Embedded-based Tomato Septoria Leaf Detection with Intel Movidius Neural Compute Stick

Tomatoes are a horticulture product with the potential to be developed since they have a high economic value and are in high demand by industry and consumers. Tomato plants, on the other hand, still need to be handled carefully in order to boost yield harvest. Furthermore, susceptibility of the tomato plants to Septoria leaf spot disease, which arises due to Septoria Lycopersici Speg fungal infection, is being one of the challenges in escalating tomato production itself. Regarding the problem, this study aims to detect Septoria leaf spot on tomato plants by developing a tool utilizing deep learning and Convolutional Neural Network (ConvNets or CNN) model. CNN model conducted in this work is a supervised learning technique that extensively operated for solving linear and non-linear problems. Moreover, Raspberry Pi microcontroller and Intel Movidius Neural Computing Stick (NCS) are employed in this work, in order to accelerate the computing process and to ease the detection process considering its portability, speed, and accuracy. The precise detection range is from 84.22 percent to 100 percent, with an average accuracy rate of 95.89 percent.