Identification of lesser cornstalk borer-attacked maize plants using infrared images

• Autor(a) principal: Zandonadi, R. S.
• Data de Publicação: 2005
• Outros Autores: Pinto, F. A. C., Sena Jr, D. G., Queiroz, D. M., Viana, P. A., Mantovani, E. C.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: https://doi.org/10.1016/j.biosystemseng.2005.05.002; http://www.locus.ufv.br/handle/123456789/22216
• topic: Maize; Infrared images; Cornstalk borer-attacked

The lesser cornstalk borer (Elasmopalpus lignosellus) is a pest that damages the maize plants in the initial growing phase causing stand reduction that can result in yield decrease. The machine vision system could be an alternative for the development of site-specific management of this pest. The objective of this work was to develop and evaluate a machine vision algorithm for identifying maize plants attacked by lesser cornstalk borer based on colour infrared images. To develop the algorithm, images of 40 maize plants were taken on different days after emergency. The plants were grown in pots, and 25 of them were infested with lesser cornstalk borer larvae and 15 were left healthy. The algorithm had three stages: leaf identification, image block classification, and plant classification. In the leaf identification stage, the plant leaves were segmented by thresholding the normalised difference vegetation index image. For the block classification stage, different neural network architectures and block sizes were tested for identification of non-attacked and attacked plant image blocks. Then, in the plant classification stage, discriminating functions were used to classify the scene as either a healthy or an attacked plant. The algorithm performance was compared with the performance of four human experts by using the Kappa coefficient of agreement. The largest size of image block, 9 by 9 pixels, was chosen because of its less computational exigency and because its performance was not significantly different from the other tested block sizes. The algorithm performance was significantly better than just one human expert. The Kappa coefficients for the algorithm and the three best human experts were 63·0 and 49·7%, respectively. The overall accuracy of the algorithm and the best three human experts was 81·6 and 73·4%, respectively.