MSc thesis topic: UAV-based grape cluster detection and extraction of features with agronomical interest with video sequences using Deep Learning and Artificial Intelligence
UAVs have enabled a great revolution in agriculture due to the important reduction in costs they present, together with a rapid data acquisition. The combination of UAV data with Artificial Intelligence and Deep Learning has provided the farmer with new tools to know the state of the crop in more detail.
The identification of grape clusters and the estimation of their dimensions, among many other relevant features, are of importance for the estimation of the yield and the quality of it. Therefore, being able to know these features in advance can prepare the farmer with useful information about the status of the vineyard and the income to be received at the end of the campaign.
Relevance to research/projects
- Train an algorithm for video sequences that allows the detection and tracking of grape clusters (masks)
- Analyze how to georeference the extracted masks
- Extract features with agronomical interests from the masks detected
- Santos, T. T., de Souza, L. L., dos Santos, A. A., & Avila, S. (2020). Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association. Computers and Electronics in Agriculture, 170, 105247.
- Kapania, S., Saini, D., Goyal, S., Thakur, N., Jain, R., & Nagrath, P. (2020). Multi object tracking with UAVs using deep SORT and YOLOv3 RetinaNet detection framework. Paper presented at the Proceedings of the 1st ACM Workshop on Autonomous and Intelligent Mobile Systems.
- Wang, Y., Doherty, J. F., & Van Dyck, R. E. (2000). Moving object tracking in video. Paper presented at the Proceedings 29th Applied Imagery Pattern Recognition Workshop.
- Stein, M., Bargoti, S., & Underwood, J. (2016). Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors, 16(11), 1915.
- UAV enthusiast
- Willing work with AI and DL
- Excited to work with high-tech platforms
Theme(s): Sensing & measuring