Efficient fruit disease diagnosis on resource-constrained agriculture devices

Le résumé fourni par la source

Climate change in recent years has caught the attention of researchers worldwide, with drastic effects on many economic sectors, especially agriculture and livestock. Due to global warming and declining water levels, food security is becoming an increasingly urgent concern. The situation is more challenging in underdeveloped nations such as Pakistan, where little attention has been paid to improving the processes to produce effective seeds and herbicides. The problem has been exacerbated due to financial constraints and a lack of technology to monitor crops. It has been observed that even a noncritical disease can ruin the crop if timely inspections aren’t performed. Although many solutions have been proposed recently, most are based on heavy mobile applications and cloud-based solutions, which results in considerable computational cost, power consumption, and latency. This work proposes a Deep Neural Networks (DNNs)-based light-weight solution for diagnosing and classifying apple crop diseases for farmers working in rural places (where the internet is not accessible). With the increasing need for efficient and robust Machine Learning (ML) applications on edge devices, such as mobile phones, Raspberry Pi, and Jetson Nano-based devices, there has been a demand for more efficient Deep Neural Networks (DNN) models. To meet this demand, various DNN-based models have been experimented with, including Basic CNN Architecture, AlexNet, and EfficientNet Lite to measure performance After a thorough examination and evaluation of each model’s performance, efficiency, and resource usage, the most optimal model was picked and developed applications for classifying apple crop disease. Using a transfer learning strategy on a specially developed EfficientNet DNN architecture, achieving 85% test accuracy.

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