Pine wilt disease (PWD) caused by pine wood nematode (PWN, Bursaphelenchus xylophilus) originated in North America and has since spread to Asia and Europe. PWN is currently a quarantine object in 52 countries. In recent years, pine wilt disease has caused considerable economic losses to the pine forest production industry in China, as it is difficult to control. Thus, one of the key strategies for controlling pine wilt disease is to identify epidemic points as early as possible. The use of hyperspectral cameras mounted on drones is expected to enable PWD monitoring over large areas of forest, and hyperspectral images can reflect different stages of PWD. The trend of applying hyperspectral techniques to the monitoring of pine wilt disease is analyzed, and the corresponding strategies to address the existing technical problems are proposed, such as data collection of early warning stages, needs of using unmanned aerial vehicles (UAVs), and establishment of models after preprocessing.

The air-suction precision seed-metering device is prone to the instability of the seed adsorption state, which arises from blockage of the suction hole and leads to uneven seeding. This paper analyzed and determined key structural parameters of the seed-metering plate, then established an adsorption mechanics model of the seed during the migration process and designed the key structure of the air-suction seed-metering device with the aim of improving the uniformity of high-speed direct seeding of vegetables. Furthermore, we used the DEM-CFD coupling method to analyze the influence of the law of seeds on the change of the flow field with different hole types. Results showed that the turbulent kinetic energy (202.65 m²∙s⁻²) and the coupling force to the seeds (0.029 N) of the B-type hole are the largest, which is the best fluid domain structure for the suction hole of the seed-metering plate. Moreover, we used Adams to analyze the meshing process between the knock-out wheel and the seed-metering plate, affirming the rationality of the knock-out wheel design. Finally, in order to improve the working efficiency of the seed-metering device, we performed one-factor and response surface experiments of seeding performance using the air-suction seed-metering device designed with the optimized structure as the experimental object. Analysis of the influence of weights across each factor on the experimental performance evaluation indicators revealed an optimal combination of seeding performance parameters in the air-suction seed-metering device, namely a seed-throwing angle of 13°, a working speed of 14.5 km/h, and negative pressure of 3.1 kPa. Results from verification experiments revealed the corresponding experimental indicators, namely qualified, multiple, and missing indexes of 95.9, 1.2%, and 2.9%, respectively.

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R² > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

Tea is a popular beverage worldwide and also has great medical value. A fundamental understanding of tea shoot growth and a precision picking model should be established to realize mechanized picking of tea shoots with a small product loss. Accordingly, the terminal bud length (L_bud), tea stem length (L_stem), terminal bud angle (α_bud), tea stem angle (α_stem), and growth time (t) were considered as the key growth parameters; the sum of the vertical lengths of the terminal bud and stem (ξ), the picking radius (r), and the vertical length of the stem (Z_stem) were considered as the picking indexes of the tea shoots. The variations in growth parameters with time were investigated using a 3-D coordinate instrument, and the relationships between the growth parameters and the picking indexes were established using an artificial neural network (ANN). The results indicated that the tea growth cycles for periods P₁, P₂, P₃, P₄, P₅, and P₆ were 14, 7, 6, 4, 4, and 6 d, respectively. A growth cycle diagram of the tea growth was established. Moreover, a 5-2-12-3 ANN model was developed. The best prediction of ξ, r, and Z_stem was found with 16 training epochs. The MSE value was 0.0923 × 10⁻⁴, and the R values for the training, test, and validation data were 0.99976, 0.99871, and 0.99857, respectively, indicating that the established ANN model demonstrates excellent performance in predicting the picking indexes of tea shoots.

The performance evaluation and optimization of an energy conversion system design of an energy intensive drying system applied the method of combining exergy and economy is a theme of global concern. In this study, a gas-type industrial drying system of black tea with a capacity of 100 kg/h is used to investigate the exergetic and economic performance through the exergy and exergoeconomic methodology. The result shows that the drying rate of tea varies from the maximum value of 3.48 g_water/g_{dry matter} h to the minimum 0.18 g_water/g_{dry matter} h. The highest exergy destruction rate is found for the drying chamber (74.92 kW), followed by the combustion chamber (20.42 kW) in the initial drying system, and 51.83 kW and 21.15 kW in the redrying system. Similarly, the highest cost of the exergy destruction rate is found for the drying chamber (18.497 USD/h), followed by the combustion chamber (5.041 USD/h) in the initial drying system, and 12.796 USD/h and 5.222 USD/h in the redrying system. Furthermore, we analyzed the unit exergy rate consumed and the unit exergy cost of water removal in different drying sections of the drying system, and determined the optimal ordering of each component. These results mentioned above indicate that, whether from an energy or economic perspective, the component improvements should prioritize the drying chamber. Accordingly, minimizing exergy destruction and the cost of the exergy destruction rate can be considered as a strategy for improving the performance of energy and economy. Overall, the main results provide a more intuitive judgment for system improvement and optimization, and the exergy and exergoeconomic methodology can be commended as a method for agricultural product industrial drying from the perspective of exergoeconomics.

The ventilation resistance of air flow through rice grain layers is one of the key parameters affecting drying uniformity as well as the energy consumption of the drying process. In order to reveal the variation of characteristics of the ventilation resistance with paddy grain moisture content, the air velocity and the bed layer depth are needed. A second order model was fitted to pressure drop using the response surface methodology and the results are compared with those of the Ergun model. The results showed that the pressure drop increases with the increase of paddy grain moisture content, air velocity and the bed layer depth, and the interactions between the air velocity and the bed layer depth have the most significant influence on the pressure drop. Moreover, a second-order polynomial pressure drop model based on RSM was established and compared with the Ergun model. The results showed that the pressure drop model established by RSM is similar to that of the Ergun model.