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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.7
- 수록면
- 97 - 117 (21page)
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초록· 키워드
오류제보하기
Crops such as wheat (Triticum aestivum L.), rice (Oryza sativa L.), maize (Zea mays L.), and soybean (Glycine max L.) are the most important sources of food and ensure food security worldwide. Yield losses because of plant-bacteria and fungi are a major global concern.
Therefore, various management technologies like diagnostic techniques for bacterial and fungal diseases are among the most important criteria for yield loss management from biotic diseases. In particular, the early detection, prediction, and classification of plant diseases are essential for improved monitoring of plant diseases and resources. Over the last few decades, the utilization of digital imaging systems for plant disease identification has received considerable attention.
Moreover, digital imaging methods have facilitated the early detection of several plant diseases using both 2-dimensional image assessment and the current more accurate 3-dimensional image analysis techniques via knowledge-based approaches using MRI and CT. Simultaneously, state-of-the-art, machine and deep learning approaches with different algorithms have progressed to increase precision in disease detection. In this study, we review and discuss important bacterial and fungal diseases that occur in major food crops, the importance of their early detection, and different imaging systems used in plant disease detection, their applications, challenges, and prospects. A literature survey indicated that with the emergence of new tools, the accuracy of the digital imaging system for plant disease detection is bound to increase and has a wide scope in the agriculture sector for helping the farming community as well as increasing sustainability and food security.
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