Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm

Nancy C, Kiran S
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Abstract

Agriculture plays a vital role in India's economy, and the health of crops is critical for maximizing yield. In particular, cucumber, a key salad ingredient known for its health benefits, is susceptible to various diseases such as water mold, bacterial wilt, angular leaf spot, anthracnose, and powdery mildew. These diseases not only affect the quality of cucumbers but also significantly reduce their yield. Early detection of these diseases is crucial for successful cultivation, but traditional manual methods of disease identification by farmers or diagnosticians are time-consuming and prone to misidentification. To address these challenges, we explore advanced artificial intelligence techniques. We implement and compare various machine learning algorithms, including ResNet, AlexNet, and VGG-16, for disease classification in cucumbers. However, these methods often struggle with issues such as noise, irrelevant features, and the generation of pertinent characteristics. To overcome these limitations, we propose a novel approach using a GLCM (Gray Level Co-occurrence Matrix) feature extraction method combined with a Random Forest classifier. This new algorithm aims to improve the accuracy and efficiency of disease detection. Our dataset comprises four distinct categories: Healthy, Anthracnose, Aphids, and CYSDV. It is sourced from diverse platforms, including online repositories like kaggle and direct collection from cucumber farms. The initial phase of our methodology involves noise reduction by converting images into the LAB color space and isolating specific regions using the k-means clustering algorithm. Subsequently, we extract texture features from the diseased leaf images using the GLCM algorithm, and classification is performed using the Random Forest model. Comparative analysis shows that our proposed Random Forest algorithm outperforms previous models like LGBM (Light Gradient Boosting Machine) and QSVM (Quantum-Support Vector Machine) in predicting disease presence in cucumber plants with higher accuracy rate of 98.62%, Precision 98.77%, Recall 98.48% and also F1 Score 98.62%.
利用随机森林算法的 GLCM 特征检测黄瓜叶片病害
农业在印度经济中发挥着至关重要的作用,而农作物的健康对于最大限度地提高产量至关重要。特别是黄瓜,这种以健康而闻名的重要沙拉配料很容易受到各种病害的侵袭,如水霉病、细菌性枯萎病、角斑病、炭疽病和白粉病。这些病害不仅会影响黄瓜的质量,还会大大降低黄瓜的产量。及早发现这些病害对成功种植至关重要,但农民或诊断人员传统的人工病害识别方法既费时又容易造成误判。为了应对这些挑战,我们探索了先进的人工智能技术。我们实施并比较了各种机器学习算法,包括 ResNet、AlexNet 和 VGG-16,用于黄瓜的病害分类。然而,这些方法往往难以解决噪声、不相关特征和相关特征的生成等问题。为了克服这些局限性,我们提出了一种使用 GLCM(灰度共生矩阵)特征提取方法与随机森林分类器相结合的新方法。这种新算法旨在提高疾病检测的准确性和效率。我们的数据集包括四个不同的类别:健康、炭疽、蚜虫和 CYSDV。数据集来自不同的平台,包括 kaggle 等在线资源库和直接从黄瓜农场收集的数据。我们方法的初始阶段包括通过将图像转换为 LAB 色彩空间和使用 k-means 聚类算法隔离特定区域来降低噪音。随后,我们使用 GLCM 算法从病叶图像中提取纹理特征,并使用随机森林模型进行分类。对比分析表明,我们提出的随机森林算法在预测黄瓜植株是否存在病害方面优于 LGBM(光梯度提升机)和 QSVM(量子支持向量机)等先前的模型,准确率高达 98.62%,精确率 98.77%,召回率 98.48%,F1 分数 98.62%。
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