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Abstract Random media pose limitations on the imaging capability of photoelectric detection devices. Currently, imaging techniques employed through random media primarily operate within the laser wavelength range, leaving the imaging potential of terahertz waves unexplored. In this study, we present an approach for terahertz bistatic three-dimensional imaging (TBTCI) of hidden objects through random media. By deducing the field distribution of bistatic terahertz time-domain spectroscopy system, and proposing an explicit point spread function of the random media, we conducted three-dimensional imaging of hidden objects obscured by the random media. Our proposed method exhibits promising applications in imaging scenarios with millimeter-wave radar, including non-invasive testing and biological imaging.

Pear anthracnose, caused by Colletotrichum bacteria, is a severe infectious disease that significantly impacts the growth, development, and fruit yield of pear trees. Early detection of pear anthracnose before symptoms manifest is of great importance in preventing its spread and minimizing economic losses. This study utilized hyperspectral imaging (HSI) technology to investigate early detection of pear anthracnose through spectral features, vegetation indices (VIs), and texture features (TFs). Healthy and diseased pear leaves aged 1 to 5 days were selected as subjects for capturing hyperspectral images at various stages of health and disease. Characteristic wavelengths (OWs1 and OWs2) were extracted using the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm. Significant VIs were identified using the Random Forest (RF) algorithm, while effective TFs were derived from the Gray Level Co-occurrence Matrix (GLCM). A classification model for pear leaf early anthracnose disease was constructed by integrating different features using three machine learning algorithms: Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN). The results showed that: the classification identification model constructed based on the feature fusion performed better than that of single feature, with the OWs2-VIs-TFs-BPNN model achieving a highest accuracy of 98.61% in detection and identification of pear leaf early anthracnose disease. Additionally, to intuitively and effectively monitor the progression and severity of anthracnose in pear leaves, the visualization of anthracnose lesions was achieved using Successive Maximum Angle Convex Cone (SMACC) and Spectral Information Divergence (SID) techniques. According to our research results, the fusion of multi-source features based on hyperspectral imaging can be a reliable method to detect early asymptomatic infection of pear leaf anthracnose, and provide scientific theoretical support for early warning and prevention of pear leaf diseases.

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management.

Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature maps. Additionally, the SPPCSPC module is optimized to preserve target area features as much as possible under different receptive fields, and the Soft-NMS algorithm is employed to reduce the likelihood of missing detections in overlapping regions. The model is trained on a diverse dataset collected from real-world field settings. Upon validation, the improved YOLOv7-E object detection algorithm achieves an average precision and recall of 91.4% and 89.8%, respectively, in inflorescence detection under various time periods, distances, and weather conditions. The detection time for a single image is 80.9 ms, and the model size is 37.6 Mb. In comparison to the original YOLOv7 algorithm, it boasts a 4.9% increase in detection accuracy and a 5.3% improvement in recall rate, with a mere 1.8% increase in model parameters. The YOLOv7-E object detection algorithm presented in this study enables precise inflorescence detection and localization across an entire tree at varying distances, offering robust technical support for differentiated and precise blossom thinning operations by thinning machinery in the future.

The intelligent diagnosis key technology of orchard nutrients provides a decision-making basis for precision fertilization, which has important research significance. This article reviewed the recent research literature, compared and analyzed existing technologies, and summarized solved and unresolved problems. It aimed to find breakthroughs to further improve the level of intelligent diagnosis key technology for orchard nutrients, and promote the implementation and application of the technology. Research had found that the current rapid nutrient detection technologies were mostly based on spectral data, with a focus on preprocessing algorithms and regression models. Hyperspectral technology shows good performance in predicting tree and soil nutrients due to its large number of characteristic variables. Meanwhile, preprocessing algorithms such as filtering, transformation, and feature band selection had also solved the problem of data redundancy. However, there were few studies for small and trace elements, and field applications. Laser breakdown-induced spectroscopy has good prospects for soil nutrient detection, as it can simultaneously detect multiple nutrients. There had been some studies on the technology for generating suitable nutrient standards for orchards in terms of soil and tree nutrients, but it requires a long and extensive experiment, which is time-consuming and laborious. A universal and rapid method needs to be studied to meet the construction needs of suitable nutrient standards for different varieties of fruit trees.

To reduce pesticide pollution and promote sustainable agricultural development in China, we designed a pilot-scale biofilter system to treat residual imidacloprid wastewater in an orchard. The biofilter system demonstrated a high rate of removal of imidacloprid from the biodegradation wastewater, with removal rates from the outlet exceeding 99% at different concentrations of pesticides. Among environmental factors, imidacloprid concentration at the inlet and biomixture significantly affected the activity of imidacloprid-degrading bacteria. The dominant microbial communities during the stable operation of the biofilter system included Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes at the phylum level and Bacillus, Methylobacter, and unclassified_f__Microbacteriaceae at the genus level. In future initiatives to improve biofilter performance and applicability, increasing attention should be paid to the dominant microbial communities, the number of biofilter units, and important environmental factors. Orchard workers in China should improve the existing treatment of residual pesticide wastewater to mitigate agricultural non-point source pollution.

As a key component of fertilization equipment, the fertilizer discharger has an important impact on the accuracy of the amount of fertilizer applied during the fertilization process. Countries around the world have been advocating for reducing the use of chemical fertilizers and improving fertilizer utilization, and researchers have also conducted in-depth research on precision fertilizer devices. In order to further improve the precision of dosing and distribution devices for fertilizers, in this study, four types of fertilizer dischargers (spiral fertilizer dischargers, groove wheel fertilizer dischargers, disc fertilizer dischargers, and air-feed fertilizer dischargers) which are currently commonly used are thoroughly analyzed. The operating principle and performance characteristics of the fertilizer dischargers are elaborated upon, and the current research status of their structure and working parameter optimization are discussed and summarized. Overall, the research of Chinese researchers is mainly described. The problems existing in research on the precise fertilizer discharge of fertilizer dischargers are pointed out, and the future development trend is predicted, aiming to provide a beneficial reference for improving the technical level of precise fertilizer discharge.

In addressing the challenges of high energy consumption and low efficiency in fertilization borehole drilling for clayey soils in southern orchards, this study utilizes the Discrete Element Method to establish a simulation model for clayey soils. Through this approach, we identify an optimal set of operational parameters that significantly reduces energy consumption. By utilizing simulation technology to model the drilling process, we analyzed the impact of rotation speed and feed rate on the torque and resistance of the drilling apparatus. Initially, this paper describes field measurements of particle parameters in soils from southern orchards. Subsequently, utilizing the Discrete Element Method and particle contact theory, we established a simulation model to represent the interactions between soil and soil, as well as soil and auger in the soil environment of the southern region. For the Southern orchard clay with a moisture content of 16.8% and a measured angle of repose of 35.55°, parameter calibration was performed. The contact model “Hertz-Mindlin with Johnson-Kendall-Roberts” was selected in EDEM. Using Design Expert, a regression model variance analysis was applied to the discrete element model parameters, leading to the determination of the optimal values for significant soil model parameters. The soil JKR surface energy is 5.85 J·m−2, with a soil–soil restitution coefficient of 0.65 and a soil–steel static friction coefficient of 0.5. Subsequently, discrete element simulation experiments on the drilling apparatus were conducted in EDEM, considering various rotation speeds and feed rates. The simulation analysis indicates that the torque consistently increases with higher rotation speeds, with a maximum relative error of 7%. The torque initially rises from zero to a maximum value, then gradually decreases to a low value, followed by a rapid increase to a higher value, and finally drops back down. This cycle repeats in the observed pattern. The total force experienced reaches its minimum average value of 200 N at a feed rate of 0.05 m/s. Simulation test results indicate that, among the three forces acting on the auger (vertical resistance, horizontal resistance, and lateral resistance), vertical resistance is the primary factor contributing to power consumption. As the rotation speed increases, the maximum value of vertical resistance continues to rise, while horizontal resistance and lateral resistance exhibit a declining trend. As the feed rate increases, the maximum values of resistance in all three directions also increase. When the feed rate exceeds 0.05 m/s, the maximum lateral resistance experiences a sharp increase. Through comprehensive analysis, the optimal operational parameters for borehole fertilization are determined to be a rotation speed of 100 r·min−1 and a feed rate of 0.05 m/s. The aim of this study is to reduce the energy consumption of borehole fertilization operations, minimize carbon emissions, and promote the sustainable development of orchard production.

Orchard thinning can avoid biennial bearing and improve fruit quality, which is a necessary agronomic section in orchard management. The existing methods of artificial fruit thinning and chemical spraying are no longer suitable for the development of modern agriculture. With the continuous acceleration of the construction process of modern orchards, blossom thinning mechanization has become an inevitable trend in the development of the orchard flower and fruit management. Based on relevant reports in the past 20 years, the paper discusses the current level of development of mechanized blossom thinning technologies and equipment in orchards from three aspects: mechanism research, machine development, and intelligent upgrading. Firstly, for thinning mechanism research, three directions were investigated: the rope flexible hitting force, thinning agronomic requirements, and the fruit tree growth model between thinning and fruit yields. Secondly, for marketable machine developments, two types of machines were investigated: the hand-held thinner and tractor-mounted thinner. The hand-held thinner is mainly suitable for traditional old orchards with a messy canopy structure, especially in the interior and top of the canopy. The tractor-mounted thinner is mainly suitable for orchards with the same crown structure, such as the hedge type, trunk type, and V-type. Thirdly, for equipment intelligent upgrading, the research of the intelligent detection algorithm for inflorescence on the fruit tree was investigated, for species including the apple, pear, citrus, grape, litchi, mango, and apricot. Finally, combining the advantages and disadvantages of the research, the authors propose thoughts and prospects, which can provide a reference for the design and applications of orchard mechanized blossom thinning.