Insect counting through deep learning-based density maps estimation
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Author/sBereciartua-Pérez, Arantza; Gómez, Laura; Picón, Artzai; Navarra-Mestre, Ramón; Klukas, Christian; [et al.]
Convolutional neural network
Density map estimation
Digitalization and automation of assessments in field trials are established practice for farming product development. The use of image-based methods has provided good results in different applications. Although these models can leverage some problems, they still perform poorly under real field conditions using mobile devices on complex applications. Among these applications, insect counting and detection is necessary for integrated pest management strategies in order to apply specific treatments at early infection stages to reduce economic losses and minimize chemical usage. Currently the counting task for the assessment of the degree of infestation is done manually by the farmer. Current state of the art object counting methods do not provide accurate counting in crowded images with overlapped or touching objects which is the case for insect counting images. This makes necessary to define novel approaches for insect counting. In this work, we propose a novel solution based on deep ...