Abstract Grasping, carrying and placing of objects are the fundamental capabilities and common operations for robots and robotic manipulators. Grippers are the most essential components of robots and play an important role in many manipulation tasks, since they serve as the end-of-arm tools, as well as the mechanical interface between robots and environments/grasped objects. Gripper developments are motivated by the great number of different requirements, diverse workpieces and the desire for well adapted and reliable systems. Grippers provide temporary contact with the grasped objects in manipulations. Secure grasping not only requires contacting the objects, but also avoiding the risk of potential slip and damage while the objects are picked and placed. To offer secure grasping for objects with a wide variety of shapes, sizes and materials, various sensors and control strategies are also needed. With the developments of technologies, labor shortage caused by the population aging, as well as the requirements of high automation degree, agricultural robots will find their increasing applications in agricultural and food industries. As the end-of-arm tools for the robots, grippers can be seen as the hands of robots, almost all automatic manipulations are conducted directly by robotic grippers. This paper gives a detailed summary about the state-of-the-art robotic grippers, grasping and sensor-based control methods, as well as their applications in robotic agricultural tasks and food industries. Different from workpiece in industrial environment, agricultural products are fragile and damageable. The requirement for grasping agricultural products is higher than that of grasping of industrial workpieces, various sensors are needed to be installed to the grippers to make them less aggressive, and more flexible and controllable. Therefore, particular attention has been paid to the sensors that used in the grippers to improve their sensing and grasping capabilities. The advantages and disadvantages of the grippers are discussed and summarized. Finally, the challenges and potential future trends of grippers in agricultural robots are reported.
BackgroundIn agricultural-dependent economies, extension programmes have been the main conduit for disseminating information on farm technologies, support rural adult learning and assist farmers in developing their farm technical and managerial skills. It is expected that extension programmes will help increase farm productivity, farm revenue, reduce poverty and minimize food insecurity. In this study, we estimate the effects of extension services on farm productivity and income with particular reference to agricultural extension services delivered by Association of Church-based Development NGOs (ACDEP).MethodsThe study used cross-sectional data collected from 200 farm households from two districts in the Northern region of Ghana. The robustness of the estimates was tested by the use of regression on covariates, regression on propensity scores and Heckman treatment effect model.ResultsThe study found positive economic gains from participating in the ACDEP agricultural extension programmes. Apart from the primary variable of interest (ACDEP agricultural extension programme), socio-economic, institutional and farm-specific variables were estimated to significantly affect farmers’ farm income depending on the estimation technique used.ConclusionsThe study has reaffirmed the critical role of extension programmes in enhancing farm productivity and household income. It is, therefore, recommended that agricultural extension service delivery should be boosted through timely recruitment, periodic training of agents and provision of adequate logistics.
During the relatively short time since the discovery of fullerenes in 1985, carbon nanotubes in 1991, and graphene in 2004, the unique properties of carbon-based nanomaterials have attracted great interest, which has promoted the development of methods for large-scale industrial production. The continuously increasing commercial use of engineered carbon-based nanomaterials includes technical, medical, environmental and agricultural applications. Regardless of the application field, this is also associated with an increasing trend of intentional or unintended release of carbon nanomaterials into the environment, where the effect on living organisms is still difficult to predict. This review describes the different types of carbon-based nanomaterials, major production techniques and important trends for agricultural and environmental applications. The current status of research regarding the impact of carbon nanomaterials on plant growth and development is summarized, also addressing the currently most relevant knowledge gaps.Graphical abstract The study reviews recent research activities exploring synthesis of carbon nanomaterials, their potential applications and impacts on plant development The study reviews recent research activities exploring synthesis of carbon nanomaterials, their potential applications and impacts on plant development
Forecasting agricultural markets remains challenging due to nonlinear dynamics, structural breaks, and sparse data. A long-standing belief holds that simple time-series methods outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds with modern time-series foundation models (TSFMs). Using USDA ERS monthly commodity price data from 1997-2025, we evaluate 17 forecasting approaches across four model classes, including traditional time-series, machine learning, deep learning, and five state-of-the-art TSFMs (Chronos, Chronos-2, TimesFM 2.5, Time-MoE, Moirai-2), and construct annual marketing year price predictions to compare with USDA's futures-based season-average price (SAP) forecasts. We show that zero-shot foundation models consistently outperform traditional time-series methods, machine learning, and deep learning architectures trained from scratch in both monthly and annual forecasting. Furthermore, foundation models remarkably outperform USDA's futures-based forecasts on three of four major commodities despite USDA's information advantage from forward-looking futures markets. Time-MoE delivers the largest accuracy gains, achieving 54.9% improvement on wheat and 18.5% improvement on corn relative to USDA ERS benchmarks on recent data (2017-2024 excluding COVID). These results point to a paradigm shift in agricultural forecasting.
This paper presents the development and evaluation of a custom Convolutional Neural Network (CustomCNN) created to study how architectural design choices affect multi-domain image classification tasks. The network uses residual connections, Squeeze-and-Excitation attention mechanisms, progressive channel scaling, and Kaiming initialization to improve its ability to represent data and speed up training. The model is trained and tested on five publicly available datasets: unauthorized vehicle detection, footpath encroachment detection, polygon-annotated road damage and manhole detection, MangoImageBD and PaddyVarietyBD. A comparison with popular CNN architectures shows that the CustomCNN delivers competitive performance while remaining efficient in computation. The results underscore the importance of thoughtful architectural design for real-world Smart City and agricultural imaging applications.
Isaac Corley, Hannah Kerner, Caleb Robinson
et al.
Field boundary maps are a building block for agricultural data products and support crop monitoring, yield estimation, and disease estimation. This tutorial presents the Fields of The World (FTW) ecosystem: a benchmark of 1.6M field polygons across 24 countries, pre-trained segmentation models, and command-line inference tools. We provide two notebooks that cover (1) local-scale field boundary extraction with crop classification and forest loss attribution, and (2) country-scale inference using cloud-optimized data. We use MOSAIKS random convolutional features and FTW derived field boundaries to map crop type at the field level and report macro F1 scores of 0.65--0.75 for crop type classification with limited labels. Finally, we show how to explore pre-computed predictions over five countries (4.76M km\textsuperscript{2}), with median predicted field areas from 0.06 ha (Rwanda) to 0.28 ha (Switzerland).
Mohammed Brahimi, Karim Laabassi, Mohamed Seghir Hadj Ameur
et al.
Machine learning models in agricultural vision often achieve high accuracy on curated datasets but fail to generalize under real field conditions due to distribution shifts between training and deployment environments. Moreover, most machine learning competitions focus primarily on model design while treating datasets as fixed resources, leaving the role of data collection practices in model generalization largely unexplored. We introduce the AgrI Challenge, a data-centric competition framework in which multiple teams independently collect field datasets, producing a heterogeneous multi-source benchmark that reflects realistic variability in acquisition conditions. To systematically evaluate cross-domain generalization across independently collected datasets, we propose Cross-Team Validation (CTV), an evaluation paradigm that treats each team's dataset as a distinct domain. CTV includes two complementary protocols: Train-on-One-Team-Only (TOTO), which measures single-source generalization, and Leave-One-Team-Out (LOTO), which evaluates collaborative multi-source training. Experiments reveal substantial generalization gaps under single-source training: models achieve near-perfect validation accuracy yet exhibit validation-test gaps of up to 16.20% (DenseNet121) and 11.37% (Swin Transformer) when evaluated on datasets collected by other teams. In contrast, collaborative multi-source training dramatically improves robustness, reducing the gap to 2.82% and 1.78%, respectively. The challenge also produced a publicly available dataset of 50,673 field images of six tree species collected by twelve independent teams, providing a diverse benchmark for studying domain shift and data-centric learning in agricultural vision.