Classification of Coffee Leaf Diseases using the Convolutional Neural Network (CNN) EfficientNet Model

Muhammad Imron Rosadi, Lukman Hakim, M. Faishol A.
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Abstract

Coffee leaf disease is a problem that needs attention because it affects the quality and productivity of the coffee harvest and is detrimental to farmers. Therefore, a system is needed to identify types of coffee leaf diseases using artificial intelligence. There are four types of coffee leaf diseases, namely Miner leaf, Phoma leaf, Rust leaf, and Nodisease leaf. The research used the EfficientNet Architecture Convolutional Neural Network (CNN) method to detect types of disease on coffee leaves. This method was chosen because it is capable and reliable in processing digital images for pattern recognition. The dataset used is 1,464 images with dimensions of 2048 x 1024 pixels with RGB type which are divided into 1,264 training data and 400 testing data. Several architectures used in EfficientNet are EfficientNet B0, EfficientNet B1, EfficientNet B2, EfficientNet B3, EfficientNet B4. Parameters used are Lanczos resampling, Epoch 25, Learning Rate 0.0001, Loss Function Sparse Categorical Cross Entropy, Optimizer Adam. The results of training data testing, namely the CNN EfficientNet B1 Architecture Model method, got the best accuracy of 97% and a loss of 0.1328 and testing data testing got an accuracy of 0.97% and a loss of 0.1328. The architecture of the EfficientNet B1 model is better than other architectural models, namely VGG16, ResNet50, MobileNetv2, EfficientNet B0, EfficientNet B2, EfficientNet B3, EfficientNet B4, EfficientNet B5, EfficientNet B6, EfficientNet B7.
使用卷积神经网络 (CNN) EfficientNet 模型对咖啡叶病进行分类
咖啡叶病是一个需要关注的问题,因为它会影响咖啡收成的质量和产量,并对农民造成损害。因此,需要一个利用人工智能识别咖啡叶病类型的系统。咖啡叶片病害有四种,即螨虫叶、蚜虫叶、锈病叶和结节病叶。研究使用 EfficientNet 架构卷积神经网络(CNN)方法来检测咖啡叶片上的病害类型。之所以选择这种方法,是因为它在处理数字图像进行模式识别方面具有很强的能力和可靠性。所使用的数据集为 1,464 幅图像,尺寸为 2048 x 1024 像素,RGB 类型,分为 1,264 个训练数据和 400 个测试数据。效能网络使用的几种架构分别是效能网络 B0、效能网络 B1、效能网络 B2、效能网络 B3 和效能网络 B4。使用的参数为 Lanczos 重采样、Epoch 25、学习率 0.0001、损失函数 Sparse Categorical Cross Entropy、优化器 Adam。训练数据测试结果,即 CNN EfficientNet B1 架构模型方法的准确率为 97%,损失为 0.1328;测试数据测试的准确率为 0.97%,损失为 0.1328。EfficientNet B1 模型的架构优于其他架构模型,即 VGG16、ResNet50、MobileNetv2、EfficientNet B0、EfficientNet B2、EfficientNet B3、EfficientNet B4、EfficientNet B5、EfficientNet B6、EfficientNet B7。
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