Detection of Macrolepiota Procera Mushrooms Using Machine Learning

The overpopulation of the latest centuries and recent technological advancements in the primary sector increased demand for improved productivity and product quality in agricultural activities. Modern customers value eco-friendly activities and sustainable consumption. Thus they choose foods with high concentrations of nutritious ingredients. Machine learning algorithms enable the implementation of numerous potential solutions in precision agriculture in conjunction with new potent mechanisms. One algorithm that offers high-precision real-time object identifications is the YOLOv5 (You Only Look Once). Furthermore, to improve the resilience of precision agriculture, this work presents experimental findings from the machine learning algorithm (Yolov5) on a unique dataset based on mushroom crops, such as Macrolepiota Procera species.