Deteksi Residu Insektisida Profenofos pada Cabai Merah (Capsium annum L.) melalui Augmentasi Citra dan CNN (Convolutional Neural Network)

  • Zulfa Hana Maulida Udayana University
  • I Putu Budisanjaya Program Studi Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Udayana, Badung, Bali, Indonesia.
  • I Made Supartha Utama Program Studi Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Udayana, Badung, Bali, Indonesia.
  • Chatchawan Chaicana Program Studi Teknik Mesin, Fakultas Teknik, Universitas Chiang Mai, Muang, Chiang Mai, Thailand
  • Wahyu Nurkholis Hadi Syahputra Program Studi Teknik Mesin, Fakultas Teknik, Universitas Chiang Mai, Muang, Chiang Mai, Thailand

Abstract

Abstrak


Penggunaan insektisida dalam pertanian meningkat pesat namun juga menghadapi tantangan terkait dampak negatifnya terhadap kesehatan manusia dan lingkungan. Salah satu insektisida yang umum digunakan adalah profenofos. Profenofos sering digunakan petani untuk mengendalikan hama pada tanaman cabai merah di Indonesia. Oleh karena itu, deteksi residu insektisida profenofos pada cabai penting untuk memastikan keamanan konsumsi cabai. Penelitian ini bertujuan untuk mengembangkan metode baru dalam mendeteksi residu insektisida profenofos pada cabai merah (Capsicum Annum L.) melalui augmentasi citra dan CNN (Convolutional Neural Network). Dalam penelitian ini, dilakukan penyiapan larutan insektisida, pengambilan citra cabai, pengujian dengan GC (Gas Chromatography) Agilent 6890N, pra-pemrosesan citra, dan implementasi pemodelan CNN.  Dilakukan penyemprotan pada 15 cabai dengan konsentrasi 0  dan 10 mg/l, diikuti pengambilan citra menggunakan smartphone sehingga terdapat 30 citra. Selanjutnya, setiap citra diaugmentasi sebanyak 50 kali, menghasilkan total 1530 citra. Proses ini melibatkan rotasi, pergeseran, zoom, serta horizontal dan vertical flipping. Model CNN yang terdiri dari convolution layer dan fully connected dilatih dengan optimizer Adam, loss function categorical crossentropy, learning rate 0,0001, dan dilakukan pelatihan sebanyak 20 epoch. Hasil analisis menunjukkan bahwa model mencapai akurasi sebesar 85% dengan nilai precision, recall, dan F1-score masing-masing adalah 0,86; 0,85; dan 0,84. Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode augmentasi citra dan CNN telah berhasil mendeteksi residu insektisida profenofos pada cabai merah dengan konsentrasi 0 dan 10 mg/l.


Abstract


The use of insecticide in agriculture is rapidly increasing but also faces challenges related to their negative impacts on human health and the environment. One commonly used insecticide is profenofos. Farmers frequently use Profenofos to control pests on red chili plants in Indonesia. Therefore, detecting profenofos insecticide residue on chili is crucial to ensure the safety of chili consumption. This study aims to develop a new method for detecting profenofos insecticide residues on red chilies (Capsicum Annum L.) through image augmentation and CNN (Convolutional Neural Network). In this study, insecticide solution preparation, chili image acquisition, testing with GC (Gas Chromatography) Agilent 6890, image preprocessing, and CNN model implementation were conducted. Insecticide solution spraying was conducted on 15 chilies with concentrations of 0 and 10 mg/l, followed by smartphone image acquisition, resulting in 30 images. Subsequently, each image was augmented 50 times, resulting in 1530 images. This process involves rotation, shifting, zoom, and horizontal and vertical flipping. The CNN model, consisting of convolution layers and fully connected layers, was trained with the Adam optimizer, categorical cross-entropy loss function, the learning rate of 0,0001, and trained for 20 epochs. The analysis results indicate that the model achieved an accuracy of 85% with precision, recall, and F1-score values of 0,86, 0,85, and 0,84, respectively. Based on these results, it can be concluded that the image augmentation and CNN method successfully detected profenofos insecticide residues on red chilies at concentrations of 0 and 10 mg/l.

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References

Akram, A., Fayakun, K., & Ramza, H. (2023). Klasifikasi Hama Serangga pada Pertanian Menggunakan Metode Convolutional Neural Network. Building of Informatics, Technology and Science (BITS), 5(2). https://doi.org/10.47065/bits.v5i2.4063

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8

Amin, F., & Mahmoud, M. (2022). Confusion matrix in binary classification problems: a step-by-step tutorial. Journal of Engineering Research, 6(5), 0.http://dx.doi.org/10.21608/erjeng.2022.274526

Ardiwinata, A. N., Ginoga, L. N., Sulaeman, E., & Harsanti, E. S. (2018). Pesticide residue monitoring on agriculture in Indonesia. Jurnal Sumberdaya Lahan, 12(2), 133–144.

Ashraf, H. N., Walayat, N., Saleem, M. H., Niaz, N., Hafeez, A., Atiq, M. N., Chattha, M. S., El-Sheikh, M. A., & Ali, S. (2022). Determination of pesticide residues from grapes procured from different markets using through high performance liquid chromatography (HPLC). Pakistan Journal of Botany, 54(2), 737–741. http://dx.doi.org/10.30848/PJB2022-2(19)

Badan Pusat Statistik. (2023). Produksi Tanaman Sayuran 2022.

Banerjee, K., & Hübschmann, H.-J. (2022). Automation in Pesticide Residue Analysis in Foods: A Step toward Smarter Laboratories and Green Chemistry. ACS Agricultural Science & Technology, 2(3), 426–429. https://doi.org/10.1021/acsagscitech.2c00126

Deepalakshmi, P., Lavanya, K., & Srinivasu, P. N. (2021). Plant leaf disease detection using CNN algorithm. International Journal of Information System Modeling and Design (IJISMD), 12(1), 1–21. https://doi.org/10.4018/IJISMD.2021010101

Dijk, T. van, & Croon, G. de. (2019). How do neural networks see depth in single images? Proceedings of the IEEE/CVF International Conference on Computer Vision, 2183–2191.

Elngar, A. A., Arafa, M., Fathy, A., Moustafa, B., Mahmoud, O., Shaban, M., & Fawzy, N. (2021). Image classification based on CNN: a survey. Journal of Cybersecurity and Information Management, 6(1), 18–50. https://doi.org/10.54216/JCIM.060102

Fachrel, J., Pravitasari, A. A., Yulita, I. N., Ardhisasmita, M. N., & Indrayatna, F. (2023). Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model. Applied Sciences, 13(14), 8227. https://doi.org/10.3390/app13148227

Food and Agriculture Organization of the United Nations. (2020). World Food and Agriculture-Statistical Yearbook 2020. Food and Agriculture Organization of the United Nations.

Gong, M. (2021). A novel performance measure for machine learning classification. International Journal of Managing Information Technology (IJMIT) Vol, 13. https://doi.org/10.5121/ijmit.2021.13101

Hassan, H., Elsayed, E., El-Raouf, A. E.-R. A., & Salman, S. N. (2019). Method validation and evaluation of household processing on reduction of pesticide residues in tomato. Journal of Consumer Protection and Food Safety, 14(1), 31–39. https://doi.org/10.1007/s00003-018-1197-2

Hendra, Sarbino, & Syahputra, E. (2021). Pengaruh Frekuensi Penyemprotan Insektisida Profenofos untuk Mengendalikan Lalat Buah Bactrocera Spp pada Tanaman Cabai. Jurnal Sains Pertanian Equator, 10(1), 1–12. http://dx.doi.org/10.26418/jspe.v10i1.43865

Ibrahim, M. S., Hamid, S. A., Muhammad, Z., Leh, N. A. M., Abdullah, S., Bakar, S. J. A., Osman, M. K., & Fadhlullah, S. Y. (2022). CNN Comparative Study for Apple Quality Classification. 2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE), 53–58. https://doi.org/10.1109/ICCSCE54767.2022.9935652

Lee, J., Ko, K., & Chung, H. (2022). Application of colorimetric sensor in monitoring dissolved CO2 in natural waters. Journal of Environmental Management, 312, 114893. https://doi.org/https://doi.org/10.1016/j.jenvman.2022.114893

Lyu, X., Peng, W., Yu, W., Xin, Z., Niu, S., & Qu, Y. (2021). Sustainable intensification to coordinate agricultural efficiency and environmental protection: A systematic review based on metrological visualization. Journal of Land Use Science, 16(3), 313–338. https://doi.org/10.1080/1747423X.2021.1922524

Manggala, B., Chaichana, C., Syahputra, W. N. H., & Wongwilai, W. (2023). Pesticide residues detection in agricultural products: A review. Natural and Life Sciences Communications, 22(3), e2023049. https://doi.org/10.12982/NLSC.2023.049

Palakodati, S. S. S., Chirra, V. R. R., Yakobu, D., & Bulla, S. (2020). Fresh and Rotten Fruits Classification Using CNN and Transfer Learning. Rev. d’Intelligence Artif., 34(5), 617–622. https://doi.org/10.18280/ria.340512

Pratama, D., Wijaya, S., Santosa, S. A., & Tamba, S. P. (2023). Penerapan Neural Network LSTM dalam Memprediksi Sentimen Pengguna Twitter terhadap Bitcoin. Jurnal Tekinkom (Teknik Informasi Dan Komputer), 6(2), 349–354. https://doi.org/10.37600/tekinkom.v6i2.921

Putra, I. W. W. P., Setiyo, Y., Gunam, I. B. W., & Anggreni, A. A. M. D. (2021). Isolation and identification of profenofos pesticide degrading bacterium from soil sample of Bedugul, Indonesia. IOP Conference Series: Earth and Environmental Science, 724(1). https://doi.org/10.1088/1755-1315/724/1/012037

Sanjaya, J., & Ayub, M. (2020). Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup. Jurnal Teknik Informatika Dan Sistem Informasi, 6(2). https://doi.org/10.28932/jutisi.v6i2.2688

Shahid, A. H., & Singh, M. P. (2020). A deep learning approach for prediction of Parkinson’s disease progression. Biomedical Engineering Letters, 10(2), 227–239. https://doi.org/10.1007/s13534-020-00156-7

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1–48. https://doi.org/10.1186/s40537-019-0197-0

Son, T. T., Lee, C., Le-Minh, H., Aslam, N., & Dat, V. C. (2022). An enhancement for image-based malware classification using machine learning with low dimension normalized input images. Journal of Information Security and Applications, 69, 103308. https://doi.org/https://doi.org/10.1016/j.jisa.2022.103308

Suwimantara, I. M. G., Sucipta, I. N., & Tika, I. W. (2022). Hubungan antara Penggunaan Alat Pelindung Diri (APD) dengan Keluhan Petani akibat Pestisida (Studi Kasus di Subak Sri Gumana,Desa Rejasa,Kabupaten Tabanan). JURNAL BETA (BIOSISTEM DAN TEKNIK PERTANIAN), 10(1), 186–190. https://doi.org/10.24843/JBETA.2022.v10.i01.p19

Taareluan, J. A., Ngangi, J., Roring, V. I. Y., & Ogi, N. L. (2021). Toksisitas Ekstrak Daun Jarak (Ricinus communis Linnaeus) Sebagai Biopestisida Terhadap Mortalitas Hama Larva Bawang Daun (Spodoptera exigua Hubner). NUKLEUS BIOSAINS, 2(1), 1–9. https://ejurnal.unima.ac.id/index.php/nukleus-biosains/article/view/2441

Yang, H., Ni, J., Gao, J., Han, Z., & Luan, T. (2021). A novel method for peanut variety identification and classification by Improved VGG16. Scientific Reports, 11(1), 15756.
Published
2024-07-01
How to Cite
MAULIDA, Zulfa Hana et al. Deteksi Residu Insektisida Profenofos pada Cabai Merah (Capsium annum L.) melalui Augmentasi Citra dan CNN (Convolutional Neural Network). Jurnal BETA (Biosistem dan Teknik Pertanian), [S.l.], v. 12, n. 2, p. 293-301, july 2024. ISSN 2502-3012. Available at: <https://ojs.unud.ac.id/index.php/beta/article/view/116314>. Date accessed: 04 nov. 2024. doi: https://doi.org/10.24843/JBETA.2024.v12.i02.p11.
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Articles