Monitoring System for Leucoptera malifoliella (O. Costa, 1836) and Its Damage Based on Artificial Neural Networks

Dana Čirjak (), Ivan Aleksi, Ivana Miklečić, Ana Marija Antolković, Rea Vrtodušić, Antonio Viduka, Darija Lemic, Tomislav Kos and Ivana Pajač Živković
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Dana Čirjak: Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia
Ivan Aleksi: Department of Computer Engineering and Automation, Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31 000 Osijek, Croatia
Ivana Miklečić: Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia
Ana Marija Antolković: Department of Pomology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia
Rea Vrtodušić: Department of Pomology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia
Antonio Viduka: Department of Plant Nutrition, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia
Darija Lemic: Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia
Tomislav Kos: Department of Ecology, Agriculture and Aquaculture, University of Zadar, Trg Kneza Višeslava 9, 23 000 Zadar, Croatia
Ivana Pajač Živković: Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia

Agriculture, 2022, vol. 13, issue 1, 1-19

Abstract: The pear leaf blister moth is a significant pest in apple orchards. It causes damage to apple leaves by forming circular mines. Its control depends on monitoring two events: the flight of the first generation and the development of mines up to 2 mm in size. Therefore, the aim of this study was to develop two models using artificial neural networks (ANNs) and two monitoring devices with cameras for the early detection of L . malifoliella (Pest Monitoring Device) and its mines on apple leaves (Vegetation Monitoring Device). To train the ANNs, 400 photos were collected and processed. There were 4700 annotations of L . malifoliella and 1880 annotations of mines. The results were processed using a confusion matrix. The accuracy of the model for the Pest Monitoring Device (camera in trap) was more than 98%, while the accuracy of the model for the Vegetation Monitoring Device (camera for damage) was more than 94%, all other parameters of the model were also satisfactory. The use of this comprehensive system allows reliable monitoring of pests and their damage in real-time, leading to targeted pest control, reduction in pesticide residues, and a lower ecological footprint. Furthermore, it could be adopted for monitoring other Lepidopteran pests in crop production.

Keywords: apple pests; automatic monitoring systems; deep learning models; site-specific crop management; sustainable agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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