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Early plant disease detection plays a significant role in efficient crop yield. Plant diseases like black measles, black rot, bacterial spot, etc. affect the growth, crop quality of plants and economic impacts in the agriculture industry. To avoid the impact of these diseases, expensive approaches and the use of pesticides are some solutions the farmers usually implement. The use of chemical means damages the plant and the surrounding environment. In addition, this kind of approach intensifies the cost of production and major monetary loss to farmers. Early discovery of diseases as they occur…mehr

Produktbeschreibung
Early plant disease detection plays a significant role in efficient crop yield. Plant diseases like black measles, black rot, bacterial spot, etc. affect the growth, crop quality of plants and economic impacts in the agriculture industry. To avoid the impact of these diseases, expensive approaches and the use of pesticides are some solutions the farmers usually implement. The use of chemical means damages the plant and the surrounding environment. In addition, this kind of approach intensifies the cost of production and major monetary loss to farmers. Early discovery of diseases as they occur is the most important period for efficient disease management. Manual disease detection through human experts to identify and recognize plant diseases is a usual practice in agriculture. With the improvements in technology, automatic detection of plant diseases from raw images is possible through computer vision and artificial intelligence researches.
Autorenporträt
Dr. M. Aravind Kumar obtained B. Tech Degree in ECE, M.Tech Degree in VLSI System Design from JNTUH, and Ph.D. from GITAM University, Visakhapatnam. He has 15 years of teaching experience. He is a Life member of FIE, ISTE, IETE, SCIEI, UACEE, and IAENG. He Published 35 Research Papers in refereed Journals and Conferences.