Deep learning to recognize and count green leafhoppers

Abstract

Abstract 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.

Keywords

• Pest counting
• Pest recognition
• Deep-learning methods
• Auchenorrhyncha-typhlocybinae pests
• Image processing
• Global changes