Fertilizers application are widely used to get a higher yield in agricultural fields. Nutrient management can be improved by cultivating leguminous species in order to obtain a better understanding of the mechanisms that increase the amount of available phosphorus (P) and potassium (K) through fertilizer treatments. A pot experiment was conducted to identify the leguminous species (i.e., chickpea and pea) under various fertilizer treatments. Experimental design is as follows: T0 (control: no fertilizer was applied), T1: P applied at the level of (90 kg ha⁻¹), T2: (K applied at the level of 90 kg ha⁻¹), and T3: (PK applied both at 90 kg ha⁻¹). All fertilizer treatments significantly (p < 0.05) improved the nutrient accumulation abilities and enzymes activities. The T3 treatment showed highest N uptake in chickpea was 37.0%, compared to T0. While T3 developed greater N uptake in pea by 151.4% than the control. However, T3 treatment also increased microbial biomass phosphorus in both species i.e., 95.7% and 81.5% in chickpeas and peas, respectively, compared to T0 treatment. In chickpeas, T1 treatment stimulated NAGase activities by 52.4%, and T2 developed URase activities by 50.1% higher than control. In contrast, T3 treatment enhanced both BGase and Phase enzyme activities, i.e., 55.8% and 33.9%, respectively, compared to the T0 treatment. Only the T3 treatment improved the activities of enzymes in the pea species (i.e., BGase was 149.7%, URase was 111.9%, Phase was 81.1%, and NAGase was 70.0%) compared to the control. Therefore, adding combined P and K fertilizer applications to the soil can increase the activity of enzymes in both legume species, and changes in microbial biomass P and soil nutrient availability make it easier for plants to uptake the nutrients.

A new prototype of fluidic sprinkler is proposed in this paper. To determine the discharge coefficient (A), pattern radius (B), sprinkler rotation speed (C), droplet diameter distribution (D), and the radial application pattern (E), several sprinkler heads were specially fabricated to carry out the experiments. After systematic experiments were conducted with the 10PXH fluidic sprinkler, the results showed that the inner contraction angle influenced A, B, C, and E, and the geometrical parameters of offset length and working area length influenced E. The operating pressure influenced B, C, D, and E. The reductions in the radius exactly corresponded to the reduction of the discharge coefficient. As the inner contraction angle increased from 10° to 70°, the pattern radius at 250 kPa decreased from 10.7 to 8.4 m and the discharge coefficient decreased from 0.9 to 0.67. The time per rotation and the droplet diameter distribution were analyzed, respectively. The radial application pattern at 250 kPa for all nozzles was established. For the square configurations, the smaller the lateral spacing, the higher the uniformity.

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R²) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season.

The Crop Water Stress Index (CWSI) is a useful tool for evaluating irrigation scheduling and achieving water conservation and crop yield goals. This study examined the CWSI under different water stress conditions for the scheduling of wheat crop irrigation and developed indices using the leaf canopy temperature in Faisalabad, Pakistan. The experiments were conducted using a randomized, complete block design and four irrigation treatments with deficit levels of D_0%, D_20%, and D_40% from the field capacity (FC) and D_100% (100% deficit level). The CWSI was determined at pre-heading and post-heading stages through the lower baseline (fully watered crop) and upper limit (maximum stress). These baselines were computed using the air temperature and canopy temperature of plant leaves and the vapor pressure deficit (VPD). The CWSI for each irrigation treatment was calculated and the average seasonal CWSI value for the whole season was used to develop the empirical relationships for scheduling irrigation. The relationships between the air canopy temperatures and the VPD resulted in slope (x) = −0.735 and interception (c) = −0.8731 as well as x = −0.5143 and c = −1.273 at the pre- and post-heading stages, respectively. The values of the CWSI for the treatment at deficit levels of D_0%, D_20%, D_40%, and D_100% were found to be 0.08, 0.61, 0.20, and 0.64, respectively. The CWSI values developed in this study can be effectively used to promote better the monitoring of irrigated wheat crops in the region.