A Multi-Task Machine Learning Model for Weed Detection and Dense Area Identification in Paddy Fields Using Image and Video Processing to Enhance Yield Quality.

Crop yields in paddy fields can be greatly lowered by weeds and crowded places. Conventional techniques for identifying thick areas and weeds are frequently labor-and time-intensive. While machine learning-based techniques present a viable substitute, creating a single model that is capable of properly and effectively completing both jobs is difficult. This study uses image and video processing to present a multi-task machine-learning model for weed detection and dense area identification in paddy fields. The YOLO V8 deep learning architecture, which is renowned for its great accuracy and speed, serves as the foundation for the model. We gathered a sizable dataset of labeled weeds and thick areas from paddy field photos and videos in order to train the algorithm. After that, the model was trained to simultaneously identify dense areas and detect weeds. The model was assessed using a different test dataset once it had been trained. The outcomes demonstrated that even when used with video streams, the model maintained good accuracy on both tasks. The suggested model can be utilized to create other paddy field management applications, including: • Automated weed detection: The suggested model has the potential to assist farmers in increasing yields and lessening their environmental effects by automating the processes of weed detection and dense area designation. [ABSTRACT FROM AUTHOR]