Smart Agriculture and AI、Spectroscopy and Chemometric Analyses、Leaf Properties and Growth Measurement

To get maximum value added products, quality product monitoring is the most fundamental requirement. Pro-duction of agriculture products can be minimized due to many of the reasons. The fundamental key factor of the quality reduction is the diseases or fungal infection present in the plants. Consequently controlling the diseases or infection in plants require substantially enhancement of the quality production. These infections can lead to severe damage to the rice crop production. Diseases present in rice plants have a critical effect on crop production and accurate detection of disease is the only way to mitigate their effects on the plants. It is a complex task to accurately recognize the disease manually. Early detection and apt remedies is important to expediting the healthy production of the rice plant in order to supply adequately. It also provides foodsecurity to the promptly growing population. The current review addresses the present and projected machine vision approaches for detecting rice disorders. Various techniques have been used to detect the disease present in rice crops is discussed in this paper also associated limitation is also presented. As a result, thispaper discussed the non-destructive and possible applications of machine vision techniques for rice disease diagnosis in depth.