Aiming at the problem of grain breakage during the harvesting of fresh corn, this paper theoretically analyzes the collision process between the ear picking device and the corn ear, and a flexible ear picking structure composed of flexible materials and buffer springs is determined. Combined with a new harvesting method that reverses the growth direction from top to bottom, a reverse flexible ear plucking device for fresh corn was designed. We used the ADAMS software to simulate the ear picking process of fresh corn, analyze the contact force between the rigid structure and flexible buffer structure under different picking claw speeds and stalk feeding speeds, and obtain the optimal parameter combination: the picking claw speed was 2 m/s, and the stalk feeding speed was 1 m/s. On the basis of the simulation, a reverse flexible fresh corn harvesting bench was built, and the optimal operating parameters were obtained from the test: the speed of ear picking claws was 2.11 m/s; the number of ear picking claws was four; the thickness of the flexible body was 4.52 mm; the stem feeding speed was 1.04 m/s; the corresponding grain breakage rate was 0.128%, which was far lower than the national standard (0.5%); and the ear impurity content was 0.3%, which was far lower than the national standard (2%). The results are consistent with the simulation results, proving that the model is reliable. This research achieved the harvest of fresh corn ears with a low grain damage rate, verified the possibility of reverse flexible ear picking, and provided a reference for the research and development of low-damage fresh corn harvesting machines.

Real-time knowledge of kernel breakage during corn harvesting plays a significant role in the adjustment of operational parameters of corn kernel harvesters. (1) Transfer learning by initializing the DenseNet121 network with pre-trained weights for training and validating a dataset of corn kernels was adopted. Additionally, the feature extraction capability of DenseNet121 was improved by incorporating the attention mechanism of a Convolutional Block Attention Module (CBAM) and a Feature Pyramid Network (FPN) structure. (2) The quality of intact and broken corn kernels and their pixels were found to be coupled, and a linear regression model was established using the least squares method. The results of the test showed that: (1) The MAP^b50 and MAP^m50 of the improved Mask Region-based Convolutional Neural Network (RCNN) model were 97.62% and 98.70%, in comparison to the original Mask Region-based Convolutional Neural Network (RCNN) model, which were improved by 0.34% and 0.37%, respectively; the backbone FLOPs and Params were 3.09 GMac and 9.31 M, and the feature extraction time was 206 ms; compared to the original backbone, these were reduced by 3.87 GMac and 17.32 M, respectively. The training of the obtained prediction weights for the detection of a picture of the corn kernel took 76 ms, so compared to the Mask RCNN model, it was reduced by 375 ms; based on the concept of transfer learning, the improved Mask RCNN model converged twice as quickly with the loss function using pre-training weights than the loss function without pre-training weights during training. (2) The coefficients of determination R² of the two models, when the regression models of the pixels and the quality of intact and broken corn kernels were analyzed, were 0.958 and 0.992, respectively. These findings indicate a strong correlation between the pixel characteristics and the quality of corn kernels. The improved Mask RCNN model was used to segment mask pixels to calculate the corn kernel breakage rate. The verified error between the machine vision and the real breakage rate ranged from −0.72% to 0.65%, and the detection time of the corn kernel breakage rate was only 76 ms, which could meet the requirements for real-time detection. According to the test results, the improved Mask RCNN method had the advantages of a fast detection speed and high accuracy, and can be used as a data basis for adjusting the operation parameters of corn kernel harvesters.

Due to the design of peanut harvesters and cleaners, peanut pods are often mixed with soil, gravel, peanut straw, and other impurities. To solve this problem, this study focused on designing a peanut pod cleaning device by integrating a negative pressure centrifugal fan, a hydrometric cleaning sieve, and a reversible long-mesh cleaning sieve. The relative motion of the peanut pod on the sieve was discussed, its stress analyzed, the design parameters of the sieve and fan determined, and the operation of the device was monitored by using sensors, which accurately recorded and adjusted the working parameters. Finally, the key parameters were tested, and the results showed that the design requirements were met at a vibration frequency of 5.5 Hz, a hydrometric cleaning sieve inclination of 15°, a reciprocating long mesh cleaning sieve inclination of 5°, and a fan speed of 1500 rev/min; the mean loss is 2.26%, and the mean impurity is 3.18%. The findings can be used to provide technical support and reference for the development of peanut pod cleaning devices.

The emission of particulate matter (PM) from agricultural activities, such as concentrated animal feeding, straw combustion, and mechanized harvest, is a hot issue in the sustainable development of agriculture, which has attracted more and more attention from government departments and researchers. However, the research on the transport of particulate matter in the agri-environment still lacks flexible and efficient measurement methods to obtain real-time and accurate spatial distribution data. The objective of our study is to produce a new intelligent platform for agri-environment atmospheric monitoring with high mobility, temporal and spatial resolution, and remote data transmission function to overcome the shortcomings of traditional atmospheric particulate matter monitoring stations, such as small particle size range, immovability, and high cost. Through the light scattering sensor, microcontroller, and wireless data transmission device assembled on the high-mobility drone, the platform could measure the mass concentration of PM2.5, PM10, and TSP at different spatial points in the agri-environment and transmit the measurement data to the receiving device on the ground through three modes: CLOUD, TCP, and UDP. We also developed monitoring software based on the Android platform, which could complete the connection of device and real-time monitoring of measurement data on the ground. Compared with stationary measurement devices, the biggest advantage of our mobile monitoring system is that it has the ability to measure the concentration of TSP and the vertical distribution of PM, which is very important for the research of agricultural environmental particulate matter emission characteristics. After the sensor and communication performance experiments, the sensors had high consistency in the overall change trend, and the communication accuracy rate was high. We carried out a flight measurement comparison experiment at the Wenhua Road Campus of Henan Agricultural University, and the measurement data were highly consistent with the data from the national monitoring stations. We also conducted an agri-environmental atmospheric measurement experiment in Muzhai Village and obtained the vertical distribution data of PM concentration at the nearby measuring point when the harvester was working. The results showed that after the harvester worked for a period of time, the PM2.5, PM10, and TSP concentrations reached the maximum at the altitude of 20 m at the measurement point, which were 80, 198, and 384 μg/m³, respectively, 2.64~3.10 times the particle concentration in the environment before the harvester began to work. Our new platform had high mobility, sensitive reading, and stable communication during the experiment, and had high application value in agricultural environmental monitoring.

To address the problems of high unharvested rates and header loss rates in existing plot-breeding wheat harvesters, this study presents the design of a comb-type header for plot wheat harvesters. Based on the loss suppression mechanism during wheat harvesting, the key components of the comb-type header were designed. To address the issue in which some wheat ears escape combing during the harvesting process, a multi-stage comb-tooth structure was developed. For the problem of seed retention on the bottom plate of the screw conveyor, the telescopic tooth at the feeding port of the screw conveyor was replaced with a scraper, and a rubber plate was added. To determine the optimal combing time, wheat plant posture changes under the action of the nose (hereinafter referred to as the nose) were analyzed through theoretical analysis, simulation, and bench testing. It was determined that the optimal combing moment occurs when the plants begin to rebound to the maximum reverse bending. On this basis, a numerical simulation model of the header combing system was constructed using the discrete element method, with the header loss rate as the evaluation index to explore the influence of the nose height, the machine forward speed, and the combing drum rotation speed on the header performance. A regression model of header loss was constructed using the Box–Behnken response surface method, and the optimal working parameters were determined as follows: a nose height of 554 mm, a machine forward speed of 0.65 m/s, a combing drum rotation speed of 667 r/min, and the predicted loss rate of 8.59%. To verify the operational performance of the comb-type header, a field test of the wheat-harvesting prototype was conducted. The results showed that, under the optimal working parameters, the header loss rate was 7.24%, no wheat ears escaped combing, and no seed retention occurred in the header, which meets the requirements for plot wheat-breeding harvesting. This study provides a theoretical basis for the design and development of small-sized combing harvesters.

Design and optimization of the seedling needle and push device for a mechanical transplanter of seedlings from bowl-type trays made of paddy straw