A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning
A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning
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DOI：
10.3390/agriengineering3030032
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发表时间：
2021-09-01
期刊：
影响因子：
2.8
通讯作者：
Reddy, Gopireddy Harshavardhan
中科院分区：
文献类型：
--
作者：
Ahmed, Ahmed Abdelmoamen;Reddy, Gopireddy Harshavardhan
植物病害是全球农业部门面临的重大挑战之一。在美国，作物病害每年造成三分之一的作物产量损失。尽管作物病害诊断很重要，但如果通过光学观察植物叶片症状来进行诊断，对于资源有限的农民来说仍是一项挑战。因此，迫切需要显着改进作物病害的检测、监测和预测，以减少作物农业损失。在此背景下，机器学习 (ML) 赋能的计算机视觉对于改善大规模作物监测具有巨大前景。本文提出了一种基于机器学习的移动系统，用于自动化植物叶病诊断过程。开发的系统使用卷积神经网络 (CNN) 作为底层深度学习引擎，用于对 38 种疾病进行分类。我们收集了一个图像数据集，其中包含 96,206 张健康和受感染植物的植物叶子图像，用于训练、验证和测试 CNN 模型。用户界面被开发为 Android 移动应用程序，允许农民拍摄受感染植物叶子的照片。然后它会显示疾病类别以及置信百分比。预计该系统将为农民创造更好的机会，以保持农作物健康，并消除使用可能对植物造成压力的错误肥料。最后，我们使用各种性能指标（例如分类准确性和处理时间）评估了我们的系统。我们发现我们的模型在识别 14 种作物物种中最常见的 38 种疾病类别方面实现了 94% 的总体分类准确率。
Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves' symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species.