Yapay Görme ile Bitkilerin Fenolojik Evrelerinin Gözlemlenmesi

Concerns due to decreasing natural resources and expected growth of world population lead scientists to invest more research into potential improvement of agricultural efficiency using tools of computing technology. Improvements that can be achieved by monitoring phenology of agricultural plants become critical due to its effect on 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 texture based feature descriptors outperform texture based feature descriptors for the discrimination of phenological stages.