Deep learning to recognize and count green leafhoppers

• Published in: Bulletin of the National Research Centre Vol. 46; no. 1; pp. 1 - 5
• Main Authors: Proença, Maria da Conceição, Rebelo, Maria Teresa
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 27.06.2022; Springer Nature B.V; SpringerOpen
• Subjects: Agricultural practices; Agriculture; Algorithms; Auchenorrhyncha-typhlocybinae pests; Cicadellidae; Computers; Datasets; Deep learning; Deep-learning methods; Economic importance; Global changes; Humanities and Social Sciences; Image processing; Insects; Integrated pest management; Laptop computers; multidisciplinary; Pest control; Pest counting; Pest recognition; Pests; Research centers; Science; Science (multidisciplinary); Traps; Vines; Vineyards; Wineries & vineyards; Pest counting; Deep-learning methods; Image processing; Auchenorrhyncha-typhlocybinae pests; Global changes; Pest recognition
• ISSN: 2522-8307; 2522-8307
• DOI: 10.1186/s42269-022-00875-0

Background Vineyards are a crop of great economic importance in Portugal, whose production of over 224 kha of vines may be affected by evolving global changes, as new pests arrive in greater numbers at more northern latitudes. Integrated pest management requires early recognition and assessment of pests to enable a proportionate response in control. Results Using yellow sticky traps to catch green leafhoppers in the vineyards under attack, we could use the image of the traps and deep learning methods to evaluate with high accuracy the number of insects presents and establishes a procedure to assess any number of traps in a short period of time. Conclusions Implementation is possible with ordinary laptop computers and could contribute to more extensive and more frequent coverage in surveillance, since the human labor required to count hundreds of insects in each trap is reduced to seconds.