Identification of soil heavy metal pollution by constructing 2D plane using hyperspectral index

Effects of water stress and fertilizer stress on maize growth and spectral identification of different stresses

Water stress and fertilizer stress have a significant impact on the growth and yield of maize. In order to improve the timeliness and accuracy of irrigation and fertilizer application, it is crucial to monitor water stress and fertilizer stress rapidly and accurately. This would help in conserving water and fertilizer resources and ensuring a stable maize yield. To this end, pot experiments were set up to explore the growth differences and photosynthetic properties of maize under water stress and fertilizer stress. The hyperspectral technology was used to construct the spectral indexes that can distinguish stress types, and the classification algorithm was combined to identify stress types. The research has shown that the plant height, basal diameter, leaf area, and photosynthetic properties of maize decreased with an increase in drought stress. However, rewatering could compensate for drought stress. Furthermore, fertilizer stress also affected water uptake by plants, and high nitrogen stress had a significant negative effect on the growth of maize plants. We employed a combination of spectral indexes and the support vector machine (SVM) classification algorithm in a stepwise manner to identify stress types. Using the training dataset, we constructed six classifiers for distinguishing stress types, including the SVM classifier, K-nearest neighbor (KNN) classifier, naive Bayes (NB) classifier, decision tree (DT) classifier, random forest (RF) classifier, and AdaBoost classifier. Our results showed that the RF and AdaBoost classifiers obtained stable results in stress type differentiation, achieving accurate identification of unstressed, water stressed, and fertilizer stressed maize plants. This is expected to provide a solid basis and reference for monitoring crop stress types in agricultural fields.