부산대학교 도서관

Agriculture is the ultimate imperative and primary source of origin to furnish domestic income for multifarious countries. The disease caused in plants due to various pathogens like viruses, fungi, and bacteria is liable for considerable monetary losses in the agriculture corporation across the world. The security of crops concerning quality and quantity is crucial to monitor disease in plants. Thus, recognition of plant disease is essential. The plant disease syndrome is noticeable in distinct parts of plants. Nonetheless, commonly the infection is detected in distinct leaves of plants. Computer vision, deep learning, few-shot learning, and soft computing techniques are utilized by various investigators to automatically identify the disease in plants via leaf images. These techniques also benefit farmers in achieving expeditious and appropriate actions to avoid a reduction in the quality and quantity of crops. The application of these techniques in the recognition of disease can avert the disadvantage of origin by a factious selection of disease features, extraction of features, and boost the speed of technology and efficiency of research. Also, certain molecular techniques have been established to prevent and mitigate the pathogenic threat. Hence, this review helps the investigator to automatically detect disease in plants using machine learning, deep learning and few shot learning and provide certain diagnosis techniques to prevent disease. Moreover, some of the future works in the classification of disease are also discussed.