Watermelon is a crop susceptible to diseases. Rapid and effective detection of watermelon diseases is of great significance to ensure the yield of watermelon. Aiming at the interference of the environment and obstacles in the natural environment, resulting in low target detection accuracy and poor robustness, this paper takes watermelon leaves as the research object, considering anthracnose, leaf blight, leaf spot and normal leaves as examples. A disease recognition method based on deep learning is proposed. This paper has improved the pre-selected box setting formula of the SSD model and tested it in multiple SSD models. Experiments show that the average accuracy of the final SSD768 model is 92.4%, and the average accuracy of the IOU is 88.9%. It shows that this method can be used to detect watermelon diseases in natural environment.

中文翻译：

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基于深度学习的西瓜病害检测

西瓜是一种易受病害的作物。西瓜病害的快速有效检测对保证西瓜产量具有重要意义。针对自然环境中环境和障碍物的干扰，导致目标检测精度低、鲁棒性差的问题，本文以西瓜叶片为研究对象，以炭疽病、叶枯病、叶斑病和正常叶片为例。提出了一种基于深度学习的疾病识别方法。本文对SSD模型的预选框设置公式进行了改进，并在多个SSD模型中进行了测试。实验表明，最终 SSD768 模型的平均准确率为 92.4%，IOU 的平均准确率为 88.9%。表明该方法可用于自然环境中西瓜病害的检测。