Research Article
Pest Birds Detection Approach in Rice Crops Using Pre-trained YOLOv4 Model
@INPROCEEDINGS{10.1007/978-3-031-23116-2_19, author={Ismael Diakhaby and Mouhamadou Lamine Ba and Amadou Dahirou Gueye}, title={Pest Birds Detection Approach in Rice Crops Using Pre-trained YOLOv4 Model}, proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 5th EAI International Conference, InterSol 2022, Abuja, Nigeria, March 23-24, 2022, Proceedings}, proceedings_a={INTERSOL}, year={2023}, month={2}, keywords={Object detection Bird Deep learning YOLOv4 Rice crops Performance evaluation}, doi={10.1007/978-3-031-23116-2_19} }- Ismael Diakhaby
Mouhamadou Lamine Ba
Amadou Dahirou Gueye
Year: 2023
Pest Birds Detection Approach in Rice Crops Using Pre-trained YOLOv4 Model
INTERSOL
Springer
DOI: 10.1007/978-3-031-23116-2_19
Abstract
In Senegal, farmers in general and rice growers particularly are still facing many issues such as climatic hazards and water-scare environments in their daily life. A very acute and challenging problem for rice crops remains, however, their destruction by pest birds. These latter attack the rice crops when they are mature, leaving the farmers in disarray and without solution. Indeed, such an attack results in a drastic reduction in yields during harvest. Over time, many repellent techniques like scarecrow have been used, but show their limitations. In this paper, we tackle this problem and propose a pest birds detection approach in rice crops using pre-trained YOLOv4 detector and transfer learning. To show the efficiency of our model we conduct experiments on a real bird dataset, exhibiting a mean average precision of(96\%).
