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Due to increasing human interference and continuing climate change, crops are facing a variety of stresses, including diseases, insect pests, drought, heat, cold, frost, flooding, excessive fertilization, and environmental pollution. Crops are characterized by a variety of planting types, a wide distribution range, and significant differences in growing environments. It is therefore essential to accurately and rapidly identify and quantify these stresses on a much larger scale. Advanced methods and measurements are required to overcome the limitations of traditional manual surveys and visual observations. Data acquisition approaches must shift from in situ random sampling to continuous airborne or spaceborne monitoring. The rapid development of hyperspectral and multispectral imaging (HSI and MSI, respectively) techniques has facilitated significant advancements in monitoring, classification, identification, and diagnosis in crop fields. At present, such methods are widely used in areas such as agriculture, forestry, pasture, and ecology, among others. However, data preprocessing, feature selection algorithms, sample quantity, classifiers, and accuracy assessment often vary based on different crops and HSI and MSI data sources. There are a number of critical issues that need to be urgently addressed, such as small-sample classification, spectral dimensionality reduction, and the selection of sensitive spectral bands. The following Special Issue aims to facilitate the exchange of knowledge and promote development regarding any aspect related to crop fields based on various HSI and MSI techniques, thereby facilitating their introduction and application in precise monitoring and diagnosis.