Towards real-time crops surveillance for disease classification: exploiting parallelism in computer vision
Abstract Considering the incessantly increasing economic losses due to plant diseases in the agricultural sector, we have designed a real-time system capable of classifying plant diseases. In this context, we have proposed an image processing algorithm that transforms the image into three colorspaces, which are processed simultaneously. The algorithm executes in a series of intermediate steps, including contrast stretching, feature vector construction, and identification of salient regions. To enable effective execution, we have also proposed the underlying On-Chip communication architecture that allows efficient interconnection between the three digital signal processing cores, each processing its own colorspace. The architecture has been synthesized for 90 nm process, as well as on an FPGA, achieving a post-layout operational frequency of 644 MHz, and an area of 1208.9 µm 2 on the die. We demonstrate that our system outperforms few existing works in literature in terms of accuracy and computation time.