Since its introduction in 2014, the LiDAR odometry and mapping (LOAM) algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics. LOAM provides robust support for autonomous navigation in complex dynamic environments through precise localization and environmental mapping. This paper offers a comprehensive review of the innovations and optimizations made to the LOAM algorithm, covering advancements in multi-sensor fusion technology, frontend processing optimization, backend optimization, and loop closure detection. These improvements have significantly enhanced LOAM's performance in various scenarios, including urban, agricultural, and underground environments. However, challenges remain in areas such as data synchronization, real-time processing, computational complexity, and environmental adaptability. Looking ahead, future developments are expected to focus on creating more efficient multi-sensor fusion algorithms, expanding application domains, and building more robust systems, thereby driving continued progress in autonomous driving, intelligent robotics, and autonomous unmanned systems.

Accurately extracting the canopies of fruit trees is crucial to improve the estimation accuracy of CNC inversion as well as determine a reasonable application of nitrogen fertilizer. To date, existing studies have mainly focused on canopy extraction in scenarios with no grass or sparse grass cover, paying less attention to scenarios with a full grass cover. Thus, in this paper, a two-stage canopy extraction (TCE) method was proposed to address the issue of canopy extraction in scenarios with full grass cover. Firstly, the height difference between the canopies of pear trees and the ground grass was used to eliminate the interference of the ground grass and achieve a coarse-grained canopy extraction. Then, based on the extracted coarse-grained canopies and CIELAB color space, the color thresholds of the L*, a*, and b* channels were determined to remove the interference factors, e.g., branches, shadows, and trellises, for fine-grained canopy extraction by using data distribution from the three channels based on a histogram and the threshold of confidence interval. In canopy extraction experiments, the accuracy, recall, precision, and F1-score of TCE in scenarios with full grass cover can reach 91.725%, 95.789%, 91.284%, and 93.482%, respectively, demonstrating the effectiveness of TCE in addressing canopy extraction issues in this scenario. Thirdly, the RF algorithm was utilized to select suitable VIs based on R2 and RMSE values, and CNC inversion models were constructed. In estimation experiments on CNC inversion, the R2, RMSE, and nRMSE of the constructed CNC inversion based on TCE in a scenario with full grass cover were 0.724, 0.243, and 19.120%, respectively. A comparative analysis with the baseline method revealed that accurate canopy extraction contributed to a high estimation accuracy of CNC inversion. Therefore, our proposed method can provide technical support for the efficient and non-destructive monitoring of the canopy nutrient status in pear orchards.

Three-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processing, posing challenges to real-time navigation. To address these issues, this paper proposes a 3D point cloud optimization method based on the octree data structure for autonomous navigation of orchard mobile robots. This approach includes two key components: 1) In terms of orchard mapping, the spatial indexing and segmentation features of the octree data structure are introduced. According to the sparsity and density of the point cloud, the 3D orchard map is adaptively divided and the key information of the orchard is retained. 2) In terms of path planning, by using octree nodes as the unit nodes for RRT* random tree expansion, an improved RRT* algorithm based on octree is proposed. Field experiments were conducted in a pear orchard based on this method. The experimental results show that: 1) The overall number of point cloud data points in the map was reduced by approximately 76.32%, while important features, including tree morphology, trellis structure, and road surface information, were fully preserved. 2) When different octree node resolutions were applied, the improved RRT* algorithm demonstrated significant improvements in path generation time, sampling point utilization, path length, and curvature. The lateral tracking error increased as the resolution of octree nodes decreased. At a resolution of 0.20 m, the maximum average lateral tracking error was 0.079 m, indicating strong path trackability. This method exhibits tremendous potential for processing large-scale 3D point cloud data and enhancing path planning efficiency, providing a valuable technical reference for the real-time autonomous navigation of mobile robots in complex orchard environments.

Accurate and rapid monitoring canopy-scale nitrogen content (CNC) on pear trees is crucial for precise application of nitrogen fertilizer. Unmanned Aerial Vehicle (UAV)-based spectral analysis is becoming a promising solution for fast monitoring plant nutrition. However, complex data collection conditions, e.g., unpredictable local microclimate, in orchards could easily compromise the quality of spectral images, thereby affecting the estimation accuracy of CNC inversion model. This study aimed to enhance the quality of canopy-scale raw spectral images to improve the accuracy of CNC inversion through the fusion of ground-space spectral imagery. Firstly, collected leaf-scale hyperspectral images, i.e., spectral reflectance and color data, were used as reference values to enhance the quality of canopy-scale raw multispectral images through constructing mapping models based on machine learning algorithms. The conversion of spectral reflectance and color data between leaf-scale and canopy-scale were conducted using the 4SAIL model and the CIELAB color space, respectively. Then, according to the accuracy of mapping models from four classic machine learning algorithms, the RF algorithm was the optimal choice for constructing CNC inversion models. Furthermore, 10 Vegetation Indexes (VIs), 6 Color Indexes (CIs), and their combinations were analyzed using fitting models with simulated canopy-scale spectral reflectance and leaf-scale color data. Based on the top three R2 and RMSE values in each type of model, CNC inversion models were constructed using single VI, single CI, and combinations of VIs and CIs. Meanwhile, four methods were tested in each inversion model. The experimental results showed that the R2 and RMSE values of the models using mapped data were averagely improved 0.066 and 0.006, respectively, compared to those using raw canopy reflectance and color data. Among all the inversion models using the mapped data, the combination 1 (C1) inversion model (7 VIs and 2 CIs) performed the best, with R2, RMSE, nRMSE, and MAE values reaching 0.832, 0.155, 8.333%, and 0.152, respectively. Finally, compared to the C1 inversion model, by screening the inversion results from multi model, the R2 of CNC inversion model increased 0.089, enhancing to 0.921. Meanwhile, the RMSE, nRMSE, and MAE decreased 0.038, 2.043%, and 0.072. reaching 0.117, 6.290%, and 0.080, respectively. This study effectively improved the accuracy of CNC inversion by the fusion of ground-space spectral imagery and can offer reference for the application of nitrogen fertilizer in pear orchards.

As the largest fruit producer in the world, China's comprehensive orchard mechanization rate is below 30% and faces the problem of aging orchard farmers. Thinning is an essential agronomic practice in orchard management. Therefore, to get a marketable product, artificial hand fruit thinning (AHFT) has become a major but costly management practice in modern orchard planting. The authors developed two types of new orchard blossom thinners: a tractor-mounted three-arm blossom thinner (TTBT) and a hand-held electric blossom thinner (HEBT). The arm shape, spindle rotation speed, and rope arrangement density of TTBT can be adjusted flexibly according to the canopy structure of the fruit tree. HEBT is portable and suitable for different canopy types, especially for traditional orchards with a complex-structured canopy. In this paper, a performance evaluation of the two types of blossom thinners on Y-trellis 'Sucui' No.1 pear orchard was carried out. In field tests, three treatments were designed and tested, which are TTBT combined with AHFT, HEBT combined with AHFT, and AHFT only. Four indices were used to evaluate the tests: blossom retention rate, fruit setting rate, fruit yield and quality, and work efficiency and cost. The test results showed that the blossom retention rate of TTBT and HEBT at 50% for Y-trellis 'Sucui' No.1 pear orchard was perfect; the difference in blossom retention rate and coefficient of variation of every layer of TTBT was very small, and the mean coefficient of variation was 2.97%, which is 1.98% lower than that of HEBT, meaning that the working stability of TTBT was higher than HEBT. The working efficiencies of TTBT and HEBT were much higher than that of AHFT, specifically, 130 and seven times higher, respectively. Although mechanical blossom thinning reduces the fruit setting rate to a certain extent, it has no effect on fruit yield and quality after fruit thinning for final marketable fruit. The profitable areas of TTBT and HEBT were 0.87 hm(2 )and 0.08 hm(2 ), respectively.

In modern orchard management, precision agriculture plays a crucial role. However, traditional methods for assessing fruit tree growth often suffer from limitations such as insufficient accuracy and low efficiency. To address these challenges, this study proposes a precise monitoring approach for fruit tree growth by integrating 3D LiDAR SLAM (Simultaneous Localization and Mapping) with point cloud optimization. A mobile robot equipped with a LiDAR sensor performs high-precision environmental scanning within a structured orchard, utilizing SLAM algorithms to generate a three-dimensional orchard map and construct detailed 3D models of trees. Based on these models, key parameters such as trunk diameter at breast height, tree height, and branch dimensions are quantitatively extracted. Experimental results indicate that the proposed method achieves measurement errors of less than 3% for both trunk diameter and tree height, demonstrating strong adaptability to different environments and high measurement efficiency. Compared to traditional manual measurements, this approach effectively overcomes challenges in complex orchard scenarios, providing reliable technical support for fruit tree growth monitoring and scientific management. Furthermore, this study lays a solid foundation for the integration and advancement of 3D LiDAR SLAM technology in precision agriculture.

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.

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.

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.

Solar insecticidal lamps Internet of Things (SIL-IoT) is a new green prevention and control technology for pest management. In the implementation of SIL-IoT to large-scale regions, two practical issues remain to be solved, that is: 1) scheduling the cleaning tasks of SILs periodically and 2) minimizing the insecticidal efficiency reduction over time. As smartphones are widely available among farmers across the globe, mobile crowdsensing (MCS) for agricultural data collection becomes a cost-effective and efficient solution by integrating participatory sensing based on a large group of individuals. This article proposes an MCS-enabled framework to address the SIL maintenance problem (SILMP) and perform system analysis by considering both the partition structure of farmland and the insecticidal efficiency of SILs. In addition, considering the farmland's practical natural geographical features, we propose dividing the regions of interest into numerous subareas, where each subarea can be considered a separate partition. Finally, we formulate the SILMP framework as two subproblems, i.e., path planning and task selection, and propose two different methods to tackle each problem based on the concept of greedy algorithm. Simulation results show that our proposed methods have improved performance in the tradeoff between task cost and insecticidal efficiency and outperform the three selected baseline algorithms.

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem.

Highlights Lotus seed coats are often peeled using water jets, which has not been studied in-depth. A water jet-based lotus seed peeling system is proposed and investigated in detail. Response surface method experiment was used to enhance peeling-machine performance. Proposed system performs well in terms of cost, stability, and percentage of seed peeled and damaged. Abstract. Lotus seed coats are often peeled using water jets. However, currently, existing fresh lotus seed peeling machines could process lotus seeds only if their size differences are small. Due to a lack of research on water jet operation parameters, the peeling process is often associated with a low percentage of seed peeled (PSP) and a high percentage of seed damage (PSD). A new type of water jet-based fresh lotus seed peeling machine was designed and tested to solve the aforementioned problems. Three-factor, three-level response surface method (RSM) was used in experiments to enhance peeling-machine performance, and quadratic polynomial regression equations were obtained via analysis of the experimental data. Results show that these independent variables had significant effects on PSP and PSD: water jet pressure (X1), water jet angle (X2), and processing speed (X3). Specifically, X1 and X2 had an interactive effect on PSD, whereas X1 and X3 both had a strong interactive effect on PSP and PSD. The optimal parameters were a water jet pressure of 0.60 MPa, water jet angle of 20°, and processing speed of 62 rpm, leading to predicted PSP and PSD of 93.13% and 1.65%, respectively (similar to the validation experimental PSP and PSD of 93.89% and 1.76%, respectively); thus, proving the accuracy of the RSM model. In general, the proposed peeling machine provided a high machine capacity (19.65 kg/h), a high percentage of seed peeled, and a low percentage of seed damaged, and all performance indices fulfilled the requirements for use. Keywords: Lotus seed, Parameter optimization, Peeling, Water jet.

以麦秸与鸡粪为原料,采用批次式中温厌氧发酵方式,探究微量元素Fe2+、Co2+、Ni2+对混合厌氧发酵的影响.实验结果表明:Fe2+、Co2+、Ni2+最佳浓度分别为210、32、32 mg/L,当Fe2+、Co2+、Ni2+的添加浓度在适宜范围内时,可显著(p<0.05)提高发酵系统产CH4潜力及CODs去除率;当Fe2+浓度为210 mg/L、Co2+浓度为32 mg/L、Ni2+浓度为32 mg/L时,其CH4累积产量分别为322.21、331.19、357.10 mL/(g VS),相比于对照组为245.35 mL/(g VS)显著(p<0.05)提高31.33％、34.99％、45.54％,CODs去除率依次为61.83％、57.01％、63.63％,提高CH4产量的排序为Ni2+>Co2+>Fe2+,Ni2+对CH4累积产量的提高最明显,同时也有最高的CODs去除率,且各实验组pH值均稳定在适宜范围内,厌氧发酵稳定运行,可为麦秸与家禽粪便的沼气工程提供技术参考.

为了进一步提高稠密标签环境中标签估计算法的精度,在分析比较传统的基于比特标签估计算法的基础上,提出一种比特估计的优化算法.首先,基于二项分布理论,利用未被选择比特位的观测值计算空闲比特率;然后,通过确定空闲比特率的阈值,建立稠密标签环境中的标签数量估计模型;最后,推导出标签数量估计值与时隙消耗的数学表达式.仿真结果表明,改进算法的标签估计精度要优于传统的基于比特标签估计的精度,且对于不同规模的标签群,改进算法具有稳定的估计性能.

In the poster, a new paradigm of smart farming is proposed for the first time. It is named as Photovoltaic Agricultural Internet of Things (PAIoT). We envision the scenarios of PAIoT in terms of energy supply, communication method and computing models. In addition, PAIoT’s advantages are presented. Finally, the open research issues are discussed for the PAIoT.

A new paradigm of mobile crowd sensing towards smart agriculture is termed Agricultural Mobile Crowd Sensing (AMCS). The AMCS is intended to improve the existing agricultural data collection system and facilitate the realization of smart agriculture. The comparative analysis between Space-Air-Ground Integrated Network and the AMCS is presented, regarding the cost, range of data collection, scalability, data granularity and flexibility. Finally, the conclusions and prospects are discussed.

太阳能杀虫灯在农业趋光性害虫受灯光引诱并接触金属网时释放高压脉冲电流杀灭害虫,可有效减少施用农药造成的环境污染和食品安全问题.本文介绍了利用无线传感器网络技术提升太阳能杀虫灯在农业迁飞性趋光害虫防治领域的应用效果,明确提出了一种新型农业物联网——太阳能杀虫灯物联网.首先,从杀虫灯在国内农业生产中的应用研究现状出发,总结了杀虫灯在林果、 水稻和蔬菜等作物生产种植中的部署特点和杀虫工作时段分布情况;其次,分析了现有联网型太阳能杀虫灯节点的产品特点和杀虫灯物联网研究现状;然后,结合太阳能杀虫灯的能量采集方式、 田间部署特点,综合分析了基于太阳能能量采集方式的传感器网络研究现状和基于启发式的传感器网络节点部署研究现状;最后,探讨了太阳能杀虫灯物联网的节点部署、 能量预留管理、 虫害爆发区域边界定位、 虫情数据抗干扰传输等关键研究问题,并对太阳能杀虫灯物联网在农业生产中的应用进行了总结和展望.

为了解决犁旋一体机作业过程中调节机具问题,设计了一种犁旋一体机自动调平系统,该系统包括执行机构、控制系统、液压系统.根据犁旋一体机自身的特点,提出了一种确定调平角度范围的方法,并根据实际田间作业情况,运用EDEM仿真软件进行田间作业的虚拟仿真,仿真结果表明:地表平整度小于2 cm,满足农艺要求.在设计和仿真的基础上,进行田间试验,将手动调平的犁旋一体机的作业情况和自动调平的犁旋一体机的作业情况进行对照,分析了作业过程中机具的角度变化和作业后的耕深及其稳定性,地表平整度.结果表明:自动调平犁旋一体机相对于手动调平犁旋一体机,在耕深的稳定性和耕后地表平整度上有较为明显的提高,前者耕深稳定系数达到87.31％,后者为84.76％.前者地表平整度为1.97 cm,后者为2.56 cm.