In order to efficiently detect total nitrogen content in Korla fragrant pear leaves, near-infrared spectroscopy technology was utilized to develop a detection model. The collected spectra underwent various preprocessing techniques including first-order derivative, second-order derivative, Savitzky–Golay + second-order derivative, multivariate scattering correction, multivariate scattering correction + first-order derivative, and standard normal variable transformation + second-order derivative. A competitive adaptive reweighted sampling algorithm was employed to extract characteristic wavelengths, and a prediction model for the total nitrogen content of fragrant pear leaves was established by combining the random forest algorithm, genetic algorithm-based random forest algorithm, radial basis neural network algorithm, and extreme learning machine algorithm. The study found that spectral preprocessing of SNV + SD along with the radial basis neural network algorithm yielded better predictions for total nitrogen content of fragrant pear leaves. The validation set results showed an R² of 0.8547, RMSE of 0.291%, and RPD of 2.699. Therefore, the SNV + SD + CARS + RBF algorithm combination model proved to offer optimal comprehensive performance in predicting the total nitrogen content of fragrant pear leaves.

In the context of cutting grass in orchards, the practice of leaving cut weeds in the orchard rows hinders the decomposition of the weeds and the absorption of nutrients by the fruit trees. To address this issue, a grass-cutting machine with an integrated sweeping disc was designed to remove weeds from orchard rows and sweep them to the roots of the trees to promote their absorption of nutrients. A coupled simulation platform was established using multi-body dynamics ADAMS and the discrete element method EDEM. The weed-shedding and sweeping device was dynamically analyzed through an ADAMS–EDEM collaborative simulation that enabled the use of a second-order regression orthogonal rotation experiment and response surface methodology. The optimal parameters for the cutting tools, cutter shaft speed, and the number of cutting tools included 23 cutting tools arranged in a single helical pattern for the cutting device, a cutter shaft speed of 728 rpm, and claw-shaped blades as the cutting tools. A prototype machine was built based on the optimized parameters and tested in the field. The results indicated that, when there were 250 m² of weeds, the cutting rate reached 92.96%. The machine was highly maneuverable, and the average remaining weed height in the orchard was 110 mm, which met the national standards and local agricultural requirements. The new orchard grass-cutting and sweeping device meets the technical demands of orchard grass operations in the Xinjiang region of China.

To investigate the causes of flow fluctuations in a worm-type distributor during the fertilization process, this study employed the discrete element method to simulate the fertilization process. The analysis focused on the influences of force chain evolution on particle flow fluctuations and the effects of the rotational speed. The results indicate that the flow fluctuations in the worm-type distributor are not solely attributed to its helical structure but are closely associated with the evolution of force chains within the particle systems. Furthermore, a one-to-one correspondence between the flow fluctuations and force-chain evolution exists. The rotational speed was found to exert significant influences on the axial and circumferential distributions of the force chains. As the rotational speed increases, the coefficient of variation (CV) of the axial distribution of the force chains gradually decreases. Meanwhile, the uniformity index (UI) of the circumferential distribution of the force chains initially increases and then decreases. In the context of a worm-type distributor, the axial distribution of force chains emerges as the dominant influencing factor for flow fluctuations. For the specific worm-type distributor model chosen in this study, the minimum flow fluctuations were observed at a rotational speed of 80 rpm. These research findings offer valuable insights into understanding the flow fluctuations of worm-type distributors and provide references for further exploration in this field.

The physicochemical components of rice such as starch, protein, fat and water have significant influence on its nutritional value, and the drying process can easily cause changes in these components. In this paper, the effect of technical parameters on the nutritional quality of rice during hot-air drying was studied, and a control method of the rice-drying process based on effective accumulated temperature was proposed to ensure the drying quality and improve the drying efficiency. Through thin-layer drying experiments, hot-air temperature (T), humidity (RH), initial moisture content (MC), wind speed (V) and tempering ratio (TR) were selected as control factors, and the central composite design was adopted to optimize the experimental scheme. The relationship between each factor and nutrient quality was revealed through response surface analysis, and the regression model and process optimization parameters were established. The results show that the optimization parameters are as follows: hot-air temperature, 48.87 °C; humidity, 30.12%; initial moisture content, 21.31%; wind speed, 0.62 m/s; tempering ratio, 2.87; the optimized total drying time is 4.23 h; the effective accumulated temperature is 214.44 °C·h. The contents of protein, fat, amylose and amylopectin were 8.47 g/100 g, 1.97 g/100 g, 15.33 g/100 g and 60.50 g/100 g, respectively. The relative error of the verification test was 4.17%. The optimized process can effectively maintain the nutritional quality of rice and improve drying efficiency. This paper provides a new way to deeply explore the mechanism of rice quality change, and the established process reference chart provides a scientific basis for actual drying operations and the development of an intelligent control system.

Considering the pear in the arid region as the research object, single-factor testing and water–fertilizer coupling testing were conducted. The response of pear tree growth to water, nitrogen, and phosphorus was explored and provided a theoretical basis for efficient water and fertilizer management. Among them, the single-factor test set water, nitrogen, and phosphorus as the three factors, and five levels were set. Screening out W3, W4, N3, N4, P3, and P4 promoted plant nutrient uptake and fruit quality. Eight treatments were set up in the water and fertilizer coupling test: Treatment 1 (T1, W3N3P3), Treatment 2 (T2, W3N3P4), Treatment 3 (T3, W3N4P3), Treatment 4 (T4, W3N4P4), Treatment 5 (T5, W4N3P3), Treatment 6 (T6, W4N3P4), Treatment 7 (T7, W4N4P3), and Treatment 8 (T8, W4N4P4). The results showed that the leaf area index of the T1, T2, T3, and T4 treatments was significantly higher than that of the other treatments at maturity. The yield, single fruit weight, and primary fruit rate were the highest under T3 treatment. The gray correlation degree analysis of fruit quality showed that the T3 treatment had the highest degree of correlation and ranking of each fruit quality index, indicating that the T3 treatment had the highest fruit quality. The yield model showed that irrigation with 6510.06 m³ hm⁻², nitrogen fertilizer with 337.5 kg N hm⁻², and phosphate fertilizer with 262.5 kg P hm⁻² had the best yield. A detailed investigation of pear tree growth and fruit quality showed that the T3 treatment had the best fruit growth and development performance, and the pear fruit quality was the best.

A multi-duct orchard sprayer is designed in this paper to address the problem of low droplet coverage during plant protection in walnut orchards in South Xinjiang, China. This spray comprises a base frame, a pesticide tank, an air delivery system, a copying base frame and a drive system. Effects of the sprayer structure on the flow field were simulated using computational fluid dynamics. Operation parameters were optimised using the response surface analysis and validated using a field test. The results demonstrated that five jet tubes in the simulation design could fulfil the design requirements of GB/T 32250.3-2022. The upper, middle and lower droplet coverages were 74.92%, 90.01% and 69.9%, respectively, when the sprayer’s advancing speed, the angle of jet tubes and the nozzle diameter were 0.53 m/s, 70.27° and 0.81 mm, respectively. The machine can effectively enhance the spraying efficiency and droplet coverage of each canopy during plant protection operations in the new walnut orchards of South Xinjiang, China, and provide references for designing and optimising fruit tree spraying machinery.