Insect counting through deep learning-based density maps 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 learning density map estimation to tackle insects counting in wild conditions. To this end, a fully convolutional regression network has been designed to accurately estimate a probabilistic density map for the counting regression problem. The estimated density map is then used for counting whiteflies in eggplant leaves. The proposed method was compared with a baseline based on candidate object selection and classification approach. The results for alive adult whitefly counting by means of density map estimation provided R2 = 0.97 for the counted insects in the main leaf of the image, that outperforms by far the baseline algorithm (R2 = 0.85) based on image processing methods for feature extraction and candidate selection and deep learning-based classifier. This solution was embedded to be used in mobile devices, and it has been gone for exhaustive validation tests, with diverse illumination conditions and background variability, over leaves taken at different heights, with different perspectives and even unfocused images, for the analyzed pest under real conditions.