Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation

• Published in: Horticulturae 2024-08, Vol.10 (8), p.811
• Main Authors: Suyala, Qiqige, Li, Zhuoling, Zhang, Zhenxin, Jia, Liguo, Fan, Mingshou, Sun, Youping, Xing, Haifeng
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Algorithms; Analysis; Arid zones; Back propagation networks; Canopies; Crop yield; Drought; hyperspectral; Irrigation; Irrigation water; Least squares method; Leaves; Loam soils; Moisture content; Neural networks; Parameter sensitivity; Plants (botany); Potassium; potato; Potatoes; precision irrigation; Reflectance; Regression analysis; Remote control; Remote monitoring; Remote sensing; Semi arid areas; Semiarid lands; Spectral reflectance; Support vector machines; Vegetable industry; Vegetables; Vegetation; Water; Water conservation; Water content; Water demand; water diagnosis models; Water monitoring; Water shortages; Water stress; Water supply; Water treatment; Wheat
• ISSN: 2311-7524; 2311-7524
• DOI: 10.3390/horticulturae10080811

Appropriate water supply is crucial for high-yield and high-quality potato tuber production. However, potatoes are mainly planted in arid and semi-arid regions in China, where the precipitation usually cannot meet the water demand throughout the growth period. In view of the actual situation of water shortage in these areas, to monitor the water status of potato plants timely and accurately and thus precisely control the irrigation are of significance for water-saving management of potatoes. Hyperspectral remote sensing has unique advantages in diagnosing crop water stress. In this paper, the canopy spectral reflectance and plant water content were measured under five irrigation treatments. The spectral parameters that respond to plant water content were selected, and a hyperspectral water diagnosis model for leaf water content (LWC) and aboveground water content (AGWC) of potato plants was established. It was found that potato tuber yield was the highest during the entire growth period under sufficient irrigation, and the plant water content showed a downward trend as the degree of drought intensified. The peak hyperspectral reflectance of potato plant canopies appeared in the red wavelength, where the reflectance varied significantly under different water treatments and decreased with decreasing irrigation. Six models with sensitive bands, first-order derivatives, and moisture spectral indices were established to monitor water content of potato plants. The R2 values of partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models are 0.8418, 0.9020, and 0.8926, respectively, between LWC and hyperspectral data; and 0.8003, 0.8167, and 0.8671, respectively, between the AGWC and hyperspectral data. These six models can all predict the water content of potato plants, but SVM is the best model for predicting LWC of potato plants. These results are of great significance for guiding precision irrigation of potato plants at different growth stages.