Rapid and accurate identification of maize architecture characteristics is important for understanding both yield potential and crop breeding experiments. Most canopy architecture indicators cannot fully reflect the vertical leaf distribution in field environments. We conducted field experiments on sixty maize cultivars under four planting densities at three different sites, and herein introduce two novel indicators, “kurtosis and skewness,” based on the manually measured leaf area index (LAI) of maize at five different canopy heights. Then, we constructed the LAI, plant height (PH), kurtosis, and skewness estimation models based on unmanned aerial vehicle multispectral, RGB, and laser detecting and ranging data, and further assessed the canopy architecture and estimated yield. The results showed that the fitting coefficient of determination (R²) of cumulative LAI values reached above 0.97, and the R² of the four indicators’ estimation models based on multi-source data were all above 0.79. A high LAI, along with greater kurtosis and skewness, optimal PH levels, and strong stay-green ability, are essential characteristics of high-yield maize. Moreover, the four indicators demonstrated high accuracy in estimating yield, with the R² values based on measured canopy indicators at the four planting densities being 0.792, 0.779, 0.796, and 0.865, respectively. Similarly, the R² values for estimated yield based on estimated canopy indicators were 0.636, 0.688, 0.716, and 0.775, respectively. These findings provide novel insight into maize architecture characteristics that have potential application prospects for efficient estimation of maize yield and the breeding of ideal canopy architecture.

The lengths of leaves, leaf sheaths, and internodes are the main factors affecting individual plant types. An ideotype is a basis for developing a high-yielding population structure. Water and nitrogen (N) fertilizer can directly affect the growth of a plant’s organs. To evaluate the effects of irrigation and nitrogen application on the length and growth of wheat leaves, leaf sheaths, and internodes, we carried out a 5 year field experiment in the high yield wheat fields of the North China Plain. Five treatments (T₂–T₆) were applied, and irrigation was carried out in springtime at the appearance of the second leaf (T₂), the third leaf (T₃), the fourth leaf (T₄), the fifth leaf (T₅) and the sixth leaf (T₆). The results showed that the irrigation and N topdressing periods had different effects on the leaves, leaf sheaths, and internodes. The lengths of the upper three leaves gradually increased with the progression of the irrigation and N topdressing. The increases in the lengths of the leaf sheath were similar and followed the irrigation and N topdressing pattern at four stages of leaves in the spring: n-1, n-2, n-3 and n-4. The most effective growth of the internodes was achieved by irrigation and N topdressing at the n + 2 and n + 3 stages. The vertical spacing among the upper three leaves increased with irrigation and N topdressing at the appearance of the top second (or flag) leaf. Differences in temperature and precipitation over the years either weakened or enhanced the differences in the plants’ organ lengths with the different treatments. However, the orders of treatments did not alter organ length in different years. Earlier irrigation and N topdressing treatments (T₂, T₃, and T₄) showed an inhibitory effect on the leaves and leaf sheaths during the early growth stage. The inhibitory effect was more evident in the later-emerged leaves and leaf sheaths than in those that emerged earlier. However, irrigation and N fertilization increased the final length of the organs by improving the growth rate during the rapid incremental phase (RIP) and the slow incremental phase (SIP). Although the most significant extensions of the lengths of leaves, leaf sheaths, and internodes were achieved by irrigation and N topdressing before the organs entered the RIP, the specific growth stages were different among the three organs. These results can provide a reference for directly regulating the development of wheat organs and constructing an ideotype.

Wheat canopy chlorophyll content (CSPAD) is an important physiological parameter characterizing the photosynthetic capacity and nutritional status of crops. Precision agricultural technologies are widely used for non-destructive monitoring of wheat SPAD, but the SPAD inversion models have limitations due to the incorporation of many principal components besides spectral parameters. In the current study, combined with the SPAD values measured by a handheld instrument, an effective approach for estimating CSPAD from unmanned aerial vehicle (UAV) hyperspectral data is proposed. A fusion modeling scheme based on spectral parameters was constructed by extracting (1) the traditional vegetation index (VI), (2) the sensitive-band index (2D-COSI) screened based on two-dimensional correlation spectroscopy (2D-COS), and (3) the geometric-angle index (SPADSI) constructed by combining the SPA and the PROSAIL model. Finally, the CSPAD estimation model was developed by using Gaussian Process Regression (GPR) and Support Vector Machine Regression (SVM), and their accuracy comparison and feature importance analysis were conducted at different growth stages. We found that the model integrating three types of spectral parameters performed better as compared to the model with a single type of parameter. Further, the GPR model had the highest estimation efficiency at 20 days after the anthesis stage (R² = 0.90, RMSE = 5.95, MAE = 4.47) as compared to the SVM model and other growth stages. This study provides innovative insights and technical support based on a CSPAD estimation framework integrating multiple types of spectral characteristics for the rapid and non-destructive monitoring of wheat CSPAD and for overall sustainability in farmland management.