Precision Agriculture: Classifying Banana Leaf Diseases with Hybrid Deep Learning Models

D. Banerjee, V. Kukreja, S. Hariharan, Vandana Sharma
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Abstract

The majority of the people in India dependent on farming to earn a living. As a due to climate change, farmers face various challenges. One of them is a reduction in yield, and one of the causes of that is the development of diseases in the plant. The main economic agricultural activity is a banana plantation, particularly in Asian and African nations. Feature extraction using CNN and SVM was used to identify and classify the banana fruit leaf diseases. The dataset was initially improved, precompiled using Matlab code, and then divided into training and testing sections. During the conduct of this research, the ratio employed to divide the data into training and validation was 80:20. After the CNN was implemented successfully, and the SVM models, the maximum average accuracy measured was 94%. According to this study, the suggested model achieves the automatic right diagnosis of banana leaf diseases and gives a workable method for the detection of crop leaf diseases with high recognition accuracy.
精准农业:用混合深度学习模型对蕉叶病害进行分类
在印度,大多数人靠务农为生。由于气候变化,农民面临着各种挑战。其中之一是产量下降,其中一个原因是植物疾病的发展。主要的经济农业活动是香蕉种植园,特别是在亚洲和非洲国家。采用CNN和SVM相结合的特征提取方法对香蕉叶片病害进行识别和分类。首先对数据集进行改进,使用Matlab代码进行预编译,然后将其分为训练和测试部分。在进行本研究时,将数据分为训练和验证的比例为80:20。在CNN和SVM模型成功实现后,测得的最大平均准确率为94%。通过本研究,该模型实现了香蕉叶片病害的自动正确诊断,为作物叶片病害的检测提供了一种具有较高识别精度的可行方法。
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