Abstract
With the rapid development of artificial intelligence and deep learning technologies, their applications in the field of agriculture, particularly in plant disease detection, have become increasingly extensive. This study focuses on the high-precision detection of tomato diseases, which is of paramount importance for agricultural economic benefits and food safety. To achieve this aim, a tomato disease image dataset was first constructed, and a NanoSegmenter model based on the Transformer structure was proposed. Additionally, lightweight technologies, such as the inverted bottleneck technique, quantization, and sparse attention mechanism, were introduced to optimize the model's performance and computational efficiency. The experimental results demonstrated excellent performance of the model in tomato disease detection tasks, achieving a precision of 0.98, a recall of 0.97, and an mIoU of 0.95, while the computational efficiency reached an inference speed of 37 FPS. In summary, this study provides an effective solution for high-precision detection of tomato diseases and offers insights and references for future research.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.