Face à la crise mondiale causée par la pandémie de covid-19, les pays prennent des mesures.Les rayons des supermarchés restent approvisionnés pour le moment. Mais une crise pandémique prolongée pourrait rapidement mettre à mal les chaînes d’approvisionnement alimentaire, qui sont des réseaux complexes d’interactions entre exploitants, intrants agricoles, usines de transformation, services d’expédition, détaillants et bien d’autres.
The Date Palm Innovation Platform is a collaborative work is a platform for research, technology and innovation for the integrated production system of the Date Palm. The platform has been developed by the Association of Agricultural Research Institutions in the Near East and North Africa (AARINENA) in collaboration with the Food and Agriculture Organization of the United Nations (FAO).
For an intelligent agricultural robot to reliably operate on a large-scale farm, it is crucial to accurately estimate its pose. In large outdoor environments, 3D LiDAR is a preferred sensor. Urban and agricultural scenarios are characteristically different, where the latter contains many poorly defined objects such as grass and trees with leaves that will generate noisy sensor signals. While state-of-the-art methods of state estimation using LiDAR, such as LiDAR odometry and mapping (LOAM), work well in urban scenarios, they will fail in the agricultural domain.
It is difficult to establish the precise mathematical model of agricultural wheeled robots with differential drive for path tracking control, due to characteristics of nonlinear, strong coupling and multivariable. Here, path tracking control is studied for agricultural wheeled robot with differential drive based on sliding mode variable structure. Firstly, the motion model of agricultural wheeled robots with differential drive is established and control goal is determined for path tracking. Then, sliding mode variable structure is applied to design the controller.
This paper presents Thorvald II, a modular, highly re-configurable, all-weather mobile robot intended for applications in the agricultural domain. Researchers working with mobile agricultural robots tend to work in a wide variety of environments such as open fields, greenhouses, and polytunnels. Until now agricultural robots have been designed to operate in only one type of environment, with no or limited possibilities for customization.
3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presents a deep learning method, Deep-3DMTS for creating a single-perspective approach for 3DMTS through the use of a Convolutional Neural Network (CNN). The novel method is developed and validated via simulation against the standard 3DMTS approach.
This paper proposes a bionic electric spraying rod to perform the crop watering and spraying in the farm. The design concept of multiple vertebrae structures of snake is used to realize a reproducible snake bone arm and muscles of snake, which can be regarded as multiple sets of thin wires and be pulled and released through driver module. It results in different attitudes of the snake bone arm. A water pipe is installed in the snake arm connected to the spray nozzle for spraying. The mobile application interface (APP) is designed to provide the user to control the arm remotely.
This paper describes a remote monitoring system of the agricultural robot using Web application. We developed the system in order to make clear condition about robot combine and adequately manage agricultural task data. The system makes the combine data accumulated in database so that it can be seen from remote-situated PC.
The paper presents an efficient approach for the modelling of wire robots kinematic and dynamics considering the effects of structural elasticity. Using the simulation and animation system several potential applications in agriculture have been simulated and analysed. The paper discusses possible robot configurations, system dynamic constraints and limits, as well as reachable performance for typical large-span wire robot applications in agriculture.
Since 2017, in line with COAG’s recommendation, the Research and Extension Unit engaged in the development of a participatory AIS assessment framework including a customizable toolbox for countries with a totally new capacity development perspective. The assessment framework is meant for actors of the national agricultural innovation systems, i.e.