Intelligent Agriculture: Perception Technologies and Agricultural Equipment for Crop Production Processes

Flax, an important oil and fiber crop, is widely cultivated in temperate and sub-frigid regions worldwide. China is one of the major producers of flax, with Gansu Province predominantly practicing cultivation in hilly areas. However, common issues such as feeding difficulties, stem entanglement, and low threshing efficiency significantly restrict the improvement of planting efficiency. This study addresses the key technical challenges in flax combine harvesting in hilly regions by developing a discrete element model of the flax plant and utilizing DEM-FEA co-simulation technology. The performance of two threshing drum models (T₁ and T₂) was analyzed, focusing on motion trajectory, stress distribution, and threshing effects. The simulation results show that the T₂ model, with its combination of rib and rod tooth design, significantly improves threshing and separation efficiency. The loss rate was reduced from 5.6% in the T₁ model to 1.78% in the T₂ model, while the maximum stress and deformation were significantly lower, indicating higher structural stability and durability. Field validation results revealed that the T₁ model had a total loss rate of 3.32%, an impurity rate of 3.57%, and an efficiency of 0.09 hm²/h. In contrast, the T₂ model achieved a total loss rate of 2.29%, an impurity rate of 3.39%, and an efficiency of 0.22 hm²/h, representing a 144.4% improvement in working efficiency. These findings indicate that the T₂ model has a higher potential for flax harvesting in hilly and mountainous regions, especially in improving threshing efficiency and operational stability, providing an important theoretical basis for optimizing threshing equipment design. Full article

Crop phenotype detection is a precise way to understand and predict the growth of horticultural seedlings in the smart agriculture era to increase the cost-effectiveness and energy efficiency of agricultural production. Crop phenotype detection requires the consideration of plant stature and agricultural devices, like robots and autonomous vehicles, in smart greenhouse ecosystems. However, collecting the imaging dataset is a challenge facing the deep learning detection of plant phenotype given the dynamic changes among leaves and the temporospatial limits of camara sampling. To address this issue, digital cousin is an improvement on digital twins that can be used to create virtual entities of plants through the creation of dynamic 3D structures and plant attributes using RGB image datasets in a simulation environment, using the principles of the variations and interactions of plants in the physical world. Thus, this work presents a two-phase method to obtain the phenotype of horticultural seedling growth. In the first phase, 3D Gaussian splatting is selected to reconstruct and store the 3D model of the plant with 7000 and 30,000 training rounds, enabling the capture of RGB images and the detection of the phenotypes of the seedlings, overcoming temporal and spatial limitations. In the second phase, an improved YOLOv8 model is created to segment and measure the seedlings, and it is modified by adding the LADH, SPPELAN, and Focaler-ECIoU modules. Compared with the original YOLOv8, the precision of our model is 91%, and the loss metric is lower by approximately 0.24. Moreover, a case study of watermelon seedings is examined, and the results of the 3D reconstruction of the seedlings show that our model outperforms classical segmentation algorithms on the main metrics, achieving a 91.0% mAP50 (B) and a 91.3% mAP50 (M). Full article

Due to the high plant height, heavy ear, and easy forward tilt of millet during harvesting, the reel of a traditional combine harvester is often difficult to adapt to the special growth characteristics of millet, resulting in serious grain loss. Therefore, optimizing the design of the reel is important to improve the harvesting efficiency of millet and reduce the grain header loss. In order to determine the optimal reel speed ratio(λ), kinematics simulation experiments and analysis were carried out under different combinations of forward speed and reel revolution speed. The results showed that the supporting effect of the reel is insufficient when λ ≤ 1, and the trochoidal trajectory of the reel can provide a backward driving force when λ > 1, the optimum speed ratio of the reel should be controlled between 1.5 and 1.6. Field experiments results showed that the grain header loss rate was the lowest (0.9%) when λ = 1.6. This study provides key guidance for the adjustment of the combine harvester, effectively reducing the grain header loss rate in harvesting millet, and improving the harvesting efficiency. Full article

Nighttime pineapple detection for automated harvesting robots is a significant challenge in intelligent agriculture. As a crucial component of robotic vision systems, accurate fruit detection is essential for round-the-clock operations. The study compared advanced end-to-end style transfer models, including U-GAT-IT, SCTNet, and CycleGAN, finding that CycleGAN produced relatively good-quality images but had issues such as the inadequate restoration of nighttime details, color distortion, and artifacts. Therefore, this study further proposed an enhanced CycleGAN approach to address limited nighttime datasets and poor visibility, combining style transfer with small-sample object detection. The improved model features a novel generator structure with ResNeXtBlocks, an optimized upsampling module, and a hyperparameter optimization strategy. This approach achieves a 29.7% reduction in FID score compared to the original CycleGAN. When applied to YOLOv7-based detection, this method significantly outperforms existing approaches, improving precision, recall, average precision, and F1 score by 13.34%, 45.11%, 56.52%, and 30.52%, respectively. These results demonstrate the effectiveness of our enhanced CycleGAN in expanding limited nighttime datasets and supporting efficient automated harvesting in low-light conditions, contributing to the development of more versatile agricultural robots capable of continuous operation. Full article

To investigate the biomechanical properties of Camellia oleifera branches under two loading speeds within a specific diameter range, three-point bending tests were conducted using a universal material–testing machine. The tests were performed at loading speeds of 10 mm/min and 20 mm/min on branches with diameters ranging from 5 mm to 40 mm. This study aims to provide insights into the design of a manipulator gripper used in a vibrating harvester for Camellia oleifera fruit. Four main varieties of Camellia oleifera were tested to determine their elastic modulus. The nonlinear least squares method, based on the hyperbolic tangent function, was employed to fit the bending load–deflection curves of the branches. This process constructed multi-parameter transcendental equations involving elastic modulus, diameter, and loading speed. Results indicated that the branches of four Camellia oleifera varieties exhibited significant differences in their biomechanical properties, with their modulus of elasticity ranging from 459.01 MPa to 983.33 MPa. This suggests variability in the bending performance among different varieties. For instance, Huaxin branches demonstrated the highest rigidity, while Huashuo branches were softer in general. For the proposed empirical fitting equations, when the fitting parameter k is 168 ± 20 and the parameter c is 3.102 ± 0.421, the bending load–deflection relationship of the branches can be predicted more accurately. This study provides a theoretical basis for enhancing the efficiency of mechanized vibratory picking of Camellia oleifera and optimising the design of the gripper. Full article

Ratooning rice plants have a high moisture content and strong adhesion during harvesting. Traditional cleaning devices are prone to clogging when processing ratooning rice, resulting in a series of problems such as high grain loss rate and high grain impurity rate. In response to the above issues, this article adopts the CFD-DEM coupling method to design a spiral step cleaning device. A detailed analysis was conducted on the influence of the cone angle and thickness of the spiral-stepped skeletons on the flow state, and flow velocity and pressure distribution cloud maps were obtained under different structural parameters. The vortex morphology under different thicknesses of the spiral-stepped skeletons was compared, and the structural parameters of the device were determined. The motion trajectory and distribution of impurity particles under different inlet flow velocities were analyzed using data superposition, and the appropriate inlet flow velocity range was determined. A test bench was built, and a three-factor quadratic regression orthogonal rotation combination experiment was conducted with fan speed, feeding rate, and device inclination angle as experimental factors. The results of the bench test show that the performance index reaches its optimum when the device inclination angle, fan speed, and feeding rate are 2.47°, 2906 r/min, and 4.0 kg/s, respectively. At this time, the grain impurity rate, grain loss rate, and sieve clogging rate are 2.21%, 2.15%, and 3.5%, respectively. Compared to those of traditional cleaning equipment, these value are reduced by 44.5%, 39.6%, and 83.9%, respectively. This study can provide ideas for the design of ratooning rice cleaning devices. Full article

The advent of precision agriculture has highlighted the necessity for the careful determination of crop phenology at increasingly smaller scales. Although remote sensing technology is extensively employed for the monitoring of crop growth, the acquisition of high-precision phenological data continues to present a significant challenge. This study, conducted in Youyi County, Shuangyashan City, Heilongjiang Province, China, employed time-series spectral index data derived from Sentinel-2 remote sensing images to investigate methodologies for the extraction of pivotal phenological phases during the primary growth stages of maize. The data were subjected to Savitzky–Golay (S-G) filtering and cubic spline interpolation in order to denoise and smooth them. The combination of dynamic thresholding with slope characteristic node recognition enabled the successful extraction of the jointing and tasseling stages of maize. Furthermore, a comparison of the extraction of phenophases based on the time-series curves of the NDVI, EVI, GNDVI, OSAVI, and MSR was conducted. The results showed that maize exhibited different sensitivities to the spectral indices during the jointing and tasseling stages: the OSAVI demonstrated the highest accuracy for the jointing stage, with a mean absolute error of 3.91 days, representing a 24.8% improvement over the commonly used NDVI. For the tasseling stage, the MSR was the most accurate, achieving an absolute error of 4.87 days, with an 8.6% improvement compared to the NDVI. In this study, further analysis was conducted based on maize cultivation data from Youyi County (2021–2023). The results showed that the maize phenology in Youyi County in 2021 was more advanced compared to 2022 and 2023, primarily due to the higher average temperatures in 2021. This study provides valuable support for the development of precision agriculture and maize phenology monitoring and also provides a useful data reference for future agricultural management. Full article

This study addresses the problem of detecting occluded apples in complex unstructured environments in orchards and proposes an apple detection and segmentation model based on improved YOLOv8n-SGW-YOLOv8n. The model improves apple detection and segmentation by combining the SPD-Conv convolution module, the GAM global attention mechanism, and the Wise-IoU loss function, which enhances the accuracy and robustness. The SPD-Conv module preserves fine-grained features in the image by converting spatial information into channel information, which is particularly suitable for small target detection. The GAM global attention mechanism enhances the recognition of occluded targets by strengthening the feature representation of channel and spatial dimensions. The Wise-IoU loss function further optimises the regression accuracy of the target frame. Finally, the pre-prepared dataset is used for model training and validation. The results show that the SGW-YOLOv8n model significantly improves relative to the original YOLOv8n in target detection and instance segmentation tasks, especially in occlusion scenes. The model improves the detection mAP to 75.9% and the segmentation mAP to 75.7% and maintains a processing speed of 44.37 FPS, which can meet the real-time requirements, providing effective technical support for the detection and segmentation of fruits in complex unstructured environments for fruit harvesting robots. Full article

At present, tomato transplanting in facility agriculture is mainly manual operation. In an attempt to resolve the problems of high labor intensity and low efficiency of manual operation, this paper designs a clip stem automatic transplanting and seedling picking device based on the yolov5 algorithm. First of all, through the study of the characteristics of tomato seedlings of different seedling ages, the age of tomato seedlings suitable for transplanting was obtained. Secondly, the improved yolov5 algorithm was used to determine the position and shape of tomato seedlings. By adding a lightweight upsampling operator (CARAFE) and an improved loss function, the feature extraction ability and detection speed of tomato seedling stems were improved. The accuracy of the improved yolov5 algorithm reached 92.6%, and mAP_0.5 reached 95.4%. Finally, the seedling verification test was carried out with tomato seedlings of about 40 days old. The test results show that the damage rate of the device is 7.2%, and the success rate is not less than 90.3%. This study can provide a reference for research into automatic transplanting machines. Full article

For the mechanized technical mode of total wheat straw returning to field, there are problems such as large vibration during the operation of the straw returning machine that, in turn, affect the effect of stubble breaking. This study took the Tongtian 1-JHY-220 straw returning machine as the research object to conduct field experiments, with wheat stubble height, forward velocity, and PTO speed as experimental parameters. And the vibration characteristics at different positions of the machine and the final stubble breaking rate were used as evaluation indicators. Combined with the orthogonal experiment and response surface analysis method, this article analyzes and discusses the influence of various parameters on vibration characteristics and operational effectiveness. The results show that PTO speed and wheat stubble height were the main factors affecting the vibration and operation quality of the straw returning machine. Low PTO speed and high stubble height can improve the stubble breaking rate of the straw returning machine and reduce its operation vibration. Furthermore, the multi-objective optimization results show that when the forward velocity in the range of 8.5–9 km/h, the PTO speed is 540 r/min, and the stubble height is in the range of 200–250 mm, the stubble breaking rate of the straw returning machine is greater than 86%. At this time, the total vibration of the straw returning machine and tractor rear axle is relatively small. This study can lay a foundation for further studying the impact of the vibration of the straw returning machine on the stubble breaking effect and provide a reference for the preparation of high-quality seedbed under conservation tillage. Full article

Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices. Full article

Curing modulation is one of the important processes in tobacco production, so it is crucial to recognize tobacco flue-curing states effectively and accurately. This study created a dataset of the complete tobacco flue-curing process in a bulk curing barn environment and proposed a lightweight recognition model based on a feature skip connections module. Firstly, the image data was enhanced using a color correction matrix, which was used to recover the true color of the tobacco leaf in order to reduce the misidentification of adjacent states. Secondly, the convolutional neural network model proposed in this paper introduced Spatially Separable convolution to enhance the extraction of tobacco leaf texture features. Then, the standard convolution in Short-Term Dense Concatenate (STDC) was replaced with Depthwise Separable Convolutional blocks with different expansion rates to reduce the number of model parameters and FLOPs (Floating Point Operations Per Second). Finally, the Tobacco Flue-Curing State Recognition Network (TFSNet) was constructed by combining the SimAm attention mechanism. The experimental results showed that the model accuracy was improved by 1.63 percentage points after the color correction process. The recognition accuracy of TFSNet for the seven states of tobacco flue-curing was as high as 98.71%. The number of params and the FLOPs of the TFSNet model were 203,058 and 172.39 M, which were 98.18% and 90.55% lower than that of the ResNet18 model, respectively. The size of the model was 0.78 mb, and the time consumed per frame was only 21 ms. Compared with the mainstream model, TFSNet significantly improved the detection speed while maintaining high accuracy, and it provided effective technical support for the intelligentization of the tobacco flue-curing process. Full article

The high tillage resistance of agricultural soil-engaging tools (TASTs) in farmland operations (e.g., tillage, sowing, crop management, and harvesting) increases fuel consumption and harmful gas emissions, which negatively affect the development of sustainable agriculture. Biomimetic methods are promising and effective technologies for reducing the TASTs and have been developed in the past few years. This review comprehensively summarizes the typical agricultural soil-engaging tools (ASETs) and their characteristics and presents existing biomimetic methods for decreasing TASTs. The introduction of TAST reduction was performed on aspects of tillage, sowing, crop management, and harvesting. The internal mechanisms and possible limitations of current biomimetic methods for various ASETs were investigated. The tillage resistance reduction rates of ASETs, as affected by various biomimetic methods, were quantitatively compared under different soil conditions with statistical analyses. Additionally, three future research directions were recommended in the review to further reduce TASTs and encourage the development of sustainable agriculture. Full article

With the advancement of computer vision technology, the demand for fruit recognition in agricultural automation is increasing. To improve the accuracy and efficiency of recognizing young red pears, this study proposes an improved model based on the lightweight YOLOv9s, termed YOLOv9s-Pear. By constructing a feature-rich and diverse image dataset of young red pears and introducing spatial-channel decoupled downsampling (SCDown), C2FUIBELAN, and the YOLOv10 detection head (v10detect) modules, the YOLOv9s model was enhanced to achieve efficient recognition of small targets in resource-constrained agricultural environments. Images of young red pears were captured at different times and locations and underwent preprocessing to establish a high-quality dataset. For model improvements, this study integrated the general inverted bottleneck blocks from C2f and MobileNetV4 with the RepNCSPELAN4 module from the YOLOv9s model to form the new C2FUIBELAN module, enhancing the model’s accuracy and training speed for small-scale object detection. Additionally, the SCDown and v10detect modules replaced the original AConv and detection head structures of the YOLOv9s model, further improving performance. The experimental results demonstrated that the YOLOv9s-Pear model achieved high detection accuracy in recognizing young red pears, while reducing computational costs and parameters. The detection accuracy, recall, mean precision, and extended mean precision were 0.971, 0.970, 0.991, and 0.848, respectively. These results confirm the efficiency of the SCDown, C2FUIBELAN, and v10detect modules in young red pear recognition tasks. The findings of this study not only provide a fast and accurate technique for recognizing young red pears but also offer a reference for detecting young fruits of other fruit trees, significantly contributing to the advancement of agricultural automation technology. Full article

The pitaya is a common fruit in southern China, but the growing environment of pitayas is complex, with a high density of foliage. This intricate natural environment is a significant contributing factor to misidentification and omission in the detection of the growing state of pitayas. In this paper, the growth states of pitayas are classified into three categories: flowering, immature, and mature. In order to reduce the misidentification and omission in the recognition process, we propose a detection model based on an improvement of the network structure of YOLOv8, namely YOLOv8n-CBN. The YOLOv8n-CBN model is based on the YOLOv8n network structure, with the incorporation of a CBAM attention mechanism module, a bidirectional feature pyramid network (BiFPN), and a C2PFN integration. Additionally, the C2F module has been replaced by a C2F_DCN module containing a deformable convolution (DCNv2). The experimental results demonstrate that YOLOv8n-CBN has enhanced the precision, recall, and mean average precision of the YOLOv8n model with an IoU threshold of 0.5. The model demonstrates a 91.1% accuracy, a 3.1% improvement over the original model, and an F1 score of 87.6%, a 3.4% enhancement over the original model. In comparison to YOLOv3-tiny, YOLOv5s, and YOLOv5m, which are highly effective target detection models, the [email protected]–0.95 of our proposed YOLOv8n-CBN is observed to be 10.1%, 5.0%, and 1.6% higher, respectively. This demonstrates that YOLOv8n-CBN is capable of more accurately identifying and detecting the growth status of pitaya in a natural environment. Full article

The combine harvester equipped with attitude-adjustment functionality significantly enhances its adaptability to complex terrain but often struggles to maintain the reliability of its mechanisms. Therefore, investigating the dynamic load characteristics of the attitude-adjustment mechanism becomes imperative. This article employed the DEM–FMBD (Discrete Element Method–Flexible Multibody Dynamics) bidirectional coupling simulation method to establish a multibody dynamic model of a tracked combine harvester. The study delved into the interaction mechanism and dynamic stress response characteristics between the tracked chassis and the complex terrain under various height adjustments, lateral adjustment angles, longitudinal adjustment angles, and different field-ridge crossing methods. Finally, the accuracy of the coupled simulation model was validated through a constructed stress detection system. The research findings revealed that the displacement and tilt angle deviation of the hydraulic cylinders utilized to execute the chassis adjustment actions in the constructed coupled simulation model was less than 5%, and the deviation between the simulation results and the actual maximum dynamic stress under multiple working conditions ranged from 7% to 15%. This verification confirmed the effectiveness of the DEM–FMBD coupled simulation method. Under different adjustment conditions, the maximum stress position was consistently distributed in the same area of the left-front and left-rear rotating arms. The primary and secondary effects of the various parts of the adjustment mechanism on the overall reliability of the chassis were as follows: left front > right front > left rear > right rear. By implementing the middle height with the adjustment strategy, the dynamic stress extreme value of the adjustment mechanism can be effectively reduced by 21.98%, thereby enhancing the structural stability of the chassis. Full article

The arid area of Northwest China belongs to the rain-fed agricultural area of the Loess Plateau, and water resources have become one of the important factors limiting agricultural development in this area. This study employed the AquaCrop model to predict the yield advantages and environmental adaptability of maize in Dingxi City from 2016 to 2020 under two cultivation practices: ridge tillage (100% film coverage with double ridge-furrow planting) and flat planting (81.8% film coverage with wide-film planting). The numerical simulation of the tillage and fertilization process of the double-ridge seedbed was carried out by EDEM, and the key components were tested by the Box–Behnken center combination test design principle to obtain the optimal parameter combination. The results showed that ridge planting was more suitable for agricultural planting in rain-fed arid areas in Northwest China. The simulation analysis of ridging and fertilization showed that the forward speed of the combined machine was 0.50 m/s, the rotation speed of the trough wheel of the fertilizer discharger was 39 rmp, and the rotary tillage depth was 150 mm. The qualified rate of seedbed tillage was 93.6%, and the qualified rate of fertilization was 92.1%. The research shows that the whole-film double ridge-furrow sowing technology of maize is more suitable for the rain-fed agricultural area in the arid area of Northwest China. The simulation results of the ridging fertilization device are consistent with the field experiment results. The research results provide a certain technical reference for the optimization of the whole-film double ridge-furrow sowing technology. Full article

Berries are nutritious and valuable, but their thin skin, soft flesh, and fragility make harvesting and picking challenging. Manual and traditional mechanical harvesting methods are commonly used, but they are costly in labor and can damage the fruit. To overcome these challenges, it may be worth exploring alternative harvesting methods. Using berry fruit-picking robots with perception technology is a viable option to improve the efficiency of berry harvesting. This review presents an overview of the mechanisms of berry fruit-picking robots, encompassing their underlying principles, the mechanics of picking and grasping, and an examination of their structural design. The importance of perception technology during the picking process is highlighted. Then, several perception techniques commonly used by berry fruit-picking robots are described, including visual perception, tactile perception, distance measurement, and switching sensors. The methods of these four perceptual techniques used by berry-picking robots are described, and their advantages and disadvantages are analyzed. In addition, the technical characteristics of perception technologies in practical applications are analyzed and summarized, and several advanced applications of berry fruit-picking robots are presented. Finally, the challenges that perception technologies need to overcome and the prospects for overcoming these challenges are discussed. Full article

In the process of agricultural production in solar greenhouses, the key to the healthy growth of greenhouse crops lies in accurately predicting environmental conditions. However, there are complex couplings and nonlinear relationships among greenhouse environmental parameters. This study independently developed a greenhouse environmental acquisition system to achieve a comprehensive method for the monitoring of the greenhouse environment. Additionally, it proposed a multi-parameter and multi-node environmental prediction model for solar greenhouses based on the Golden Jackal Optimization-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Self-Attention Mechanism (GCBS). The GCBS model successfully captures the complex nonlinear relationships in the greenhouse environment and accurately predicts changes in carbon dioxide concentration, air temperature and humidity, and soil temperature at different location nodes. To validate the performance of this model, we employed multiple evaluation metrics and conducted a comparative analysis with four baseline models. The results indicate that, while the GCBS model exhibits slightly higher computational time compared to the traditional Long Short-Term Memory (LSTM) network for time series prediction, it significantly outperforms the LSTM in terms of prediction accuracy for four key parameters, achieving improvements of 76.89%, 69.37%, 59.83%, and 56.72%, respectively, as measured by the Mean Absolute Error (MAE) metric. Full article