Khan Ameer Tamoor, Jensen Signe Marie, Khan Abdul Rehman, Li Shuai
Department of Plant and Environmental Science, University of Copenhagen, Copenhagen, Denmark.
Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
Front Plant Sci. 2023 Dec 8;14:1308528. doi: 10.3389/fpls.2023.1308528. eCollection 2023.
In this paper, we address the question of achieving high accuracy in deep learning models for agricultural applications through edge computing devices while considering the associated resource constraints. Traditional and state-of-the-art models have demonstrated good accuracy, but their practicality as end-user available solutions remains uncertain due to current resource limitations. One agricultural application for deep learning models is the detection and classification of plant diseases through image-based crop monitoring. We used the publicly available PlantVillage dataset containing images of healthy and diseased leaves for 14 crop species and 6 groups of diseases as example data. The MobileNetV3-small model succeeds in classifying the leaves with a test accuracy of around 99.50%. Post-training optimization using quantization reduced the number of model parameters from approximately 1.5 million to 0.93 million while maintaining the accuracy of 99.50%. The final model is in ONNX format, enabling deployment across various platforms, including mobile devices. These findings offer a cost-effective solution for deploying accurate deep-learning models in agricultural applications.
在本文中，我们探讨了在考虑相关资源限制的情况下，通过边缘计算设备在农业应用的深度学习模型中实现高精度的问题。传统模型和最新模型已展现出良好的准确性，但由于当前的资源限制，它们作为终端用户可用解决方案的实用性仍不确定。深度学习模型的一个农业应用是通过基于图像的作物监测来检测和分类植物病害。我们使用了公开可用的PlantVillage数据集，其中包含14种作物和6组病害的健康和患病叶片图像作为示例数据。MobileNetV3-small模型成功地对叶片进行了分类，测试准确率约为99.50%。使用量化进行训练后优化，在保持99.50%准确率的同时，将模型参数数量从约150万个减少到93万个。最终模型为ONNX格式，能够在包括移动设备在内的各种平台上进行部署。这些发现为在农业应用中部署准确的深度学习模型提供了一种经济高效的解决方案。
