GREENHOUSE PRODUCTIVITY ESTIMATION BASED ON THE OPTIMIZED YOLOV5 MODEL

Eraliev, Oybek, Rashidov, Kodirjon, Eraliev , Khojiakbar, Эралиев , Ойбек, Рашидов , Кодиржон, Эралиев , Хожиакбар, Eraliev , Oybek, Rashidov , Qodirjon, Eraliev , Xojiakbar

Рақамли иқтисодиёт · 2025-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarEraliev, Oybek, Rashidov, Kodirjon, Eraliev , Khojiakbar, Эралиев , Ойбек, Рашидов , Кодиржон, Эралиев , Хожиакбар, Eraliev , Oybek, Rashidov , Qodirjon, Eraliev , Xojiakbar
JurnalРақамли иқтисодиёт
Nashr sanasi2025-07-01
Jild7
Son7
Betlar598-621
TilIngliz

Kalit so‘zlar

greenhouse Productivity, YOLOv5 Optimization, Tomato Detection, Precision Agriculture, Real-time Monitoring., greenhouse Productivity, YOLOv5 Optimization, Tomato Detection, Precision Agriculture, Real-time Monitoring., issiqxona mahsuldorligi, YOLOv5 optimallashtirish, pomidorni aniqlash, aniq qishloq xo'jaligi, real vaqt rejimida monitoring.

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