The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach.
La transition agroécologique requiert de transformer la manière d’accompagner les agriculteurs dans leurs changements de pratiques. Les champs-écoles sont des dispositifs participatifs pertinents pour cela, car ils accroissent les capacités des agriculteurs à expérimenter, à produire des connaissances et à construire eux-mêmes des innovations. Il est toutefois nécessaire de veiller à la qualité de mise en œuvre de ces dispositifs, ce qui a des implications pour les acteurs de la recherche et du développement.
Global Open Data for Agriculture and Nutrition (GODAN) and The Haller Foundation joined forces in 2016 when the UK based charity released version one of the Haller Farmers App.
El propósito de esta guía es contribuir al desarrollo de las capacidades funcionales entre individuos y organizaciones que apoyan la innovación en América Latina. En la guía se ha utilizado el marco común para el desarrollo de capacidades promovido por la Plataforma de Agricultura Tropical (TAP, por sus siglas en inglés), con el fin de identificar y reforzar las capacidades funcionales clave en organizaciones e individuos.
Africa–Europe Cooperation and Digital Transformation explores the opportunities and challenges for cooperation between Africa and Europe in the digital sphere.
RECUEIL DE SUJETS SPECIAUX POUR LES CEAP ANIMES PAR LES FACILITATEURS LOCAUX ET TECHNICIENS
This brochure presents the five-year TAP-AIS project (2019-2024) funded by the European Union under the DeSIRA Initiative and implemented by the Food and Agriculture Organization (FAO) of the United Nations. The project has the main objective to strengthen capacities to innovate in national agricultural innovation systems (AIS) in the context of climate-relevant, productive, and sustainable transformation of agriculture and food systems in Africa, Latin America, Asia and the Pacific.
The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one.
This report summarizes studies conducted in a framework of TAP-AIS project implemented by FAO’s Research and Extension Unit, and funded by the European Union as a component of the European Union initiative on “Development Smart Innovation through Research in Agriculture” (DeSIRA).