To address the issue of reduced grain plumpness and yield caused by high soil temperatures under plastic film in semi-humid drought-prone areas, this study proposes a dual mulching approach using transparent plastic film and straw (TM+ST) under flat farming. This study analysed the regulatory mechanisms of TM+ST on grain filling characteristics and leaf photosynthetic physiology, aiming to provide theoretical and technical support for stable and high yields in dryland maize production

A field experiment was conducted in Yangling, Shaanxi Province (semi-humid drought-prone area, with an average annual precipitation of 550 mm, accounting for 60% from July to September) from 2021 to 2022. Three mulching treatments were set up with no mulching as the control (CK): transparent plastic film mulching with flat cropping cultivation (TM), black plastic film mulching with flat cropping cultivation (BM), and a flat plot of transparent film mulching with whole maize stalks (TM+ST). The effects of different mulching measures on post-anthesis leaf area index (LAI), SPAD value, net photosynthetic rate (P_n), stomatal conductance (G_s), transpiration rate (T_r), dry matter accumulation, dynamics of grain weight increment, grain filling characteristics, yield and yield components of spring maize were explored.

Compared with CK, mulching treatments significantly improved the green retention and photosynthetic production capacity of spring maize after anthesis, and improved the grain filling process and grain yield (an increase of 16.5%-48.9%). Compared with the single mulching treatments (TM and BM), TM+ST significantly improved the green retention of leaves, enhanced P_n, G_s, and T_r, and promoted dry matter accumulation after anthesis (an increase of 10.9%-12.6%). Moreover, TM+ST increased the grain weight at the maximum grain filling rate, maintained the highest grain filling rate at the gradual-growing period, prolonged the duration of grain filling at the fast-growing period, and prolonged the active stage of grain filling at slow-growing period. Ultimately, TM+ST treatment increased grain yield (V.S. TM and BM increased by 17.0%-17.2%) by enhancing "source" supply and optimizing grain filling process.

Dual mulching of transparent-plastic film and straw (TM+ST) could improve the green retention and photosynthetic production capacity of leaves after anthesis, optimize the grain filling process, and ultimately drive the maximization of grain weight to improve the yield of maize.

Crop nitrogen nutrition status is a key indicator to characterize the green degree and health status of maize canopy. In order to compare the accuracy of single spectral index model and texture information fusion model in maize nitrogen nutrition estimation model, this investigated the accuracy and reliability of maize nitrogen nutrition estimation model based on UAV multispectral information and texture information fusion.

Matrice-300 RTK multi-rotor aircraft equipped with MS600 Pro multi-spectral sensor was used to obtain multi-spectral images of maize tasseling-silking stages under six nitrogen levels in two years. By extracting vegetation index and texture features, the correlation between vegetation index, single texture feature, combined texture index and fusion information of vegetation index and texture index, was comprehensively analyzed. The vegetation index, normalized difference texture index (NDTI) and their combined parameters with the largest amount of information were selected. Four nitrogen nutrition parameters of maize leaf nitrogen content (LNC), plant nitrogen content (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) were compared and estimated by multiple stepwise regression (MSR), random forest (RF), support vector machine (SVM), and grey wolf optimized convolutional neural network (GWO-CNN).

(1) There were differences in the original spectral reflectance of maize under different nitrogen treatments, and the differences in the red band R (660 nm), blue band B (450 nm) and near-infrared band NIR (840 nm) were significant. (2) The vegetation indices (EVI, GARI, REOSAVI, SIPI, and MCARI), single texture features (var₄₅₀, var₆₆₀, mean₈₄₀, dis₇₂₀, and hom₈₄₀) and combined texture index NDTI extracted from UAV multispectral images could be used for LNC, PNC, LNA and PNA estimation of maize in VT-R1 stage. The GWO-CNN model based on vegetation index had better estimation effect on LNC, PNC, LNA and PNA than single texture feature and texture index model, and its R² were 0.831, 0.761, 0.826 and 0.770, respectively. (3) The accuracy of GWO-CNN model with vegetation index and texture index for LNC, PNC, LNA and PNA estimation was significantly higher than that of vegetation index and texture index, and its R² was 0.921, 0.901, 0.917 and 0.892, respectively, which was 9.77%, 15.54%, 9.92% and 13.68% higher than that of single spectral information optimal estimation model.

Fusion of multi-spectral vegetation index and texture index could effectively improve the estimation accuracy of maize nitrogen nutrition, and better evaluate the distribution of maize nitrogen distribution, which provided new ideas for precise maize nitrogen fertilizer management based on UAV platform at field scale.