Deep Learning Model YOLOv5 for Red Chilies Detection from Chilly Crop Images

The agriculture industry has been facing a growing challenge with the rising cost of labor in recent years. A large portion of the expenses incurred in agriculture goes towards employing workers. This increase in labor expenses puts farmers under pressure to find ways to reduce costs while still producing at high levels. To tackle this issue, many farmers have turned to technology and automation. Using machines and automated systems to perform tasks that were previously done by humans can help decrease labor costs and increase efficiency, keeping farmers competitive in a continuously changing industry. The usage of technology in agriculture has rapidly increased in various forms, including precision farming, crop monitoring, irrigation systems, and decision-making tools based on data. Additionally, the growth of machine learning and AI has also played a crucial role in modernizing agriculture, with computer vision-based systems being developed for crop detection, classification, and monitoring. This research focuses on creating a red chili detection system using the YOLOv5 deep learning model. This project’s objective is to accurately and successfully identify ripened red chilies in real-world images. The proposed approach first applies methods for preparing images to increase the input photos' quality, and then utilizes the YOLOv5 model for object detection. The model was trained using a large dataset of red chili images and evaluated based on performance metrics, including accuracy, speed, and robustness to variations in the input data. The outcomes of our tests show that the suggested technique is more successful than other cutting-edge deep learning models, making it a promising solution for applications in the fields of agricultural automation and food processing.