The accurate detection of pineapples of different maturity levels in a complex field environment is the key step to achieving the early yield estimation and mechanized picking of pineapple. This study proposes a target detection model based on the improved YOLOv7 to achieve the accurate detection and maturity classification of pineapples in the field. First, the attention mechanism SimAM is inserted into the structure of the original YOLOv7 network to improve the feature extraction ability of the model. Then, the max-pooling convolution (MPConv) structure is improved to reduce the feature loss in the downsampling process. Finally, the non-maximum suppression (NMS) algorithm is replaced by the soft-NMS algorithm, with a better effect at improving the detection effect when pineapple is in occlusion or overlaps. According to the test, the mean average precision (mAP) and recall of the model proposed in this paper are 95.82% and 89.83%, which are 2.71% and 3.41% higher than those of the original YOLOv7, respectively. The maturity classification accuracy of the model and the detection performance under six different field scenarios were analyzed quantitatively. This method provides an effective scheme for the vision system of the field pineapple picking robot.

In this paper, a control method was designed based on particle swarm optimization (PSO)–fuzzy PID (PSO-Fuzzy PID). In order to address the issues of significant nonlinearity and extensive hysteresis in the vapor temperature of litchi hot vapor preservation treatment equipment, as well as the inadequacy of traditional control schemes, a PSO-Fuzzy PID controller was developed through the integration of particle swarm optimization and fuzzy logic algorithms. This controller incorporates a scale factor and quantization factor, building upon the foundation of the Fuzzy PID controller. Additionally, a transfer function model was established for both the heating and disturbance links, utilizing a combination of test data modeling methods and relevant theory. Thirdly, a system simulation model was built to compare and verify PID, Fuzzy PID, and PSO-Fuzzy PID control methods, which were carried out on the MATLAB/Simulink simulation platform. Finally, the litchi load test was conducted to further verify the control performance of the PSO-Fuzzy PID method, where the litchi variety was “Jinggang Hongnuo”. The simulation results showed that the overshoot of the optimized control system was reduced by 93.5% and 42.9% compared with the traditional PID control and Fuzzy PID, and the regulation time was shortened by 46% and 31%, which indicates a better control effect. The test results showed that PSO-Fuzzy PID showed better system stability and accuracy than the fuzzy PID control method in different processing temperature and processing time for litchi’s hot vapor treatment. The maximum temperature difference decreased by 0.56 °C on average and the interference adjustment time was reduced by 7.5 s on average. PSO-Fuzzy PID has a good control effect on the temperature of litchi hot vapor treatment, which can provide a reference for model building and temperature control of other similar fruit and vegetable heat treatment equipment.

Soil environmental monitoring is crucial for ensuring the sustainability and productivity of agriculture. This study aims to develop a wireless soil monitoring system that utilizes Narrowband Internet of Things (NB-IoT), solar energy, and Global Positioning System (GPS) technologies to address the issues of high labor demand, high costs, and delayed feedback in traditional soil monitoring methods. This system can collect soil temperature, humidity, and meteorological data in real time, transmit them to a cloud platform for analysis and visualization, and predict future soil data. It employs multiple learning algorithms to build models and uses the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization. Field stability experiments were conducted on the system, and the performance of the soil moisture prediction model was evaluated. During the 84-day stability experiment, the system operated stably for 80 days, with a data collection success rate of 95.87%. In the performance evaluation of the soil moisture model, the GBDT model achieved a coefficient of determination (R²) of 0.9838 on the validation set and a root-mean-square error (RMSE) of 0.0013, with an RMSE of 0.0013 on the test set as well. The experimental results demonstrate that the system is stable and reliable, featuring low power consumption, wide coverage, and high accuracy. It can effectively predict soil moisture, providing timely and accurate support for irrigation and farming decisions.

In the context of agricultural unmanned helicopters, the complex wind disturbances over crop fields and structural perturbations due to variations in pesticide container weights present substantial challenges to flight safety. To address these issues, this paper proposes an innovative fuzzy extended state observer-based sliding mode control (FESO-SMC) methodology aimed at enhancing the aircraft’s resilience against such disturbances. Initially, this study adopts a state expansion strategy to integrate both wind and structural disturbances into a comprehensive disturbance model applicable to the agricultural unmanned helicopter. Following this, a sliding mode control law is formulated with consideration for unknown total disturbances, employing specific sliding mode functions alongside exponential reaching laws. An extended state observer is simultaneously implemented within the sliding mode control framework to estimate and mitigate these disturbances, thereby augmenting the disturbance rejection capabilities of the flight control system. Additionally, the integration of fuzzy logic facilitates adaptive parameter adjustment for the extended state observer, leading to more accurate disturbance estimation. Consequently, a trajectory tracking control system tailored specifically for the agricultural unmanned helicopter has been developed, and its performance was evaluated through simulation experiments. The results indicate that, under certain disturbances, the attitude control error of the FESO-SMC controller is reduced to one-fifth that of traditional sliding mode controllers, while position control accuracy is enhanced more than twofold, thus demonstrating that the proposed FESO-SMC controller not only exhibits superior anti-disturbance capability and robustness but also achieves higher tracking accuracy compared to conventional sliding mode controller.

Creep and relaxation characteristics of stem for rice seedlings grown in plastic cell tray were studied by static tensile testing, in order to determine the relationship between characteristic parameters (rheological model parameters, stress components and strain components) and test levels (stress levels and strain levels). Rice seedling stem specimen used in the test was 40 mm in length. And the applied test values for the creep and relaxation test ranged from 1.0-3.0 MPa and 1.5%-3.5%, respectively, each for 5 levels. The results indicated that elastic modulus in the creep and relaxation model was not affected by test levels. However, except that viscosity coefficient eta(kv), was a constant and eta(m1) decreased with the increase of test levels, other viscosity coefficient and rheological time nonlinearly increased as the test levels increased. And strain components in the creep model and stress components in the relaxation model significantly increased as the test levels increased.