The digital transformation in agriculture introduces new challenges in terms of data, knowledge and technology adoption due to critical interoperability issues, and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.
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.
Ornamental plants are constantly being improved by new technologies and cultivation systems to provide new, high-quality plant material for one of the most demanding markets in the horticulture sector. In addition, the ornamental production sector faces several challenges, such as an increase in costs of production, new and old pests and diseases, climate change and the need to adapt to environmental stresses, the need for conservation and environmental protection, and competition with other food and energy crops in terms of areas and natural resources.
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.
Accurate and timely crop mapping is crucial for environment assessment, food security and agricultural production. However, for the areas with high landscape heterogeneity and frequent cloudy and rainy weather, the insufficient high-quality satellite images limit the accuracy of crop classification. The recently launched Chinese GF-6 wide field-of-view camera (WFV) with a revisit cycle of 4-day and spatial resolution of 16-meter shows great potential for agricultural monitoring.
El presente documento sistematiza los principales hallazgos de la Mesa de Articulación Interinstitucional para el fortalecimiento del sistema territorial de innovación del sector lechero en Boyacá (Colombia), llevada a cabo en la ciudad de Tunja el 12 de mayo de 2022. Este espacio de construcción colectiva que contó con la participación de 50 delegados de la institucionalidad pública, organizaciones de productores, academia y empresas privadas, tuvo como objetivo conocer la oferta de las instituciones del departamento y su agenda 2022 en materia de innovación.
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.
The publication looks at innovations happening at all stages of the food value chain: from production to manufacturing and retailing. This also includes the extended value chain, for example input supply, financial services and agribusiness support services. Yields are improving and primary production is becoming more resilient as a result of digital technologies such as precision agriculture, agricultural drones, and digital farming services and marketplaces; and novel business models such as plant factories, crowdsourcing for farmers.
Digitalization and internet use are transforming every aspect of our lives. Digital technologies are profoundly changing how we grow food, pack it, transport it and even shop for food. Digitalization and use of digital data, applications, and platforms are opening new possibilities for developing and restructuring the agrifood system. Digital agriculture is turning to digitalizing agrifood, rural economy, and rural societies. This report introduces the FAO Digital Village Initiative, which aims to facilitate through knowledge and information.
Innovation for sustainable agricultural intensification (SAI) is challenging. Changing agricultural systems at scale normally means working with partners at different levels to make changes in policies and social institutions, along with technical practices. This study extracts lessons for practitioners and investors in innovation in SAI, based on concrete examples, to guide future investment.