Phenology recognition using deep learning: DeepPheno

Accurate and autonomous real time plant phenotyping is an essential part of modern crop monitoring and agricultural technologies. Since environmental conditions highly affect a plant's growth, accurate monitoring of phenology can a lot of information that can be used for accelerating crop production. In this paper, a deep learning architecture is utilized to recognize and classify phenological stages of several types of plants. The visual data for plants are captured every half an hour by cameras mounted on the ground agro-stations. We employ a pre-trained Convolutional Neural Network architecture (CNN) 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.