Tomato picking is a time-consuming and laborious work. The use of intelligent equipment of picking instead of manual picking can improve the production efficiency. The end-effector is an important element in direct contact with tomato fruit, and it is the key to realize automatic tomato harvest. This paper introduces a rigid-flexible coupling end-effector with a telescopic pneumatic sucker. The end-effector first extends the vacuum sucker of the adhesion mechanism to hold the target tomato and pull it out for a certain distance, and then grips the tomato with a clamping component. The target tomato picking operation was completed through the movement mode of spiral and pull combination. The physical characteristics of tomato and the mechanical characteristics of fruit stem were investigated, aiming at providing a solid theoretical basis for the design and mechanical analysis of end-effector. Then, the stability of suction and pulling force in the process of holding and pulling tomato were analyzed, so as to clarify the specifications of suction and picking parts. Finally, a composite force analysis of the adhesion mechanism and the holding mechanism was undertaken to achieve the mechanical design goal of the tomato adhesion and picking movement process. The picking performance test of the end-effector showed that the picking time of single fruit was about 5.4s and the success rate of picking could reach 88%. This study provides sufficient theoretical basis for the development of tomato picking robot and the design of end-effector.

Understanding the genetic basis of agronomic, morphological and physiological traits in popcorn is key to developing effective breeding strategies under water-limited conditions. This study evaluated additive and dominance effects on 25 traits in 10 S7 inbred lines and their 45 diallel hybrids under water-stressed and well-watered conditions. Water stress was applied 15 days before male flowering by ceasing irrigation. Significant genetic variability was observed, with reductions in grain mass and popping expansion under water stress. Normalized difference vegetation index and relative chlorophyll content effectively detected phenotypic differences during critical growth stages, while canopy temperature depression provided insights into stomatal closure. Some genotypes possessed greater drought resilience, maintaining high chlorophyll levels, associated with the stay-green trait, extending active photosynthesis and increasing biomass accumulation. Dominance effects were predominant for most traits, except for popping expansion and stem diameter, where additive effects were slightly higher under both water regimes. Lines L76, L61 and P3 had high potential for grain yield and drought tolerance. Hybrids L61 × L76 and L71 × L76 performed well under both watering treatments, underscoring the role of heterosis due to dominant allelic interactions. This research highlights the importance of exploring genetic variation for yield and drought-related traits, offering insights for developing popcorn cultivars resilient to water stress.

The synergistic control of livestock carbon emissions and environmental pollution (SCLCP) is essential for the sustainable development of animal husbandry. Using panel data from the provincial level in China from 2011 to 2021, this study empirically examined the effect of the rural digital economy on livestock carbon emissions and pollution mitigation. Four key findings are given. First, the rural digital economic significantly facilitates the synergistic control of livestock carbon emissions and pollution, with robustness confirmed through instrumental variable approaches and other robustness tests. Second, mechanism analysis revealed that the rural digital economy can promote SCLCP through green technology progress, resource allocation efficiency improvement and production agglomeration. Third, heterogeneity analysis indicates that government support would further strengthen the effect of the rural digital economy on SCLCP, and this impact mainly occurs in agricultural zone and agropastoral transitional zone. In contrast, marketization weakens this effect in agricultural zone. Finally, spatial econometric analysis demonstrated that the rural digital economy can reduce livestock carbon emissions in neighboring areas, with marketization exerting a positive moderating effect, while government support had no significant moderating effect. Additionally, the spatial spillover effect of the rural digital economy on livestock-related environmental pollution was not significant.

Agricultural green development (AGD) is an essential process for achieving sustainable agricultural development. Among the challenges encountered in transitioning to green agriculture, agricultural non-point source pollution (ANPSP) is a primary concern. The environmentally friendly practices advocated by AGD promote the reduction of ANPSP by controlling the emission and spread of pollutants. This paper provides a comprehensive overview of the current status of ANPSP, examining its causes from the perspectives of spatiotemporal dynamics, pollutant tracking and control mechanisms. Additionally, it offers targeted recommendations for the prevention and control of ANPSP, focusing on pollutant tracing, integrated technologies, the application of artificial intelligence and national policies. By systematically reviewing key advances in ANPSP research and governance, this paper provides both theoretical insights and practical suggestions for the development of efficient and sustainable prevention and control systems for ANPSP.

Currently, the main optimization of pneumatic conveying seed-discharge systems targets distributors, ignoring the important role of pipelines. In this study, simulations were used to determine an improved structure of the conveying pipe. The horizontal pipe was determined to be a 300 mm straight pipe, which was connected under the vertical bellows as a transition. This study adopted the L₉(3⁴) orthogonal test method to optimize the bellows parameters, using as index the coefficient of variation of the uniformity of the bellow outlets. The indoor bench test was designed to validate different flow rate and bellows length combinations. The optimal bellows parameter combination of the corrugated circle had a radius of 8 mm, bellows length of 500 mm, corrugation distance of 40 mm and corrugation length of 16 mm, achieving a 3.46% coefficient of variation of the particle distribution in the plumbing. Under various particle mass flow rates (33.5, 67.0, 101 g·s⁻¹), the bellows length of the optimal solution was verified to be in the interval of 400−500 mm, and the consistency coefficients of the variations in row displacements of the system satisfied the requirements. This study provides guidance for the design and application of piping in pneumatic seed-discharge systems.

The integration of augmented reality (AR) into embedded agricultural systems is reshaping precision farming by enabling real-time visualizations and interactions with complex environmental data. In the face of mounting global pressures, from climate variability to resource constraints and food system demands, AR-enhanced platforms present a promising pathway toward more efficient farming practices. However, existing research has predominantly treated AR as a standalone tool, overlooking its potential to link and enable the functional integration of diverse embedded technologies. Therefore, the objective of the present review is to investigate how AR visualization can integrate data from Internet of Things devices, unmanned aerial vehicles, farming machinery, robotics, edge computing platforms and artificial intelligence (AI) to enable their coordinated, field-level deployment in precision agriculture. The article offers three primary contributions: a structured synthesis of AR applications across embedded systems, a conceptual architecture for AR-centered smart farming and an integrated analysis of research gaps and future directions. Key research gaps include the lack of studies addressing model interpretability and system interoperability, insufficient exploration of real-time edge AI processing and gesture-based AR controls, and the absence of globally representative data sets for AI image analysis. Future research directions include the development of low-latency data pipelines, explainable AI interfaces, swarm-capable Drone-AR systems, energy-efficient edge AI models, federated learning for data privacy and participatory design strategies tailored for resource-limited contexts. These findings offer valuable insights for researchers, technology developers, policymakers and farmers working to implement scalable, secure and accessible AR-powered agricultural solutions.

To address challenges in crop grasping tasks for agricultural robots, specifically, poor crop background segmentation and limited adaptability in grasp point localization, this paper proposes a saliency guided segmentation approach. This method improves both object recognition and grasp point detection, thereby optimizing robot grasping performance and increasing success rates, even under complex environmental conditions. The proposed network uses a boundary aware detection strategy built on an encoder decoder architecture with an improvement module. First, standard convolutions are replaced by dynamic convolution to improve feature representation. Second, a Haar wavelet downsampling module is introduced to improve multi scale feature extraction. Finally, the standard squeeze and excitation attention block is improved with edge enhancement, which is embedded at each decoding stage to emphasize boundary information. In benchmark tests, the proposed model achieved a mean absolute error of 10.9%, with F-, E- and S-measures of 97.0%, 98.4%, and 96.8%, respectively. When deployed on an agricultural robot platform, it achieved a 78.0% grasping success rate, processing images at 35 frames per second. These results demonstrate that the proposed network reliably identifies and localizes optimal grasp points under real world conditions.

China has made considerable effort to address methane emissions in the agricultural sector. This paper analyzes the trends in China’s agricultural methane emissions using national greenhouse gas inventory data from 1994 to 2021, identifies key emission sources and reviews relevant policies, while summarizing the practical challenges currently faced in mitigation efforts. The findings reveal that China’s agricultural methane emissions remain high. Although a turning point emerged in 2017, emissions rebounded slightly in 2021 with the recovery of pork production. Rice production, enteric fermentation and manure management are the key agricultural methane emission sources. China has made progress in reducing agricultural methane emissions by integrating climate change policies with green agricultural development initiatives. However, the implementation of these policies must overcome several challenges. The growing food demand will further intensify the pressure on methane reduction. Low adoption rates of existing technologies and limited development of innovative solutions hinder progress toward emission reduction targets. The measurement, reporting and verification (MRV) system remains inadequately developed. Inadequate policy support and financial incentives compromise the sustainability of current efforts. This paper proposes several pathways to promote agricultural methane reduction and support the transition to low-carbon agricultural development. These include strengthening the MRV system, enhancing policy and financial support for emission reduction, advancing research and development, establishing compensation mechanisms for emission reduction, encouraging low-carbon and healthy dietary habits among consumers and strengthening international cooperation.

The rapid spread of animal diseases and the evolution of associated pathogens underscore the urgent need for improved diagnostic techniques. Established nucleic acid detection methods typically rely on expensive and complex machinery, which requires specialized expertise and is time-consuming to operate. As a result, these methods are not well-suited for the monitoring and preliminary screening of epidemics in highly-intensive livestock operations. Therefore, there is a pressing need for the development of on-site rapid nucleic acid detection technologies that offer both high sensitivity and specificity. The clustered regularly interspaced short palindromic repeats and associated proteins (CRISPR-Cas) system is notable for its simplicity, precision and high-efficiency gene-editing capabilities. Recent investigations into CRISPR-Cas-based nucleic acid detection methods have demonstrated considerable potential for advancing diagnostic technology in this field. This paper provides a comprehensive review of CRISPR-Cas-based nucleic acid detection principles and their application in diagnosing animal diseases. It aims to serve as a valuable reference for researchers and practitioners involved in the development and implementation of CRISPR-Cas technologies for animal pathogen detection.

Nitrous oxide (N₂O) is a potent greenhouse gas with about 60% of its emissions are attributed to agricultural activities. Its fluxes are influenced by a range of crop-specific factors, such as nitrogenous fertilizer inputs, soil N availability, tillage practices, temperature, pH and soil moisture. These factors interact in complex, nonlinear ways, creating the need for predictive modeling of N₂O emissions to both improve understanding and estimation and identify mitigating strategies. This proposes proposes data-driven machine learning techniques, particularly multilayer perceptron and random forest (RF) algorithms, for estimating soil N₂O fluxes in a sugarcane plantation under different irrigation regimes and to contrast machine learning results with conventional analytical methods. The findings indicate that RF modeling achieved a coefficient of determination of 87.4% for N₂O emission prediction, and identified ammonium, nitrogen nitrate, soil temperature, and water-filled pore space as the most influential predictors, in that order. The results open new possibilities for integrating machine learning to study N₂O fluxes in sugarcane and other major crops. All data and code used in this study are provided openly to support further research.