Estimating potassium in potato plants based on multispectral images acquired from unmanned aerial vehicles.

Plant potassium content (PKC) is a crucial indicator of crop potassium nutrient status and is vital in making informed fertilization decisions in the field. This study aims to enhance the accuracy of PKC estimation during key potato growth stages by using vegetation indices (VIs) and spatial structure features derived from UAV-based multispectral sensors. Specifically, the fraction of vegetation coverage (FVC), gray-level co-occurrence matrix texture, and multispectral VIs were extracted from multispectral images acquired at the potato tuber formation, tuber growth, and starch accumulation stages.

[Estimating canopy water content in wheat based on new vegetation water index].

Moisture content is an important indicator for crop water stress condition, timely and effective monitoring crop water content is of great significance for evaluate crop water deficit balance and guide agriculture irrigation. In order to improve the saturated problems of different forms of typical NDWI (Normalized Different Water Index), we tried to introduce EVI (Enhanced Vegetation Index) to build new vegetation water indices (NDWI#) to estimate crop water content. Firstly, PROSAIL model was used to study the saturation sensitivity of NDWI, and NDWI# to canopy water content and LAI (Leaf Area Index).

Assessment of the AquaCrop model for use in simulation of irrigated winter wheat canopy cover, biomass, and grain yield in the North China Plain.

Improving winter wheat water use efficiency in the North China Plain (NCP), China is essential in light of current irrigation water shortages. In this study, the AquaCrop model was used to calibrate, and validate winter wheat crop performance under various planting dates and irrigation application rates. All experiments were conducted at the Xiaotangshan experimental site in Beijing, China, during seasons of 2008/2009, 2009/2010, 2010/2011 and 2011/2012.

[Extraction and analysis of solar-induced chlorophyll fluorescence of wheat with ground-based hyperspectral imaging system].

Dataset simulated with FluorMOD and images of wheat in heading stage taken by a ground-based hyperspectral imaging system with 3.3 nm spectral resolution and 0. 71-0.

[Band depth analysis and partial least square regression based winter wheat biomass estimation using hyperspectral measurements].

The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently.