Introduction: Monitoring nitrogen nutrition indices is crucial for assessing current wheat growth conditions and guiding nitrogen fertilizer application. Methods: To estimate the wheat nitrogen nutrition index (NNI) and explore the effects of planting density and nitrogen application rates on NNI, this study employed UAVs to capture multispectral canopy imagery of wheat at key growth stages (tillering, jointing, booting, and filling) under varying planting densities and nitrogen application rates. Vegetation indices were selected using Pearson correlation and feature importance analysis. A Bayesian optimized random forest model was constructed to estimate the NNI. Results: Experimental results indicate that vegetation indices DVI, MDD, NGI, MEVI, NDVI, EVI, and ENDVI exhibit strong resistance to interference, enabling the construction of highly robust models. The NNI estimation model developed under nitrogen application level N2 (210 kg/hm ² ) demonstrated optimal performance, with R ² and RMSE values of 0.785 and 0.137, respectively. The NNI estimation model constructed at planting density P1 (1 million plants/hm ² ) was optimal, with R ² and RMSE of 0.716 and 0.158, respectively. It was also found that NNI generally exhibited an initial increase followed by a decrease as planting density increased. Discussion: The research findings systematically reveal the patterns of planting density and nitrogen application levels affecting wheat NNI. The constructed NNI estimation model plays a crucial role in assessing wheat growth status and also provides reference for rationally determining planting density and nitrogen application levels for spring wheat.

To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus.

The Auricularia auricula harvester has low harvesting productivity, low qualification rate, and high breakage rate, and the lack of research on key operating parameters has become a bottleneck restricting the large-scale development industry. In view of the difficulty of industrialization and promotion of Auricularia auricula harvesting equipment, this paper combines the research on the physical properties of Auricularia auricula, adopts a quadratic orthogonal rotation combination design test and response surface analysis method to carry out the optimization and test of the key parameters of the harvesting equipment of Auricularia auricula, including the speed of the tray fungus stick (A), the frequency of the harvesting knife (B), and the distance of the harvesting knife (C). Design-Expert software was used to analyse the data and investigate the influence of each parameter and its interaction on the harvesting productivity, qualified rate, and breakage rate. The test results show that the factors affecting the harvesting productivity are in order: (A) > (B) > (C); the factors affecting the harvest qualification rate are in order: (C) > (B) > (A); and the factors affecting the harvesting damage rate are in order: (C) > (B) > (A). The multi-objective optimization results show that the optimal parameter combination of the key parameters of the Auricularia auricula harvesting equipment operation is that the tray speed is 308.17 r/min, the harvesting knife frequency is 10.78 Hz, the spacing between the harvesting knives is 84.67 mm, the productivity is 363 bags/h, the pass rate is 94.26%, and the breakage rate is 2.40%. The field production application under this optimized parameter combination shows that the productivity of the Auricularia auricula mechanized harvester is 346 bars/h, the qualified rate is 91.43%, and the breakage rate is 3.19%. This study can provide a reference for improving the quality of Auricularia auricula harvesting equipment.

Drying is a key operation for extending the shelf life of agricultural products and maintaining food quality, and its efficiency and product outcomes are governed by coupled heat and mass transfer. This review critically summarizes the mechanisms, technological characteristics, research methods and application prospects of agricultural-product drying from a heat- and mass-transfer perspective. The moisture-migration pathways, including surface evaporation, internal diffusion, capillary flow, vapor diffusion and bound-water desorption, are first discussed within a porous-medium framework. Governing equations based on Fourier’s law, Fick’s law, energy conservation and convective transfer are then introduced to clarify the theoretical basis of drying models. Typical convective, radiative, conductive and combined drying technologies are compared in terms of transfer mechanisms, drying efficiency, energy consumption, product-quality retention, carbon-footprint potential and industrial feasibility. Particular attention is given to the effects of drying-induced heat and mass transfer on color, texture, rehydration, bioactive compounds, antioxidant activity and microstructure. Current theoretical, experimental, numerical and data-driven research methods are further reviewed, and the limitations of existing studies are identified, including simplified homogeneous assumptions, insufficient model validation, limited quantitative comparison and weak scale-up applicability. Finally, future directions are proposed, including refined multi-scale and multi-field coupled models, advanced in situ characterization, multi-energy-field synergistic drying, digital twins, predictive modeling and multi-objective intelligent optimization. This review aims to provide a more mechanism-based and application-oriented reference for developing efficient, low-carbon and quality-preserving drying systems for agricultural products.