The objective of this research was to explore the use of data information of a low-cost IMU to provide an attitude angle with acceptable accuracy for agricultural robot navigation. This work was an attempt to create attitude angle estimation system via sensor fusion method based on gyroscope and accelerometer in this low-cost IMU. The used algorithm processed and integrated the data from triple gyroscope and tri-axis accelerometer using a low-pass filter and Kalman filter. Under this algorithm, experiment data showed that the estimation precision was improved effectively.
There is a growing concern by governments, retailers and consumers about the safety and quality of food. Because products are resourced on a on a global scale it becomes important that the origin of the products as well as all the treatments during production can be traced and that the production methods can be verified as good agricultural practices (GAP). This also includes considerations for the environment and sustainability.
The field of precision agriculture increasingly utilize and develop robotics for various applications, many of which are dependent on high accuracy localization and attitude estimation. Special attention has been put towards full attitude estimation by low-cost sensors, in relation to the development of an autonomous field robot. Quaternions have been chosen due to its continuous nature, and with respect to applications in the pipeline with on other platforms.
Cotton, a major crop worldwide, is harvested in mechanized production systems once at the end of the growing season. To facilitate harvest and maximize fiber quality, the plants are typically defoliated when about 60% of the cotton bolls are open. Due to non-uniform maturation, the bolls that have opened early expose their fiber to weather until harvest, commonly for weeks, degrading fiber quality. Furthermore, high capacity harvesting machines are heavy, potentially compacting the soil that in turn reduces hydraulic conductivity in the wheel tracks and reducing yield.
Selon les évaluations de la FAO, à l’échelle de la planète, 45 pays, dont 34 en Afrique et 9 en Asie, ont besoin d’une aide alimentaire extérieure. Les conflits et les chocs météorologiques demeurent des facteurs critiques qui contribuent aux taux actuellement élevés d’insécurité alimentaire grave. Les effets de la pandémie de covid-19 ont exacerbé les vulnérabilités et accru les besoins humanitaires.
Six ans après l’adoption du Programme 2030, il est urgent de comprendre l’état d’avancement des ODD dans le monde vers l’élimination de la faim et de l'insécurité alimentaire, ainsi que pour la garantie d'une agriculture durable.
La faim dans le monde s'est considérablement aggravée en 2020, ont déclaré aujourd'hui les Nations Unies – une aggravation qui est probablement liée en grande partie aux répercussions de la pandémie de COVID-19. On ne dispose pas encore d'une image complète de l'impact de la pandémie*, mais d'après un rapport établi conjointement par plusieurs organismes des Nations Unies, un dixième environ de la population mondiale – ce qui pourrait représenter jusqu'à 811 millions de personnes – était en situation de sous-alimentation en 2020.
Version abrégée de la publication phare de la FAO, L’État de la sécurité alimentaire et de la nutrition dans le monde 2021, cette brochure contient les principaux messages et le contenu de la publication et est destiné aux médias, aux responsables politiques et au grand public
The COVID-19 pandemic is a major economic shock, throwing into question the resilience of the agrifood sector at this stage, particularly in developing countries where self-employed, wage and informal workers are threatened by food supply chain disruptions, limitations on movement and trade restrictions. Even before the crisis, small and medium agribusinesses were often considered to be credit-constrained and extremely vulnerable to shocks.
The author shares her experience of participating in a training that was organized by the Tropical Agriculture Platform – Agricultural Innovation Systems (TAP-AIS) project, ‘Developing Capacities in Agricultural Innovation Systems: Scaling up the Tropical Agriculture Platform Framework’, implemented by FAO’s Research & Extension Unit, funded by the European Union as a component of the ‘Development Smart Innovation through Research in Agriculture (DeSIRA): Towards Climate-relevant Agricultural and Knowledge Innovation Systems’ initiative.