This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.

中文翻译：

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用于茄子作物叶斑病实时检测的光谱数据驱动的机器学习分类模型


本研究介绍了使用光谱传感器数据检测茄子作物叶斑病的机器学习模型的开发和评估。在九个波段（F1：415 nm、F2：445 nm、F3：480 nm、F4：515 nm、F5：555 nm、F6：590 nm、F7：630 nm、F8：680 nm 和 F9：NIR-750 nm）上记录病变和健康组织的光谱反射率。数据揭示了不同的光谱特征，特别是在 F5 （555 nm） 和 F9 （NIR） 之间，与健康组织相比，病变组织始终表现出较低的反射率。采用两种机器学习算法，决策树 （DT） 和支持向量机 （SVM） 对光谱数据进行分类。DT 模型实现了 88.2% 的最大测试精度，基尼系数和 4 深度是最佳超参数。混淆矩阵表明，DT 模型正确识别了 883 个患病病例和 667 个健康病例，同时将 213 个健康组织错误分类为患病组织，将 25 个患病组织错误分类为健康组织。SVM 模型配置了 10.0 的成本参数和 0.01 的容差，其性能优于 DT 模型，达到了 92.4 % 的测试精度。SVM 模型正确分类了 99.3% 的患病病例和 94.1% 的健康病例。结果表明，光谱传感器数据与 ML 算法相结合，具有精确疾病检测的潜力，可促进有针对性的农药应用，并降低投入成本。SVM 模型的高精度强调了其在农业病害管理中的实用性，可实现早期干预并加强作物健康监测。未来的研究可能会探索集成多个传感器和先进的特征提取方法，以进一步提高这些系统的效率和准确性。