Performance analysis of AI-based solutions for crop disease identification, detection, and classification

Abstract

Agriculture is an integral part of human civilization. Apart from providing Food, Agriculture also contributes to the economy. During crop production, crops are susceptible to insects. The identification and treatment of these insects have become a severe issue. The optimal way to reduce this cost is by early detection of the insects and taking appropriate measures to minimize the damage to crop plants. However, the traditional method lacks to examine the diseases and insects' presence in real-time. This study provides deep learning-based solutions for real-time identification and detection of insects in the Soybean crop. Various Transfer-learning models’ performances are examined to explore the proposed solution's feasibility and reliability to find the insect's identification and detection accuracy. The accuracy achieved with the proposed solution is 98.75%, 97%, and 97% using YoloV5, InceptionV3, and CNN, respectively. Among them, the YoloV5 algorithm's performance in the solution is very fast and can run at 53fps, making them fit for real-time detection. Moreover, a dataset of crop insects was collected and labeled by mixing the images collected using different devices. The proposed study helps reduce the producer's workload and is much simpler, and provides better results.

Keywords

• Object detection
• Computer vision
• Deep learning
• Automated solution