Phenology recognition using deep learning

One of the essential part of agricultural technologies and crop monitoring is automating accurate plant phenotyping. Environmental conditions have tremendous impact on a plant's growth. Hence, accurate monitoring of phenology can provide a lot of information that can be used for increasing the yield quality and accelerating crop production. Advancements in both computer vision algorithms and communication systems have been transforming the perception of precision agriculture. Enourmous amount of information is being collected through sensors positioned on ground stations in national agriculture monitoring networks. Availability of this higher-quality measurements coupled with modern image processing algorithms steadily grows the applications possibilites in agriculture. The advancement of machine learning techniques offer a different approach comparatively to the traditional ways for agricultural applications. In this paper, we employ a deep learning approach to recognize and classify phenological stages of agricultural plants. The visual data for plants are captured every half an hour by cameras mounted on the ground agro-stations. In contrast to traditional feature extraction approaches, a pre-trained Convolutional Neural Network architecture (CNN) is employed to automatically extract the features of images. The results obtained through CNN model are compared with those obtained by employing hand crafted feature descriptors. Experimental results indicate that CNN architecture outperforms the machine learning algorithms based on hand crafted features.