Strawberry (Fragraria x ananassa) is a crop affected by various soil-borne fungal pathogens with mostly non-specific foliar symptoms and often requiring laboratory isolation for correct diagnosis. Moreover, these nonspecific foliar symptoms, appreciated by the human eye, appear after some time following infection by the pathogen. Early detection of plant diseases is one of the primary objectives in agriculture because it may contribute to identifying more tolerant cultivars in breeding programs and optimise pesticide use in agricultural production with earlier applications in emerging disease foci. New technologies, such as remote sensing and machine learning (ML) algorithms, have arisen as potential tools to improve the ability to detect and classify different crop diseases. The combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants. Six ML models, namely artificial neural network, decision tree, K-nearest neighbour, support vector machine, multinomial logistic regression and Naïve Bayes were developed to estimate physiological stress associated with Fusarium wilt disease. The results showed that stomatal conductance (g_s) and photosynthesis (A) declined even without visual symptoms of the disease. Among the six ML models evaluated, the artificial neural network model showed the highest classification performance with an overall accuracy of 81%, regardless of the physiological parameter utilized for model training. Moreover, the artificial neural network accurately predicted the absolute values of both physiological parameters (g_s and A) based on the complete spectral signature from visually healthy foliar tissue, achieving coefficients of determination of 84% and 81%, respectively. Consequently, ML models utilizing physiological response data and hyperspectral imaging exhibited remarkable robustness, facilitating the estimation of Fusarium wilt severity in strawberry plants even without visual symptoms.

草莓 (Fragraria x ananassa) 是一种受多种土传真菌病原体影响的作物，大多具有非特异性叶面症状，通常需要实验室隔离才能正确诊断。此外，这些肉眼可见的非特异性叶部症状是在病原体感染一段时间后出现的。植物病害的早期检测是农业的主要目标之一，因为它可能有助于在育种计划中识别更具耐受性的品种，并通过在新出现的病害疫源地早期应用来优化农业生产中农药的使用。遥感和机器学习（ML）算法等新技术已成为提高不同作物病害检测和分类能力的潜在工具。研究了结合使用高光谱图像和机器学习算法来检测和分类草莓植株中枯萎病早期感染引起的生理应激。开发了六种机器学习模型，即人工神经网络、决策树、K 最近邻、支持向量机、多项逻辑回归和朴素贝叶斯，用于估计与枯萎病相关的生理应激。结果表明，即使没有疾病的视觉症状，气孔导度（g _s ）和光合作用（A）也会下降。在评估的六个 ML 模型中，无论用于模型训练的生理参数如何，人工神经网络模型都显示出最高的分类性能，总体准确率为 81%。 此外，人工神经网络根据视觉健康叶组织的完整光谱特征准确预测了两个生理参数（g _s 和 A）的绝对值，确定系数分别达到 84% 和 81%，分别。因此，利用生理反应数据和高光谱成像的机器学习模型表现出显着的稳健性，即使没有视觉症状，也有助于估计草莓植物中镰刀菌枯萎病的严重程度。