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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

  • Название журналаRaqamli iqtisodiyot
  • Номер выпуска7-son
  • Количество просмотров 45
  • Количество прочтений 45
  • Дата публикации 05-07-2024
  • Язык статьиIngliz
  • Страницы598-621
English

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

Ўзбек

Zamonaviy qishloq xo'jaligida hosildorlikni maksimal darajada oshirish uchun aniq monitoring va resurslarni samarali boshqarish muhim ahamiyatga ega hisoblanadi. Ushbu tadqiqot pomidor o'simligi ma'lumotlar to'plami uchun moslashtirilgan va optimallashtirilgan zamonaviy YOLOv5 ob'ektni aniqlash modelidan foydalangan holda issiqxona mahsuldorligini baholashga yangi yondashuvni taqdim etadi. Tadqiqot pomidorlarni uch toifaga - yashil, pushti va qizil rangga aniqlash va tasniflashga qaratilgan - real vaqt rejimida pishib yetilish jarayonini har tomonlama tushunishni ta'minlaydi. Optimallashtirilgan YOLOv5 modeli boshqa standard versiyali moddellarga nisbatan yuqori unumdorlikni namoyish etib, pomidorni aniqlashda yaxshilangan yuqori aniqlikni namoyish etadi. O'tkazilgan tajribada Model tartibga solingan yetti kamerali tizim bilan jihozlangan haqiqiy issiqxonada joylashtirilib har bir kamera orqali bitta qatordagi pomidor o'simliklarini suratga olib, sinchkovlik bilan kuztuv olib borildi. Bitta qatordan olingan natijalarni butun issiqxonaga (sakkiz qatordan iborat) ekstrapolyatsiya qilish orqali umumiy hosildorlikni aniq baholashga erishildi. Pomidor o'simliklari holati va asosiy statistik ma'lumotlarning real vaqt rejimida monitoringini osonlashtirish uchun veb-ilova ishlab
chiqilgan bo’lib, Ilova yashil, pushti va qizil pomidorlarning foizlari haqida ma'lumot beradi. Bu issiqxona operatorlariga resurslarni taqsimlash va boshqarish bo'yicha ongli qarorlar qabul qilish imkonini beradi. Taklif etilayotgan metodologiya issiqxona
mahsuldorligini baholash uchun keng ko'lamli va amaliy yechim taklif etadi. Olingan natijalar qishloq xo'jaligida kompyuter orqali boshqarish dasturlarini rivojlantirishga, issiqxonalar mahsuldorligini oshirishga, issiqxonalarda barqaror va samarali amaliyotlarni rivojlantirishga yordam beradi

Русский

В современном сельском хозяйстве точный мониторинг и эффективное управление ресурсами имеют первостепенное значение для максимизации урожайности. В этом исследовании представлен новый подход к оценке продуктивности теплиц с использованием современной модели обнаружения объектов YOLOv5, адаптированной и оптимизированной для специального набора данных о помидорах. Исследование направлено на обнаружение и классификацию помидоров на три категории - зеленые, розовые и красные - что обеспечивает полное понимание процесса созревания в режиме реального времени. Оптимизированная модель YOLOv5 продемонстрировала превосходную производительность по сравнению со стандартной версией, продемонстрировав повышенную точность идентификации помидоров. Модель была развернута в реальной теплице, оснащенной тщательно продуманной системой из семи камер, каждая из которых фиксирует ряд растений помидора. Путем экстраполяции результатов с одного ряда на всю теплицу (состоящую из восьми рядов) была достигнута точная оценка общей продуктивности. Было разработано веб-приложение для облегчения мониторинга состояния растений помидора и ключевых статистических данных в режиме реального времени. Приложение предоставляет информацию о процентном соотношении зеленых, розовых и красных помидоров, позволяя операторам теплиц принимать
обоснованные решения по распределению и управлению ресурсами. Предлагаемая методология предлагает масштабируемое и практичное решение для оценки продуктивности теплиц, потенциально революционизирующее ландшафт точного земледелия. Полученные результаты способствуют развитию приложений компьютерного зрения в сельском хозяйстве, способствуя устойчивым и эффективным методам выращивания в теплицах

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