Explore the vast range of titles available.

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR’s potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Accurate path tracking is essential for achieving intelligent operation in unmanned agricultural machinery. To address the limitations of traditional agricultural machine path tracking methods, which are susceptible to high frequency oscillations and external disturbances, this study proposes a fixed time super-twisting sliding mode adaptive path-tracking control for unmanned agricultural vehicles. The approach utilizes a Regularized Least Squares Extreme Learning Machine (RLS-ELM) to improve robustness and adaptability under certain operating conditions. A generalized terminal sliding mode surface is first designed by incorporating both lateral and heading deviations of the vehicle. Next, a Super-Twisting Sliding Mode control law is developed to perform path tracking, while the RLS-ELM is used to estimate and compensate for unknown disturbances. The stability of the proposed control system is verified through the construction of a new Lyapunov function. The control algorithm is validated via field experiments on an agricultural platform. Results show that, compared to the Fixed-Time Generalized Terminal Super Twisting control method (FGST) and the Fixed-Time Sliding Mode Controller (FTSMC), the Extreme Learning Machine-Adaptive Fixed-Time Generalized Super-Twisting (ELM-AFGST) method reduces lateral mean absolute errors by 24.5% and 27.4%, respectively, and decreases heading mean absolute errors by 5.4% and 30.8%, respectively. These findings demonstrate that the proposed path tracking method provides a solid theoretical framework for high-precision path tracking of unmanned agricultural machines.

The rapid urbanization of rural regions, along with an aging population, has resulted in a substantial manpower scarcity for agricultural output, necessitating the urgent development of highly intelligent and accurate agricultural equipment technologies. This research introduces YOLOv8-PSS, an enhanced lightweight obstacle detection model, to increase the effectiveness and safety of unmanned agricultural robots in intricate field situations. This YOLOv8-based model incorporates a depth camera to precisely identify and locate impediments in the way of autonomous agricultural equipment. Firstly, this work integrates partial convolution (PConv) into the C2f module of the backbone network to improve inference performance and minimize computing load. PConv significantly reduces processing load during convolution operations, enhancing the model's real-time detection performance. Second, a Slim-neck lightweight neck network is introduced, replacing the original neck network's conventional convolution with GSConv, to further improve detection efficiency and accuracy. This adjustment preserves accuracy while reducing the complexity of the model. After optimization, the bounding box loss function is finally upgraded to Shape-IoU (Shape Intersection over Union), which improves both model accuracy and generalization. The experimental results demonstrate that the improved YOLOv8_PSS model achieves a precision of 85.3%, a recall of 88.4%, and an average accuracy of 90.6%. Compared to the original base network, it reduces the number of parameters by 55.8%, decreases the model size by 59.5%, and lowers computational cost by 51.2%. When compared with other algorithms, such as Faster RCNN, SSD, YOLOv3-tiny, and YOLOv5, the improved model strikes an optimal balance between parameter count, computational efficiency, detection speed, and accuracy, yielding superior results. In positioning accuracy tests, the, average and maximum errors in the measured distances between the camera and typical obstacles (within a range of 2-15 meters) were 2.73% and 4.44%, respectively. The model performed effectively under real-world conditions, providing robust technical support for future research on autonomous obstacle avoidance in unmanned agricultural machinery.

The field harvesting process of harvesting machinery is often affected by high workload and environmental factors that can impede/delay manual rowing, thereby leading to lower efficiency and quality in the residual film collector. To address this challenge, an automatic rowing control system using the 4mz-220d self-propelled residual film collector as the experimental carrier was proposed in this study. Cotton stalks in the ridges were chosen as the research object, and a comprehensive application of key technologies, machinery, and electronic control was used, thereby incorporating a pure tracking model as the path-tracking control method. To achieve the automatic rowing function during the field traveling process, the fuzzy control principle was implemented to adjust the forward distance within the pure tracking model dynamically, and the expected steering angle of the steering wheel was determined based on the kinematic model of the recovery machine. The MATLAB/Simulink software was utilized to simulate and analyze the proposed model, thus achieving significant improvements in the automation level of the residual film collector. The field harvesting tests showed that the average deviation of the manual rowing was 0.144 m, while the average deviation of the automatic rowing was 0.066 m. Moreover, the average lateral deviation of the automatic rowing was reduced by 0.078 m with a probability of deviation within 0.1 m of 95.71%. The research study demonstrated that the designed automatic rowing system exhibited high stability and robustness, thereby meeting the requirements of the autonomous rowing operations of residual film collectors. The results of this study can serve as a reference for future research on autonomous navigation technology in agriculture.

Precision agriculture relies heavily on measuring grain production per unit plot, and a grain flow monitoring system performs this using a combine harvester. In response to the high cost, complex structure, and low stability of the yield monitoring system for grain combine harvesters, the objective of this research was to design a convex curved grain mass flow sensor to improve the accuracy and practicality of grain yield monitoring. In addition, it involves the development of a grain yield monitoring system based on a cut-and-flow combine harvester prototype. This research examined the real output signal of the convex curved grain mass flow sensor. Errors caused by variations in terrain were reduced by establishing the zero point of the sensor’s output. Measurement errors under different material characteristics, flow rates, and grain types were compared in indoor experiments, and the results were subsequently confirmed through field experiments. The results showed that a sensor with a cantilever beam-type elastic element and a well-constructed carrier plate may achieve a measurement error of less than 5%. After calibrating the sensor’s zero and factors, it demonstrated a measurement error of less than 5% during the operation of the combine harvester. These experimental results align with the expected results and can provide valuable technical support for the widespread adoption of impulse grain flow detection technology. In future work, the impact of factors such as vehicle vibration will be addressed, and system accuracy will be improved through structural design or adaptive filtering processing to promote the commercialization of the system.

This research work targets to evaluate the floristic composition of Mahseer National Park, Azad Jammu and Kashmir (AJK). Field data was recorded from fifteen different sites. The Quadrat method was used for vegetation sampling while the exact location of each site, altitude, exposure and geographical coordinates were documented by using geographical positioning system (GPS). To analyze the significance of environmental variables, multivariate statistical analysis was carried out by using two-way clustering, canonical correspondence analysis (CCA) and general linear model (GLM) response curve analysis. Floristically, 109 plant species belonging to 45 families were recorded. Among families, Poaceae was most commonly distributed, accounting for 15 species in total. Two-way cluster analysis categorized the vegetation into four major plant communities. CCA was used to analyze the vegetation-environment relation. Plant species showed a significant correlation response against altitudinal gradient, total nitro-gen, electrical conductivity and calcium contents. The GLM response curve and IVI demonstrated that Cynodon dactylon was the most dominant species followed by Dalbergia sissoo and Adhatoda zeylinica . This study provided the baseline information about the eco-floristic composition. It suggested that the area is floristically rich, and needs to be analyzed in detail by future researchers.

Selective catalytic conversion of ammonia to nitrogen is an effective method for reducing ammonia emissions from both stationary and mobile sources. In this study, CeO 2 -based catalysts (M/CeO 2 , M = Co, Cu, Fe, Zr) were synthesized using the sol–gel method and subsequently tested on a simulated gas experimental platform to assess their performance in NH 3 selective catalytic oxidation (NH 3 -SCO). Results showed that Co/CeO 2 and Cu/CeO 2 catalysts exhibited high ammonia oxidation activity at respectively low temperatures, with T 50 196.8 and 229.5 °C, and T 90 239.2 and 292.1 °C. However, it was observed that while Co/CeO 2 displayed poor N 2 selectivity, Cu/CeO 2 demonstrated good N 2 selectivity. The superior catalytic performance of Cu/CeO 2 and Co/CeO 2 catalysts compared to Fe/CeO 2 and Zr/CeO 2 can be attributed to their distinct interactions with Ce. Subsequent characterization experiments were conducted to elucidate these interactions. BET and SEM analyses revealed that all M/CeO 2 catalysts possessed a typical mesoporous structure. XRD and XPS results indicated that the primary phase of each catalyst was CeO 2 , and the incorporation of M transition metals did not alter the cubic fluorite structure. The interaction between the M metal and Ce varied, impacting the Ce 3+ content on the catalyst surface, which in turn influenced oxygen species adsorption and ammonia oxidation activity. H 2 -TPR and Raman spectroscopy analyses demonstrated that M metal incorporation shifted the CeO 2 reduction peak, thereby altering reduction properties and affecting oxidation performance. In particular, the Co-metal composite shifted the reduction peak to a lower temperature, thereby enhancing the reduction properties and indirectly increasing oxidation activity. Graphical Abstract

A sustainable and cost-effective alternative for environmental applications is the use of PGM-free catalysts, which are crucial for pollutant removal. This research employed the sol-gel method to synthesize stable and effective mesoporous CeCuO x mixed oxide catalysts to facilitate the oxidation of CO and C 3 H 6 emissions at lower temperatures. Additionally, pure CeO 2 and CuO were also synthesized for comparative analysis. Catalytic performance was considerably increased by doping CeO 2 with Cu, whereas pure CuO and CeO 2 showed modest activity. XRD, SEM, BET, XPS, Raman spectroscopy, H 2 -TPR, and CO-TPD showed that increasing Ce content produced a consistent pore structure without aggregation. In contrast, higher Cu concentration resulted in bulk CuO production, which led to decreased catalytic performance. In particular, the production of isolated CuO x species partly covered or clogged pores when the Cu level was above 20 wt%, which reduced the total number of effective sites for O 2 activation. Compared to other mixed and pure oxides, the ideal catalyst, Ce 7 Cu 3 Ox (T 90, CO = 178 °C), showed smaller particle size, higher specific surface area, and a larger lattice oxygen species and oxygen vacancies concentration. Additionally, it showed the best CO and C 3 H 6 conversion due to its high Ce 3+ concentration and interfacial active Cu species ratio. H 2 -TPR and CO-TPD analyses demonstrated that Ce-Cu mixed oxides with optimal Ce/Cu ratios, particularly Ce 7 Cu 3 O x and Ce 5 Cu 5 O x , achieve enhanced reducibility and balanced CO adsorption, which are critical for maximizing catalytic reactivity. The catalytic activity exhibited the following sequence: Ce 7 Cu 3 O x  > Ce 5 Cu 5 O x  > Ce 9 Cu 1 O x  > Ce 3 Cu 7 O x  > Ce 1 Cu 9 O x  > CuO > CeO 2 . This investigation offers a valuable perspective on the mechanisms of Ce-Cu interaction, which contributes to developing high-performance CeCuOx catalysts that are free of PGMs and operate under realistic reaction conditions. Graphical abstract

A sustainable and cost-effective alternative for environmental applications is the use of PGM-free catalysts, which are crucial for pollutant removal. This research employed the sol-gel method to synthesize stable and effective mesoporous CeCuO.sub.x mixed oxide catalysts to facilitate the oxidation of CO and C.sub.3H.sub.6 emissions at lower temperatures. Additionally, pure CeO.sub.2 and CuO were also synthesized for comparative analysis. Catalytic performance was considerably increased by doping CeO.sub.2 with Cu, whereas pure CuO and CeO.sub.2 showed modest activity. XRD, SEM, BET, XPS, Raman spectroscopy, H.sub.2-TPR, and CO-TPD showed that increasing Ce content produced a consistent pore structure without aggregation. In contrast, higher Cu concentration resulted in bulk CuO production, which led to decreased catalytic performance. In particular, the production of isolated CuO.sub.x species partly covered or clogged pores when the Cu level was above 20 wt%, which reduced the total number of effective sites for O.sub.2 activation. Compared to other mixed and pure oxides, the ideal catalyst, Ce.sub.7Cu.sub.3Ox (T.sub.90, CO = 178 °C), showed smaller particle size, higher specific surface area, and a larger lattice oxygen species and oxygen vacancies concentration. Additionally, it showed the best CO and C.sub.3H.sub.6 conversion due to its high Ce.sup.3+ concentration and interfacial active Cu species ratio. H.sub.2-TPR and CO-TPD analyses demonstrated that Ce-Cu mixed oxides with optimal Ce/Cu ratios, particularly Ce.sub.7Cu.sub.3O.sub.x and Ce.sub.5Cu.sub.5O.sub.x, achieve enhanced reducibility and balanced CO adsorption, which are critical for maximizing catalytic reactivity. The catalytic activity exhibited the following sequence: Ce.sub.7Cu.sub.3O.sub.x > Ce.sub.5Cu.sub.5O.sub.x > Ce.sub.9Cu.sub.1O.sub.x > Ce.sub.3Cu.sub.7O.sub.x > Ce.sub.1Cu.sub.9O.sub.x > CuO > CeO.sub.2. This investigation offers a valuable perspective on the mechanisms of Ce-Cu interaction, which contributes to developing high-performance CeCuOx catalysts that are free of PGMs and operate under realistic reaction conditions.

Selective catalytic conversion of ammonia to nitrogen is an effective method for reducing ammonia emissions from both stationary and mobile sources. In this study, CeO.sub.2-based catalysts (M/CeO.sub.2, M = Co, Cu, Fe, Zr) were synthesized using the sol-gel method and subsequently tested on a simulated gas experimental platform to assess their performance in NH.sub.3 selective catalytic oxidation (NH.sub.3-SCO). Results showed that Co/CeO.sub.2 and Cu/CeO.sub.2 catalysts exhibited high ammonia oxidation activity at respectively low temperatures, with T.sub.50 196.8 and 229.5 °C, and T.sub.90 239.2 and 292.1 °C. However, it was observed that while Co/CeO.sub.2 displayed poor N.sub.2 selectivity, Cu/CeO.sub.2 demonstrated good N.sub.2 selectivity. The superior catalytic performance of Cu/CeO.sub.2 and Co/CeO.sub.2 catalysts compared to Fe/CeO.sub.2 and Zr/CeO.sub.2 can be attributed to their distinct interactions with Ce. Subsequent characterization experiments were conducted to elucidate these interactions. BET and SEM analyses revealed that all M/CeO.sub.2 catalysts possessed a typical mesoporous structure. XRD and XPS results indicated that the primary phase of each catalyst was CeO.sub.2, and the incorporation of M transition metals did not alter the cubic fluorite structure. The interaction between the M metal and Ce varied, impacting the Ce.sup.3+ content on the catalyst surface, which in turn influenced oxygen species adsorption and ammonia oxidation activity. H.sub.2-TPR and Raman spectroscopy analyses demonstrated that M metal incorporation shifted the CeO.sub.2 reduction peak, thereby altering reduction properties and affecting oxidation performance. In particular, the Co-metal composite shifted the reduction peak to a lower temperature, thereby enhancing the reduction properties and indirectly increasing oxidation activity. Graphical