Keywords
Hama
Tomat
How to Cite
Abstract
Tomat (Solanum lycopersicum L.) merupakan tanaman yang banyak ditanam di dunia karena gizinya yang tinggi dan memungkinkan produksi di sepanjang tahun. Meskipun demikian, tomat merupakan tanaman yang rentan mengalami kerusakan sehingga mengurangi produktivitas dan akan menyebabkan kerugian apabila tidak segera ditangani, penyebab dari kerusakan tanaman tomat itu sendiri diantaranya adalah penyakit dan hama serangga. Hama ulat dan belalang merupakan hama yang umum ditemui pada tanaman tomat dimana apabila hama tersebut tidak segera diatasi maka akan menyebabkan kerusakan pada tanaman. Untuk membantu mengidentifikasi adanya hama sekaligus mengenali bagian-bagian tanaman tomat diperlukan bantuan teknologi yakni computer vision. Pada penelitian ini computer vision akan digunakan untuk mendeteksi bagian-bagian tanaman tomat serta tingkat kematangan buah tomat yakni unripe tomato, semi ripe tomato, dan ripe tomato. Sebanyak 368 dataset kemudian dibagi menjadi 3 bagian yakni train set sebanyak 70%, valid set sebanyak 20%, dan test set sebanyak 10%. Pengujian menggunakan model YOLO-v9e mendapatkan hasil dengan performa terbaik pada kelas grasshopper dan caterpillar, serta mendapatkan performa yang cukup baik pada kelas ripe_tomato, semi_ripe_tomato, dan unripe_tomato, dan mendapatkan performa terendah pada kelas stem dan leaf.
References
[1] L. Depenbusch et al., “Tomato pests and diseases in Bangladesh and India: farmers’ management and potential economic gains from insect resistant varieties and integrated pest management,” Int J Pest Manag, pp. 1–15, Sep. 2023, doi: 10.1080/09670874.2023.2252760.
[2] M. Tolasa, F. Gedamu, and K. Woldetsadik, “Impacts of harvesting stages and pre-storage treatments on shelf life and quality of tomato ( Solanum lycopersicum L.),” Cogent Food Agric, vol. 7, no. 1, Jan. 2021, doi: 10.1080/23311932.2020.1863620.
[3] B. Su et al., “Analysis of the effect of improving drought tolerance via SlGATA17 overexpression on tomato fruit quality,” Sci Hortic, vol. 343, p. 114083, Mar. 2025, doi: 10.1016/j.scienta.2025.114083.
[4] L. Dong and H. Manghwar, “Genome-wide expression analysis of LBD genes in tomato ( Solanum lycopersicum L.) under different light conditions,” Plant Signal Behav, vol. 18, no. 1, Dec. 2023, doi: 10.1080/15592324.2023.2290414.
[5] M. Kumar et al., “Valorization Potential of Tomato (Solanum lycopersicum L.) Seed: Nutraceutical Quality, Food Properties, Safety Aspects, and Application as a Health-Promoting Ingredient in Foods,” Horticulturae, vol. 8, no. 3, p. 265, Mar. 2022, doi: 10.3390/horticulturae8030265.
[6] T. Laranjeira et al., “Sustainable Valorization of Tomato By-Products to Obtain Bioactive Compounds: Their Potential in Inflammation and Cancer Management,” Molecules, vol. 27, no. 5, p. 1701, Mar. 2022, doi: 10.3390/molecules27051701.
[7] E. J. Collins, C. Bowyer, A. Tsouza, and M. Chopra, “Tomatoes: An Extensive Review of the Associated Health Impacts of Tomatoes and Factors That Can Affect Their Cultivation,” Biology (Basel), vol. 11, no. 2, p. 239, Feb. 2022, doi: 10.3390/biology11020239.
[8] K. M.-P. Chanthini, A. Deva-Andrews, P. Kirupaanntha-Rajan, B. O. Almutairi, and S. Senthil-Nathan, “Systemic defense induced by fatty acid compounds from marine macroalgae, Chaetomorpha antennina in tomato (Solanum lycopersicum) plants alters the susceptibility of the polyphagous agricultural pest, Spodoptera litura Fab,” Chemical and Biological Technologies in Agriculture, vol. 11, no. 1, p. 65, May 2024, doi: 10.1186/s40538-024-00573-1.
[9] R. B. de Lima Filho et al., “Relationship between Acylsugars and Leaf Trichomes: Mediators of Pest Resistance in Tomato,” Insects, vol. 13, no. 8, p. 738, Aug. 2022, doi: 10.3390/insects13080738.
[10] C. Zhang, Y. Zhang, and X. Xu, “Dilated inception U-Net with attention for crop pest image segmentation in real-field environment,” Smart Agricultural Technology, vol. 11, p. 100917, Aug. 2025, doi: 10.1016/j.atech.2025.100917.
[11] A. Bijlwan et al., “Enhancing rice disease and insect-pest detection through augmented deep learning with transfer learning techniques,” Smart Agricultural Technology, vol. 11, p. 100954, Aug. 2025, doi: 10.1016/j.atech.2025.100954.
[12] D. Hunter, “World’s Best Practice Locust and Grasshopper Management: Accurate Forecasting and Early Intervention Treatments Using Reduced Chemical Pesticide,” Agronomy, vol. 14, no. 10, p. 2369, Oct. 2024, doi: 10.3390/agronomy14102369.
[13] H. Zhang et al., “Beta diversity of grasshoppers and predatory beetles across steppes is closely associated with altitude and average annual precipitation in Ningxia, northwest China,” Glob Ecol Conserv, vol. 51, p. e02941, Jun. 2024, doi: 10.1016/j.gecco.2024.e02941.
[14] K. Djufri, H. Umaternate, and J. Andres, “Efektifitas Ekstrak Daun Sirih Hijau Sebagai Insektisida Nabati Bagi Tanaman Tomat,” JBES: Journal of Biology Education and Science, vol. 4, no. 1, pp. 19–25, 2024.
[15] R. Soelistijono, “EFEKTIFITAS DAUN MAHONI (Swietenia mahagoni L. Jacq.) SEBAGAI BIOPESTISIDA Spodoptera litura F. PADA TOMAT,” Jurnal Ilmiah Hijau Cendekia, vol. 8, no. 2, p. 173, Oct. 2023, doi: 10.32503/hijau.v8i2.4107.
[16] J. Subur, Suryadhi, M. Taufiqurrohman, and N. Reza Al Hafizh, “Pemanfaatan Teknologi Computer Vision untuk Deteksi Ukuran Ikan Bandeng dalam Membantu Proses Sortir Ikan,” CYCLOTRON, vol. 7, no. 01, pp. 52–60, Jan. 2024, doi: 10.30651/cl.v7i01.21239.
[17] M. M. Huda, K. A. Prasetyo, M. A. R. Vieri, R. Wulanningrum, and M. A. D. W. Dara, “Identifikasi Mangga Berdasarkan Tingkat Kematangan Menggunakan Yolo 11,” in Seminar Nasional Teknologi & Sains, 2025, pp. 175–182.
[18] H.-T. Vo, K. C. Mui, N. N. Thien, and P. P. Tien, “Automating tomato ripeness classification and counting with YOLOv9,” Int. J. Adv. Comput. Sci. Appl, vol. 15, no. 4, 2024.
[19] P. Li, J. Zheng, P. Li, H. Long, M. Li, and L. Gao, “Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8,” Sensors, vol. 23, no. 15, p. 6701, Jul. 2023, doi: 10.3390/s23156701.
[20] V. Adi Kurniyanti and D. Murdiani, “Perbandingan Model Waterfall Dengan Prototype Pada Pengembangan System Informasi Berbasis Website,” Jurnal Syntax Fusion, vol. 2, no. 08, pp. 669–675, Aug. 2022, doi: 10.54543/fusion.v2i08.210.
[21] D. T. Haniva, J. A. Ramadhan, and A. Suharso, “Systematic Literature Review Penggunaan Metodologi Pengembangan Sistem Informasi Waterfall, Agile, dan Hybrid,” Journal of Information Engineering and Educational Technology, vol. 7, no. 1, pp. 36–42, Jun. 2023, doi: 10.26740/jieet.v7n1.p36-42.
[22] M. M. NSA, R. Wulanningrum, and A. Sanjaya, “Implementasi YOLO Dalam Deteksi Jumlah Kendaraan,” in Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 2024, pp. 1274–1281.
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