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Journal : Journal Geuthee of Engineering and Energy

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Analysis comparison effect of image capture speed on rice pest detection using yolov 5 and yolov 7 Aldio Reza, Rahmanda; Bintoro, Andik; Multazam, Teuku; Hasibuan, Arnawan; Badriana, Badriana
Journal Geuthee of Engineering and Energy Vol 4, No 2 (2025): Journal Geuthee of Engineering and Energy
Publisher : Geuthèë Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52626/joge.v4i2.66

Abstract

Pest attacks are one of the main causes of declining rice production in Indonesia. To address this issue, this study examines the use of artificial intelligence-based object detection algorithms, namely YOLOv5 and YOLOv7, in a rice pest monitoring system using drones. The main focus of this study is to assess the effect of image capture speed on pest detection results, as well as to compare the performance of the two algorithms in various aspects, such as detection accuracy, speed, and effectiveness in identifying two main types of pests, namely rice stem borers and planthoppers. The applied methodology includes collecting visual datasets from the field, object annotation using Roboflow, training and testing models using the Anaconda Prompt platform, and analyzing the detected images in binary and grayscale forms using MATLAB. Performance evaluation is carried out using Intersection over Union (IoU), mean IoU (mIoU), and visual analysis of pixel heatmaps. The results show that detection speed is affected by variations in image capture height. YOLOv7 has faster processing performance than YOLOv5, with a capture time of 0.82 s–1.41 s, while YOLOv5 is in the range of 1.15 s–1.47 s. Accuracy evaluation through IoU and mIoU calculations produces consistent values in each frame. The YOLOv5 model obtained an IoU = 0.8711 and mIoU = 0.8711, while YOLOv7 also achieved an IoU = 0.8711 and mIoU = 0.8711. Both models showed a high balance of prediction areas, but YOLOv7 was superior in terms of time efficiency and performance stability at various heights. This research provides an important contribution to the development of AI-based precision agriculture systems, especially in detecting pests automatically and in real-time to improve pest control efficiency and agricultural productivity in Indonesia.
Co-Authors Adinda Sabrina Suli Afrah, Afrah Aja Anis Monika Aldio Reza, Rahmanda Andik Bintoro Angga Pratama Arnawan Hasibuan Asbar, Yuli Ashari Zaini Hasibuan Asran Asri Asri Asri Atthaillah, Atthaillah Azizi, Jamali Bakhtiar Bakhtiar Bakhtiar, Amril Burhanuddin Burhanuddin Darmeli Nasution Dedi Fariadi Devi Sutri Insani Emi Maulani Eri Saputra Ezwarsyah Ezwarsyah Fadliani Fadliani Fadliani, Fadliani Fahrizal, Effan Fajar Rahmansyah Fakhruddin Ahmad Nasution Fauzi, Sri Wahyuni Fidyatun Nisa Fuadi, Wahyu Habib Muharry Yusdartono Hafli, Mudi Handasah, Ummu Hasibuan, Ashari Zaini Insani, Devi Sutri Irvan Fathur Rahman Jordan Jordan Kartika Kartika Lazuardi, Maulana Mirsa, Rinaldi Misbahul Jannah Mochammad Imron Awalludin Muchlis Abdul Muthalib Muhammad Daud Muhammad Hidayat Muhammad Ikhwanus Muhammad Iqbal Muhammad Muhammad Mutamimmul Ula Mutammimul Ula Nabila, Putri Sri Alisia Nasution, Aminulsyah Novianty, Vivi Nuraini Nuraini Nurfebruary, Nanda Sitti Nurmalita Pangestu, Mhd Tobi Pinem, Leo Sani Muslim Putra, Gusti Randa Affinda Putri Ramadhani, Putri Raihan Putri Ramadhani, Hardiyansyah Reza Rolianda Rosdiana Rosdiana Rosdiana Rosdiana Rozzi Kesuma Dinata Safira, Intan Sahputra, Ilham Salahuddin Salahuddin Sayed Fachrurrazi Selamat Meliala Sinuhaji, Sebastian Ferdi Caras Siregar, Aria Maigawa Suhartina, Wijayanti Surbakti, Aprina Br Syahri, Alfis syarifah asria nanda, syarifah asria Syibral Malasyi, Syibral Syukriah Taufiq Taufiq Teuku Multazam Wattimena, Sefnath J Zulfahmi Zulfahmi Zulmiardi, Zulmiardi