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Abstract Background Seed vigor identification is critical to guaranteeing the quality and yield of maize. Although seeds with impaired vigor may germinate under normal conditions, planting under unfavorable conditions makes it difficult to produce healthy plants. Therefore, non-destructive and rapid detection of seed vigor using hyperspectral imaging (HSI) technology is crucial for improving crop production efficiency. Methods Hyperspectral images of maize seeds were acquired employing the HSI system, the original spectra were preprocessed using Savitzky–Golay smoothing and multiplicative scatter correction, and the feature wavelengths were extracted using the successive projections algorithm (SPA). Discriminant models were constructed based on support vector machine (SVM), random forest, artificial neural network (ANN), and convolutional neural network (CNN-DC). Results The results showed that SVM, ANN, and CNN-DC could discriminate well between maize seeds with different vigor levels, and their accuracy rate was over 70%. The SPA algorithm showed that the RMSE value achieved a minimum of 0.3406, while the number of variables was 49. The CNN-DC model outperformed the other models, which reached the highest accuracy of 92.06%. This study demonstrates that DL combined with HSI has excellent potential for identifying seed vigor. Conclusions This study shows that the proposed method has excellent results for hyperspectral image data processing and can accurately identify maize seed vigor.

Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency. One of the main challenges faced by multi robot navigation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting position to the expected target. In this article, we propose a low-cost multi-robot collision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts the possibility of collision based on the robot’s position and environmental information, and changes the robot’s path in advance, instead of waiting for the robot to make a collision avoidance decision when it is closer. Finally, we demonstrate that a multi-robot collision avoidance approach provides an excellent solution for safe and effective autonomous navigation of a single robot working in complex sweet potato fields. Our collision avoidance method allows the robot to move forward effectively in the field without getting stuck. More importantly, this method does not require expensive hardware and computing power, nor does it require tedious parameter tuning.

Rice seed production is a critical step in breeding high-quality varieties. To ensure seed purity, it is essential that no residual grains or broken ears remain in the harvester header after harvesting each variety, thus preventing cross-contamination. This study addresses the issue of seed retention in existing rice harvesters, which lack efficient self-cleaning or other cleaning mechanisms and cannot be cleaned rapidly. A self-cleaning device for the harvester header was designed to enable one-click cleaning after harvesting a single variety. A novel cleaning nozzle was developed as the key component of the device, with its structure optimized through single-factor and orthogonal combination experiments. The number of nozzles was determined based on their spray width and the header width. A header-cleaning airflow simulation model based on Fluent–EDEM coupling was constructed to investigate the effects of nozzle inlet pressure, airflow incident angle, and nozzle outlet height on the self-cleaning rate. Optimal cleaning parameters were identified to maximize the self-cleaning rate, and the simulation results were validated. The study revealed that the nozzle’s expansion section length, throat diameter, and contraction section length significantly affect the spray width. When the expansion section length was 10 mm, the throat diameter was 8 mm, and the contraction section length was 8 mm, the nozzle achieved the largest jet angle, measuring 50.3 cm. Further analysis indicated that inlet air pressure had the greatest influence on the self-cleaning rate, followed by airflow incident angle and nozzle outlet height. The optimal parameter combination was identified as an inlet air pressure of 0.6 Mpa, an airflow incident angle of 118.25°, and a nozzle outlet height of 2.64 mm, achieving a maximum self-cleaning rate of 99.63%. A one-click cleaning system was designed using an STM32 microcontroller and hardware circuits. Field experiments under optimal parameters demonstrated a self-cleaning rate of 97.68% with a cleaning duration of 10 s per cycle. The findings provide theoretical guidance for the design and optimization of self-cleaning headers for rice seed production.

Traditional methods of pest control for sweet potatoes cause the waste of pesticides and land pollution, but the target detection algorithm based on deep learning can control the precise spraying of pesticides on sweet potato plants and prevent most pesticides from entering the land. Aiming at the problems of low detection accuracy of sweet potato plants and the complex of target detection models in natural environments, an improved algorithm based on YOLOv8s is proposed, which can accurately identify early sweet potato plants. First, this method uses an efficient network model to enhance the information flow in the channel, obtain more effective global features in the high-level semantic structure, and reduce model parameters and computational complexity. Then, cross-scale feature fusion and the general efficient aggregation architecture are used to further enhance the network feature extraction capability. Finally, the loss function is replaced with InnerFocaler-IoU (IFIoU) to improve the convergence speed and robustness of the model. Experimental results showed that the mAP0.5 and model size of the improved network reached 96.3% and 7.6 MB. Compared with the YOLOv8s baseline network, the number of parameters was reduced by 67.8%, the amount of computation was reduced by 53.1%, and the mAP0.5:0.95 increased by 3.5%. The improved algorithm has higher detection accuracy and a lower parameter and calculation amount. This method realizes the accurate detection of sweet potato plants in the natural environment and provides technical support and guidance for reducing pesticide waste and pesticide pollution.

To meet the diverse seeding requirements of super hybrid rice, common hybrid rice, and conventional rice—which vary from 1 to 3 seeds, 2 to 4 seeds, and 5 to 8 seeds per hole, respectively—this study developed a branched air-chamber type pneumatic seed metering device for rice. The device utilizes an air chamber control board to manage the branched air chamber casing, enabling precise adjustments to the seeding quantity. This study presents a theoretical analysis of the seed metering device’s operation and its critical components. Structural parameter optimization was conducted using Ansys-Fluent (2021 R1) software, followed by multi-objective optimization of operational parameters through bench testing. Simulation results indicated that optimal vacuum pressure in the seed metering disc pores reached a maximum of 857 Pa with a chamber depth of 22 mm, an angle of 100°, and a cavity depth of 25 mm, achieving a minimal coefficient of variation of 0.86%. Bench test results showed that for seeding targets of 1 to 3 rice seeds per hole, the optimal operational parameters were: two openings, a working vacuum of 1355 Pa, and a rotor speed of 32.78 r/min, resulting in a missed seeding rate of 4.70%, a qualification rate of 85.81%, and a re-seeding rate of 9.49%. For targets of 2 to 4 seeds per hole, the best parameters included three openings, a working vacuum of 1357 Pa, and a speed of 32.87 r/min, with a missed seeding rate of 4.60%, a qualification rate of 85.59%, and a re-seeding rate of 9.81%. For 5 to 8 seeds per hole, optimal parameters were six openings, a vacuum of 1339 Pa, and a rotor speed of 31.07 r/min, yielding a missed seeding rate of 4.09%, a qualification rate of 87.27%, and a re-seeding rate of 8.64%. These findings demonstrate that the branched air-chamber type pneumatic seed metering device effectively meets the varied direct seeding requirements of rice, enhancing the adaptability of pneumatic seed metering devices to different seeding quantities in rice and potentially informing the design of pneumatic seeders for other crops.

Aiming at the problems of large error and redundancy in the multi-node data acquisition of multi-greenhouse photo growth environmental information, a three-level fusion algorithm based on adaptive weighting, an LMBP network, and an improved D-S theory is proposed. The box-and-line graph method recognizes the original data and then replaces it based on the mean value method; the air temperature, humidity, and light intensity measurements are unbiased estimations of the true value to be estimated, so the first level of fusion chooses the adaptive weighted average algorithm to find the optimal weights of each sensor under the condition of minimizing the total mean-square error and obtains the optimal estimation of the weights of the homogeneous sensors of a greenhouse. The Levenberg–Marquardt algorithm was chosen for the second level of fusion to optimize the weight modification of the BP neural network, i.e., the LMBP network, and the three environmental factors corresponding to “suitable”, “uncertain” and “unsuitable” potato growth environments were trained for the three environmental factors in the reproductive periods. The output of the hidden layer was converted into probability by the Softmax function. The third level is based on the global fusion of evidence theory (also known as D-S theory), and the network output is used as evidence to obtain a consistent description of the multi-greenhouse potato cultivation environment and the overall scheduling of farming activities, which better solves the problem of the difficulty in obtaining basic probability assignments in the evidence theory; in the case of a conflict between the evidence, the BPA of the conflicting evidence is reallocated, i.e., the D-S theory is improved. Example validation shows that the total mean square error of the adaptive weighted fusion value is smaller than the variance of each sensor estimation, and sensors with lower variance are assigned lower weights, which makes the fusion result not have a large deviation due to the failure of individual sensors; when the fusion result of a greenhouse feature level is “unsuitable”, the fusion result of each data level is considered comprehensively, and the remote control agency makes a decision, which makes full use of the complementary nature of multi-sensor information resources and solves the problem of fusion of multi-source environmental information and the problem of combining conflicting environmental evaluation factors. Compared with the traditional D-S theory, the improved D-S theory reduces the probability of the “uncertainty” index in the fusion result again. The three-level fusion algorithm in this paper does not sacrifice data accuracy and greatly reduces the noise and redundancy of the original data, laying a foundation for big data analysis.

In order to solve the current problems of time-consuming manual work and low harvesting efficiency during the harvesting of potato breeding trials and the inability of traditional harvesting machinery to meet the requirements of potato single plant harvesting, a single plant harvester for potato breeding with a combined excavation device and a conveying and separating device with alternating concave and straight units is designed. This paper analyzes the force, motion, and collision of potato plants in the digging transition stage and conveying and separating stage. The key factors affecting the rate of single plant integrity and the rate of injured potatoes are determined. A three-factor quadratic regression orthogonal rotating combination experiment is conducted with the rate of single plant integrity and the rate of injured potatoes as experiment indexes, forward speed, conveying speed to forward speed ratio, and the rotational speed of the rear-vibration device as influencing factors. Based on the experiment results, a mathematical regression model is established, and response surface analysis and parametric analysis are conducted to determine that when the forward speed is 0.65 m∙s−1, the ratio of conveying speed to forward speed is 0.98, and the speed of the post-vibration device is 11.52 rad∙s−1, the single plant integrity rate is 97.14% and the rate of injured potatoes is 0.67%. The machine can better meet the requirements of potato single plant harvesting and solve the problem of plant mixing. From the perspective of potato damage rate, potato breeding harvesters are also suitable for traditional potato harvesting.

Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.

Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The first step is to replace the original U-Net’s backbone feature extraction structure with VGG16 to segment the potato crop rows. Secondly, a fitting method of feature midpoint adaptation is proposed, which can realize the adaptive adjustment of the vision navigation line position according to the growth shape of a potato. The results show that the method used in this paper has strong robustness and can accurately detect navigation lines in different potato growth periods. Furthermore, compared with the original U-Net model, the crop row segmentation accuracy is improved by 3%, and the average deviation of the fitted navigation lines is 2.16°, which is superior to the traditional visual guidance method.

A transplanting manipulator and a transplanting conveyance device were devised to address the problems with current sweet potato transplanters, which cannot properly finish boat-bottom-shaped transplanting and have a high seedling damage rate. The ground wheel transmits power to the two devices in a specific transmission ratio to ensure their synchronous movement and avoid accumulated deviations. We selected the transplanting manipulator’s parameter combination that satisfies the optimal transplanting trajectory by the VB program. To avoid damage to the seedlings during clamping, the clamping force is only provided by the spring through the structural design. Combined with the agronomic requirements of boat-bottom-shaped transplanting and the characteristics of soil resistance during the transplanting process, the transplanting depth and crank rotation speed were selected as test factors. The qualified rates of node depth in the seedling middle (QRNDSM), the qualified rates of seedling root depth (QRSRD), and the qualified rates of stalk length into the soil (QRSLS) were established, and Design-Expert was used for the quadratic regression and orthogonal combination tests to optimize the factors. The results of the verification tests reveal that when the transplanting depth is in the range of 45.56 to 58.26 mm and the crank rotation speed is in the range of 39.39 to 43.45 rpm, the maximum QRNDSM is 94.91%, the maximum QRSRD is 95.42%, and the maximum QRSLS is 94.93%, which satisfies the industry standards. Research into sweet potato transplanting can benefit from this work, especially in the direction of sweet potato boat-bottom-shaped transplanting.

Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

In this paper, an adaptive header height control system is designed. Through the influence of the natural frequency, ωn, and damping ratio, ζ, on the system’s dynamic index, the optimal hydraulic cylinder parameters are determined comprehensively. The ground profiling monitoring mechanism and the header height feedback mechanism based on the angle sensor are designed. An integrated electromagnetic proportional valve was installed to replace the original header-controlled, electronically-controlled reversing valve, and a PWM (pulse width modulation) control-simulated counterweight test was performed. The limitation of traditional PID facing the integral saturation state is analysed, and a new EVPIVS-PID algorithm is proposed and simulated. Through the analysis of multiple groups of sample data in the field test, the accuracy of the control system in the header height control and output PWM value is demonstrated. The effectiveness of the EVPIVS-PID control algorithm to change the corresponding PID parameters based on the monitoring operation speed is analysed and demonstrated. Experiments show that the adaptive control system of header height based on ground profiling has a stable control effect. The height error of cutting stubble is not more than 2 cm, which can meet the requirements of a 5–11 km/h harvesting speed in plain areas.

In order to solve the problems of easy-to-break kernels and substantial harvest losses during kernel harvesting in breeding trials plot of corn, an ear-picking device and a threshing device of corn plot kernel harvester has been optimized. To automatically change the gap of the ear-picking plate, a self-elastic structure with compression spring and connecting rod is used. The ear-picking plate is glued, and an elastic rubber gasket is placed underneath it, which effectively improves the adaptability of the ear-picking device and reduces corn kernel collision damage during ear-picking. To ensure the self-purification of the ear-picking device, a combination of auger sieve hole cleaning device and lateral pneumatic auxiliary cleaning system is used. A dual-axial flow threshing device is designed, which uses a “U”-shaped conveying system to transport maize ears in the threshing chamber. The spacing of the concave sieve may be adjusted, and the residual kernels in the threshing chamber can be cleaned up after harvesting one plot by combining three cleanings, which meets the requirements of no mixing between plots. The force analysis of corn ears in the threshing chamber determines the best design plan for the forward speed, the speed of the second threshing drum, and the threshing gap. The breakage rate and non-threshing rate regression models were created using the quadratic regression orthogonal combination test, and the parameters were optimized using MATLAB. The verification test results showed that when the forward speed was 0.61 m/s, the second threshing drum speed was 500 r/min, and the threshing gap was 40 mm, the breakage rate was 1.47%, and the non-threshing rate was 0.89%, which met the kernel harvesting requirements in corn plots.

Aiming at the digging shovel of the red soil harvester in the cassava planting area, problems such as high digging resistance and difficulty in breaking the soil when harvesting cassava tubers will lead to blockage at the connection between the digging device and the transmission device. Using the rabbit's front paws as a bionic prototype, three-dimensional scanning and reverse engineering technologies were employed to extract their unique geometric features. These quantified geometric structural characteristics were then applied to the design of an excavation shovel, aiming to reduce resistance during excavation operations. Based on the discrete element (EDEM) coupled RECURDYN, using the resistance of the excavation shovel and the Bonding key breakage rate as evaluation indicators, the discrete element orthogonal analysis of three factors including the shovel tooth length, shovel tooth width, and shovel edge inclination angle was carried out based on the simulation test results. Test was performed to determine the best parameter combination for drag reduction and crushing rate of the bionic shovel; the working resistance of the shovel was used as an evaluation index to verify the excavation performance of the bionic shovel through field tests. The optimal parameter combination of the bionic shovel based on the discrete element simulation test is the tooth length of 220 mm, the tooth width of 65.1 mm, and the blade inclination 60°. The excavation resistance of this combination is 1733.66 N and the maximum soil fragmentation rate is 92.9%. Through field tests, it can be found that when the excavation depth is 310 mm and the forward speed is 300 mm/s. The Type 1 bionic digging shovel exhibits a reduction in resistance of 6.84%, while the Type 2 bionic digging shovel demonstrates a more significant reduction of 9.21%, compared to the traditional digging shovel. Tests have shown that the bionic shovel type 2 has excellent soil excavation characteristics and can complete cassava excavation operations in tropical red soil areas. It can provide a design reference for reducing drag and saving energy for cassava harvesters. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high skin-breaking rate and high impurity rate of sweet potato during harvesting operations, a low-damage fresh-eating sweet potato combine harvester based on a two-segment potato-soil separation device was designed by using a "d"-type elevator chain combined with a double-buffer clearing platform technology. The results show that the best working parameters of the harvester are a vibrating shaft frequency of 5.2 Hz, elevator chain speed of 0.37 m/s, and cleaning platform speed of 0.58 m/s, in which the sweet potato skin-breaking rate is 1.09% and the impurity rate is 1.90%, which is in line with the standard. [ABSTRACT FROM AUTHOR]

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A unified formulation for vibration analysis of coupled open cylindrical shell and annular sector plate system with general boundary and coupling conditions is presented in this study by using a modified Fourier-Ritz method. Under the framework, each of the displacements of the open cylindrical shell and the annular sector plate, regardless of boundary and coupling conditions, is expanded as a two-dimensional (2-D) Fourier cosine series supplemented with closed-form auxiliary functions introduced to remove the potential discontinuities at the junction and accelerate the convergence of the series expansion. Since the displacement fields are constructed adequately smooth throughout the entire solution domain, an exact solution is obtained based on the Rayleigh-Ritz procedure by using the energy functions of the coupled system. The arbitrary coupling position and included angle of the open cylindrical shell-annular sector plate structure considered in the theoretical formulation make the present method more general. The convergence and accuracy of the present method are tested and validated by a number of numerical examples for open cylindrical shell-annular sector plate structure with various boundary restraints and general elastic coupling conditions. Some new results are presented to provide useful information for future researchers. [ABSTRACT FROM AUTHOR]