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 publicación “Guía para el fortalecimiento de las capacidades funcionales para la innovación en la agricultura” fue desarollada por la Organización de las Naciones Unidas para la Agricultura y la Alimentación (FAO) y el Instituto Interamericano de Cooperación para la Agricultura (IICA) con el apoyo de la Red Latinoamericana de Servicios de Extensión Rural (RELASER) y la Plataforma de Agricultura Tropical (TAP). El propósito de esta guía es contribuir al desarrollo de las capacidades funcionales entre individuos y organizaciones que apoyan la innovación en América Latina.
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.
Assessing or understanding the agriculture innovation system (AIS) is an essential step to better understand the needs, new skills and functions needed by the actors and the system. To accelerate the uptake of innovation and progress towards eradicating poverty, there is an urgent need for well-coordinated, demand-driven, and market-oriented information, knowledge, technologies and services.
Agricultural Internet of Things (IoT) has brought new changes to agricultural production. It not only increases agricultural output but can also effectively improve the quality of agricultural products, reduce labor costs, increase farmers' income, and truly realize agricultural modernization and intelligence. This paper systematically summarizes the research status of agricultural IoT. Firstly, the current situation of agricultural IoT is illustrated and its system architecture is summarized. Then, the five key technologies of agricultural IoT are discussed in detail.
Droughts are causing severe damages to tropical countries worldwide. Although water abundant, their resilience to water shortages during dry periods is often low. As there is little knowledge about tropical drought characteristics, reliable methodologies to evaluate drought risk in data scarce tropical regions are needed.
The need to urgently transition food systems to net-zero, nature-positive that can nourish all people, leaving no one behind is more critical than ever. The COVID-19 pandemic has furthered deepened complex challenges we already face from hunger and nutrition, climate and nature, and societal inequity. Innovation offers a profound opportunity to achieve these transitions and help unlock challenges across food systems.
Facilitation has proved crucial for enabling the interaction of Agricultural Innovation System (AIS) actors to address the target and to innovate. This “Guide on training of facilitators of multi-actor agricultural innovation platform” is aimed at serving facilitators when multi-actor agricultural innovation platforms (MAIPs) are organized. Since MAIPs are still an emerging concept, there are not many cases to refer to.
Digitization in agriculture is rapidly advancing further on. New technologies and solutions were developed and get invented which ease farmers’ daily life, help them and their partners to gain knowledge about farming processes and environmental interrelations. This knowledge leads to better decisions and contributes to increased farm productivity, resource efficiency, and environmental health. Along with numerous advantages, some negative aspects and dependencies risk seamless workflow of agricultural production.
This article extends social science research on big data and data platforms through a focus on agriculture, which has received relatively less attention than other sectors like health. In this paper, I use a responsible innovation framework to move attention to the social and ethical dimensions of big data “upstream,” to decision-making in the very selection of agricultural data and the building of its infrastructures.