Application of deep learning and data mining for the study of plant pathogen interaction: the case of apple and pear scab | Mašīnu dziļās mācīšanās un datizraces pielietošana augu un patogēnu mijiedarbības izpētei ābeļu un bumbieru kraupja patosistēmās
2021
Lacis, G., Latvia Univ. of Life Sciences and Technologies, Cerini, Krimuna Parish, Dobele Municipality (Latvia). Inst. of Horticulture | Morocko-Bicevska, I., Latvia Univ. of Life Sciences and Technologies, Cerini, Krimuna Parish, Dobele Municipality (Latvia). Inst. of Horticulture | Sokolova, O., Latvia Univ. of Life Sciences and Technologies, Cerini, Krimuna Parish, Dobele Municipality (Latvia). Inst. of Horticulture | Kodors, S., Rezekne Academy of Technologies (Latvia)
Fruit growing is an important niche in the structure of agriculture. Apple and pear are the most widely grown and economically significant fruit crops in the world and in Latvia, while the scab caused by Venturia inaequalis and V. pyrina are the most important diseases for these species. Smart or precision horticulture is a way to ensure this and provides a close linkage between research on local resources, environmental issues and information technologies, where common work can promote the development of fruit-growing. The aim of the study was the development of an integrated decision-making system using knowledge on plantpathogen-environment interactions in apple/V. inaequalis and pear/V. pyrina pathosystems. The following objectives were defined to fulfil the aim: 1) application of semantic analysis and data mining for plant-pathogen interaction data in apple/V. inaequalis and pear/V. pyrina pathosystems; 2) development and implementation of an image-based deep learning system for early identification and evaluation of apple and pear scab; 3) development of IoT–system model for apple and pear monitoring. The results of this study provided the knowledge on plant-pathogen interaction mechanisms, their use for disease monitoring and prognosis.
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