FPGA Based Plant Disease Identification using Convolution Neural Networks
FPGA Based Plant Disease Identification using
Convolution Neural Networks
Abstract:
— It is a very difficult task in agriculture to identify crop diseases in a timely and precise manner. Plant diseases are the principal causes of a significant decrease in crop production quality and quantity. In order to prevent damage to the entire plant, early detection of plant diseases is crucial. Numerous deep learning neural network models have been devised to address this issue. However, it requires more computation and consumes more time. This technique seeks to distinguish between a plant’s healthy and diseased leaves. In this study, we’ve demonstrated how we utilized MATLAB to transform an image into a hex file, and then .hex file is fed as final input in Vivado for classification. This paper proposes an architecture for plant disease detection based on the CNN algorithm, and its implementation on FPGAs (Field Programmable Gate Arrays).
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FPGA Based Plant Disease Identification using
Convolution Neural Networks
