The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims to contribute to the comprehensive understanding of the application of image recognition technology in smart agriculture by investigating the scientific literature related to this technology in the last few years. We discussed and analyzed the applications of plant disease and pest detection, crop species identification, crop yield prediction, and quality assessment. Then, we made a brief introduction to its applications in soil testing and nutrient management, as well as in agricultural machinery operation quality assessment and agricultural product grading. At last, the challenges and the emerging trends of image recognition technology were summarized. The results indicated that the models used in image recognition technology face challenges such as limited generalization, real-time processing, and insufficient dataset diversity. Transfer learning and green Artificial Intelligence (AI) offer promising solutions to these issues by reducing the reliance on large datasets and minimizing computational resource consumption. Advanced technologies like transformers further enhance the adaptability and accuracy of image recognition in smart agriculture. This comprehensive review provides valuable information on the current state of image recognition technology in smart agriculture and prospective future opportunities.

Proper straw cover information is one of the most important inputs for agroecosystem and environmental modeling, but the availability of accurate information remains limited. However, several remote-sensing (RS)-based studies have provided a residue cover estimation and provided spatial distribution mapping of paddy rice areas in a constant field condition. Despite this, the performance of rice crops with straw applications has received little attention. Furthermore, there are no methods currently available to quantify the wheat straw cover (WSC) percentage and its effect on rice crops in the rice-wheat cropping region on a large scale and a continuous basis. The novel approach proposed in this study demonstrates that the Landsat satellite data and seven RS-based indices, e.g., (i) normalized difference vegetation index (NDVI), (ii) Normalized difference senescent vegetation index (NDSVI), (iii) Normalized difference index 5 (NDI5), (iv) Normalized difference index 7 (NDI7), (v) Simple tillage index (STI), (vi) Normalized difference tillage index (NDTI), and (vii) Shortwave red normalized difference index (SRNDI), can be used to estimate the WSC percentage and determine the performance of rice crops over the study area in Changshu county, China. The regression model shows that the NDTI index performed better in differentiating the WSC at sampling points with a coefficient of determination (R² = 0.80) and root mean squared difference (RMSD = 8.46%) compared to that of other indices, whereas the overall accuracy for mapping WSC was observed to be 84.61% and the kappa coefficient was κ = 0.76. Moreover, the rice yield model was established by correlating between the peak NDVI values and rice grain yield collected from ground census data, with R² = 0.85. The finding also revealed that the highest estimated yield (8439.67 kg/ha) was recorded with 68% WCS in the study region. This study confirmed that the NDVI and NDTI algorithms are very effective and robust indicators. Also, it can be strongly concluded that multispectral Landsat satellite imagery is capable of measuring the WSC percentage and successively determines the impact of different WSC percentages on rice crop yield within fields or across large regions through remote sensing (RS) and geographical information system (GIS) techniques for the long-term planning of agriculture sustainability in rice-wheat cropping systems.

A tractor electro-hydraulic hitch system is considered one of the most important systems that play a strategic role in the power transmission and operation depth control of a tractor’s field operation. Its performance directly affects the operation quality of the whole work unit of the tractor. Furthermore, a tractor electro-hydraulic hitch system has gained the interest of many in the agricultural machinery sector because of its stable performance, high production efficiency, good operation quality and its low energy consumption. To fully benefit from the potential of the tractor electro-hydraulic hitch system, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of the tractor electro-hydraulic hitch system by investigating the research methods, technical characteristics and emerging trends in three key aspects that include the tillage depth adjustment method, the tillage depth control algorithm and the core components of the electro-hydraulic hitch system. The characteristics and applicable conditions of the different tillage depth adjustment methods of the electro-hydraulic hitch system were summarized. The realization methods and the control characteristics of the different algorithms were elaborated and discussed for both the PID control algorithm and the intelligent control algorithm. The working characteristics of the core components of the electro-hydraulic hitch system were analyzed based on the hydraulic control valves and sensing elements. The results have shown that the multi-parameter tillage depth adjustment method met the operation quality standard while taking the engine load stability and traction efficiency into account, and it has a greater research significance and value. The working quality can be improved effectively by introducing the intelligent algorithm. In addition, the study of smart valves with built-in sensing elements and how to improve the anti-interference ability of sensing elements, are the aspects that requires further consideration. Aiming to improve the working quality and reduce energy consumption, further research into the tractor electro-hydraulic hitch system is necessary. The results of this comprehensive review provide a reference for the intelligent operation of tractors under the precision agriculture.

To address the insufficient accuracy of traditional single-sensor navigation methods in dense planting environments of pomegranate orchards, this paper proposes a vision and LiDAR fusion-based navigation line extraction method for orchard environments. The proposed method integrates a YOLOv8-ResCBAM trunk detection model, a reverse ray projection fusion algorithm, and geometric constraint-based navigation line fitting techniques. The object detection model enables high-precision real-time detection of pomegranate tree trunks. A reverse ray projection algorithm is proposed to convert pixel coordinates from visual detection into three-dimensional rays and compute their intersections with LiDAR scanning planes, achieving effective association between visual and LiDAR data. Finally, geometric constraints are introduced to improve the RANSAC algorithm for navigation line fitting, combined with Kalman filtering techniques to reduce navigation line fluctuations. Field experiments demonstrate that the proposed fusion-based navigation method improves navigation accuracy over single-sensor methods and semantic-segmentation methods, reducing the average lateral error to 5.2 cm, yielding an average lateral error RMS of 6.6 cm, and achieving a navigation success rate of 95.4%. These results validate the effectiveness of the vision and 2D LiDAR fusion-based approach in complex orchard environments and provide a viable route toward autonomous navigation for orchard robots.

Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid obstacles due to flight altitude limitations. However, existing UAV spray systems do not spray when in obstacle neighborhoods during obstacle avoidance, resulting in insufficient droplet coverage and reduced plant protection quality in the area. To improve the droplet coverage in obstacle neighborhoods, this article carries out a study of side spray technology with an electric quadrotor UAV, and proposes the design and development of a side spray device. The relationship between the obstacle avoidance path of the UAV and the spray pattern of the side spray device and their effect on droplet coverage in obstacle neighborhoods was explored. An accurate measurement method of the relative position between the UAV and obstacles was proposed. Spray angle calculations and nozzle selection for the side spray device were carried out in conjunction with the relative position. A rotor wind field simulation model was designed based on the lattice Boltzmann method (LBM), and the spatial layout of the side spray device on the UAV was designed based on the simulation results. To explore suitable spray patterns for the side spray device, comparative experiments of droplet coverage in obstacle neighborhoods were carried out under different environments, spray patterns, and flight parameter combinations. The relationship between the flight parameter combinations and the distribution uniformity of droplets and the effective swath width of the side spray device was explored. The experimental results were analyzed by an analysis of variance (ANOVA) and a relationship model was obtained. The results showed that the side spray device can effectively improve droplet coverage in obstacle neighborhoods compared to a device without side spray using the same flight parameter combinations. The effective swath width in obstacle neighborhoods can be increased by a minimum of 6.35%, maximum of 35.32%, and average of 15.25% using the side spray device. The error between the predicted values of the relational model and the field experiment results was less than 15%. The results verify the effectiveness and rationality of the method proposed in this article. This study can provide technical and theoretical references for improving the plant protection quality of UAVs in obstacle environments.

Accurate positioning is a necessary prerequisite for the realization of intelligent and autonomous mining. Although most research efforts have focused on localization techniques, these methods are incapable of producing a sufficiently high and reliable location estimation accuracy in harsh underground coal mine environments. To enhance the positioning accuracy of the target node (TN), this article proposes an innovative method denoted as MCVBUKF-RTS-ALO by integrating the maximum correntropy unscented Kalman filter (MCUKF), variational Bayesian (VB) methodology, Rauch–Tung–Striebel (RTS) smoother, and ant lion optimizer (ALO) algorithm. First, the MCVBUKF-RTS method is proposed, taking into account complex measurement noise and abnormal measurement data to enhance the ranging accuracy due to the existence of inevitable uncertainties during practical implementation. In particular, the MCVBUKF is performed during the application of the RTS smoother for the forward filtering and backward smoothing to alleviate the influence of corrupted measurements. Following this, the robust weight total least squares is adopted to estimate the TN’s location, and the ALO is subsequently performed to further optimize the estimated results. An experimental investigation was implemented to validate the practicability and effectiveness of the designed method using the ultra-wideband (UWB) system. The experimental results demonstrate that the designed MCVBUKF-RTS-ALO method can greatly improve the positioning accuracy of the UWB system and substantially outperforms the other state-of-the-art-methods.