engleski

The use of RGB and hyperspectral imaging in detection of codling moth and its damages on apple

Codling moth is a cosmopolitan pest that causes economically significant damage in apple production. The damage is visible on the fruits, which lose their organoleptic properties and market value. Therefore, the use of artificial intelligence offers a good perspective for the early detection of the pest and its damage in the field. Information and communication technology has contributed to the use of intelligent devices throughout the agricultural chain. In the context of precision agriculture, the artificial intelligence system is a comprehensive solution for the digitalization of agriculture. In practice, this system involves the creation of an information database, and in the case of pest monitoring, red-green-blue (RGB) and hyperspectral imaging cameras (HSI) can be used. These cameras record the occurrence of pests and damage in orchards. Later, these photos are processed using machine learning methods. Based on all the data, accurate models are developed to identify the target pest and facilitate monitoring and management. Inhibiting factors for the use of cameras can be the high market price, the lack of certain electronic components, and the required expertise. Nonetheless, high-precision classification models for pest monitoring represent the future of agriculture and offer a new opportunity to reduce economic losses caused by codling moth.

Precision agriculture ; Artificial intelligence ; RGB and HSI cameras ; Cydia pomonella L. ; Apple production

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