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| Filter results4 paper(s) found. |
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1. The Vision of Future Earth Observation for AgricultureThe main objective of EO4AGRI is to catalyze the evolution of the European capacity for improving operational agriculture monitoring from local to global levels based on information derived from Copernicus satellite observation data and through exploitation of associated geospatial and socio-economic information services. EO4AGRI assists the implementation of the EU Common Agricultural Policy (CAP) with special attention to the CAP2020 reform, to requirements of Paying Agencies, and... K. Charvat, V. Safar, H. Kubickova |
2. SmartAfriHub for SmartAgriculture capacity buidling in AfricaDigital Innovation Hubs (DIH) are multi-actor ecosystems that support farming communities in their digital transformation by providing a broad variety of services from a one-stop shop. DIHs purpose is to provide a social space for community of practices; provide access to digital technologies and competencies; provide access to infrastructure and tests digital innovations (“test before invest”); provide development playground... K. Charvat, C. Miderho , A. Obot, T. Löytty, H. Kubickova |
3. A review on Sensor based robotics agriculture: Improving traditional Agriculture PracticesAgribot could be a mechanism designed to reduce the labor of farmers by increasing the speed and accuracy of the work. Elementary functions concerned in farming i.e. plowing the sphere, sowing of seeds and covering the seeds with soil. Agribot is associate degree autonomous mechanism that provides the power for choices for offered techniques. Fruit Picker robots, autonomous tractor sprayers, this... S.C. Karad, G.U. Shinde, P. Kumar |
4. Development of Lodging Direction Determination System Using Image ProcessingIn this study, image processing system was developed for application on rice plants to determine lodging condition, which was contributing factor to declining harvester efficiency by using combine harvester. Therefore, We developed a system for determination of the lodging direction by algorithm based on convolutional neural network (CNN). As for deep learning framework, Pytorch1.1.0 were used to train and test the judging direction. GoogLeNet was used as a pre-trained CNN model. Lodging... E. Morimoto, Y. Arai, K. Nonami, T. Ito |