Classifying RGB images of weeds and maize using RetinaNet One-Step Neural Networks. Dr. Andújar presenting at #EWRS Workshop ‘Integrated Weed Management (IWM) systems in the agroecology context; new challenges’ Integrated Weed Management (IWM) systems are a holistic approach to weed control that combines different methods to give the crop an advantage over weeds. In the […]
Impacts of herbicide on tomato plants. Hence the importance of automatic identification and classification of weeds through artificial intelligence to avoid undesirable effects on crops. Images were taken on the 17th of May, at the same crop field from the 12th of May.
We had the pleasure of having Professor Dr. Dominik Belter from the Institute of Robotics and Machine Intelligence, Poznan University of Technology (Poland). A superb lecture about Motion Planning & Robot Perception was presented.
The most important features of the SWEET project were discussed, raising enthusiasm among the students about the prospective approaches in terms of Precision Agriculture in the context of the 4º Industrial Revolution.
Lecture at Master in Precision Agriculture (UPO, Seville) Read More »
As the tomato (Solanum lycopersicum L.) is one of the most important crops worldwide, yet the conventional approach for weed control compromises its potential productivity. Thus, the automatic detection of the most aggressive weed species is necessary to carry out selective control of them. Precision agriculture associated with computer vision is a powerful tool to
Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops Read More »
We are proud to present The SWEET Project. Deep Learning (DL) is a fundamental subfield of Artificial Intelligence (AI) that is changing weed detection for site-specific weed management (SSWM). In the past half-decade, deep learning algorithms have been merged with ground and aerial-based technology to spot weeds in still images and real-time settings. Observing the
Presenting The SWEET Project Read More »
