A System for Automatic Rice Disease Detectionfrom Rice Paddy Images Serviced via

A System for Automatic Rice Disease Detectionfrom Rice Paddy Images Serviced via a Chatbot

A rice disease diagnosis LINE Bot System from paddy field images was presented in this paper. An easy-to-use automatic rice disease diagnosis system was necessary to help rice farmers improve yield and quality.We targeted on the images took from the paddy environment without special sample preparation. We used a deep learning neural networks technique to detect rice disease in the images. We purposed object detection model training and refinement process to improve the performance of our previous rice leaf diseases detection research. The process was based on analyzing the model's predictive results and could be repeatedly used to improve the quality of the database in the next training of the model. The deployment model for our LINE Bot system was created from the selected best performance technique in our previous paper, YOLOv3, trained by refined training data set. The performance of deployment model was measured on 5 target classes by average mAP improved from 82.74% in previous paper to 89.10%. We purposed Rice Disease LINE Bot system used this deployment model. Our system worked automatically real-time to suggest primary rice disease diagnosis results to the users in the LINE group. Our group included of rice farmers and rice disease experts, and they could communicate freely via chat. In the real LINE Bot deployment, the model's performance measured by our own defined measurement Average True Positive Point was 78.86%. It took approximately 2-3 seconds for detection process in our system servers.

通过聊天机器人从水稻图像中自动检测水稻疾病的系统

本文提出了一种基于稻田图像的水稻病害诊断系统。一个易于使用的水稻疾病自动诊断系统对于帮助稻农提高产量和质量是必要的。.. 我们的目标是在没有特殊样品准备的情况下从稻田环境中拍摄的图像。我们使用了深度学习神经网络技术来检测图像中的水稻疾病。我们针对对象检测模型进行训练和完善过程，以提高我们以前的稻叶病检测研究的性能。该过程基于对模型的预测结果的分析，可以在下次模型训练中反复使用以提高数据库的质量。LINE Bot系统的部署模型是根据我们先前的论文YOLOv3中选择的最佳性能技术创建的，并通过精炼的培训数据集进行了培训。通过对5个目标类别的平均mAP衡量部署模型的性能，从之前的82.74％提高到89.10％。我们的目标是水稻病LINE Bot系统使用此部署模型。我们的系统自动实时工作，以向LINE组的用户建议主要的水稻疾病诊断结果。我们的小组包括水稻种植者和水稻疾病专家，他们可以通过聊天自由交流。在实际的LINE Bot部署中，通过我们自己定义的度量“平均真实正点”衡量的模型性能为78.86％。我们的系统服务器中的检测过程大约花费了2-3秒。通过我们自己定义的度量标准测得的性能，平均真实积极点为78.86％。我们的系统服务器中的检测过程大约花费了2-3秒。通过我们自己定义的度量标准测得的性能，平均真实积极点为78.86％。我们的系统服务器中的检测过程大约花费了2-3秒。 （阅读更多）