GREENHOUSE PRODUCTIVITY ESTIMATION BASED ON THE OPTIMIZED YOLOV5 MODEL
In modern agriculture, precision monitoring and efficient resource
management are paramount for maximizing crop yields. This research presents a novel
approach to greenhouse productivity estimation by leveraging the state-of-the-art
YOLOv5 object detection model, tailored and optimized for a custom tomato dataset.
The study focuses on detecting and classifying tomatoes into three categories-green,
pink, and red-providing a comprehensive understanding of the ripening process in realtime. The optimized YOLOv5 model demonstrated superior performance compared to
the standard version, showcasing enhanced accuracy in tomato identification. The
model was deployed in a real-world greenhouse equipped with a meticulously arranged
seven-camera system, capturing a row of tomato plants per camera. By extrapolating
the results from the single row to the entire greenhouse (comprising eight rows), an
accurate estimation of overall productivity was achieved. A web application was
developed to facilitate real-time monitoring of tomato plant states and key statistics.
The application provides insights into the percentages of green, pink, and red tomatoes,
allowing greenhouse operators to make informed decisions on resource allocation and
management. The proposed methodology offers a scalable and practical solution for
greenhouse productivity assessment, potentially revolutionizing the precision
agriculture landscape. The findings contribute to the advancement of computer vision applications in agriculture, fostering sustainable and efficient practices in greenhouse
cultivation.
