Early diagnosis system to detect phosphorus deficiency in barley fields Based on an Improved YOLO-v8 Network

This study presents a diagnostic method for early and rapid detection of phosphorus (P) deficiency in barley fields in the natural environment, stemming from computer vision and based on a deep learning approach. This is done by creating a training model based on the YOLOv8 network. A phenotyping method is used to identify the plant, and then based on the color of the leaves and stems to detect phosphorus (P) deficiency. Some improvements have been made to the basic algorithm, using image processing techniques such as flipping, rotating, deep convolution, and resizing for feature enrichment. In our approach, images were classified using polygonal bounding boxes in collaboration with agricultural experts to identify areas of interest for the model. The dataset was divided into validation, training, and testing. In the experimental phase, we analyzed the model's performance using a set of video clips captured with a mobile phone camera as a first stage, and our model achieved a detection accuracy of up to 80%. In the second stage, we tested the model using images and video clips taken from a group of cameras installed on top of the pivot irrigation machine to take advantage of the machine’s movement to scan the entire field. The model achieved a detection accuracy of up to 65%. Therefore, the proposed method can provide an early prediction system for plant needs, helping farmers maintain crop health and choose appropriate fertilizers. Keywords: Phosphorus (P), deep learning, YOLOv8, computer vision, image processing.