期刊
AMERICAN JOURNAL OF POTATO RESEARCH
卷 -, 期 -, 页码 -出版社
SPRINGER
DOI: 10.1007/s12230-023-09924-4
关键词
Colletotrichum coccodes; Image analysis; Postharvest management; Machine learning; Tuber blemish disease
类别
Potato black dot disease has become an increasingly economic problem, leading to lower aesthetic quality of potatoes for the pre-pack market. Traditional management methods at pre- and postharvest stages have been unsatisfactory, and thus new approaches are needed for better management and detection of this disease.
Potato black dot is a foliar and tuber blemish disease that has become an increasingly economic problem in recent years. Black dot is caused by the fungus Colletotrichum coccodes and is characterised by silver/brown lesions on the tuber skin leading to lower aesthetic quality of potatoes destined for the pre-pack market. Given the consumers' growing demand for washed and pre-packed potatoes, skin blemish diseases (such as black dot and silver scurf), once considered of minor importance, are now serious challenges for the fresh potato industry. The management of C. coccodes is far from satisfactory at either pre- or postharvest stages: firstly, the disease symptoms have not been consistently described on potato plant foliage; and secondly, black dot disease is often confounded with other tuber blemishes during postharvest storage. Good field managing practices in combination with improved postharvest strategies and an accurate detection support tool can be a useful integrated approach to manage potato black dot disease. This review aims to evaluate and critically discuss different novel approaches for better management and detection of potato black dot disease.
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