Phenology monitoring of agricultural plants using texture analysis

Power of computing technology to improve the efficiency in agricultural fields is becoming increasingly more important with the current projections of expected world population growth and decrease in available land and natural resources. One of the critical improvements can be achieved by monitoring phenology of agricultural plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Inferring the phenological information contributes to a better understanding of relationships between productivity, vegetation health and environmental conditions. As part of a government supported project, a terrestrial observation network is built throughout Turkey. The network includes over twelve hundred agro-stations that are placed on many agricultural fields. The stations are equipped with many sensors including cameras that acquire image sequences of the farm fields in a periodic manner. In this study, we use textural analysis combined with machine learning techniques to develop measures in order to recognize and classify phenological stages of several types of plants purely based on the visual data captured every half an hour by cameras mounted on the ground agro-stations. Experimental results suggest that Histogram of Oriented Gradients (HOG) features outperform Gray Level Co-occurrence Matrix (GLCM) features for the discrimination of phenological stages.