Tea quality estimation by deep learning of tea field proximity images with a multispectral camera

Abstract

In this research, multispectral images and RGB images are acquired by a camera mounted on a large industrial drone and analyzed by deep learning appropriately, and as a result, it contributes to the quality prediction of agricultural products. Specifically, this drone flies at a height of 5 meters or less above the ground to take an image of a tea plantation, and a method of performing deep learning by extracting feature points by wavelet expansion. This is a research fields, so called “precision agriculture”, can be said that it is highly novel. In a general technique, a multispectral camera is taken from a height of 15 meters or more above the ground and processed into NDVI images or the like to check the condition of the field. However, many agricultural products do not meet the standards. Tea is not the fruit but the leaves themselves is a crop, and it can be said that each tea leaf is small and has a similar color, so the results of image analysis cannot be expected. We think that this can be solved by devising the imaging method, the type of image signal, and the analysis method.The case is more difficult than other crops and is a widely applicable study.