The development of Pakcoy cultivation holds good prospects, as seen from the demand for vegetable commodities in Indonesia. Its cultivation is consistently rising in terms of volume and value of vegetable imports. However, the cultivation process encounters multiple issues caused by pests and diseases. In addition, the volatile climate in Indonesia has resulted in uninterrupted pest development and the potential decline of Pakcoy’s productivity. Therefore, the detection system for pests and diseases in the Pakcoy plant is called upon to accurately and quickly assist farmers in determining the right treatment, thereby reducing economic losses and producing abundant quality crops. A web-based application with several well-known Convolutional Neural Network (CNN) were incorporated, such as MobileNetV2, GoogLeNet, and ResNet101. A total of 1,226 images were used for training, validating, and testing the dataset to address the problem in this study. The dataset consisted of several plant conditions with leaf miners, cabbage butterflies, powdery mildew disease, healthy plants, and multiple data labels for pests and diseases presented in the individual image. The results show that the MobileNetV2 provides a minimum loss compared to GoogLeNet and ResNet-101 with scores of 0.076, 0.239, and 0.209, respectively. Since the MobileNetV2 architecture provides a good model, the model was carried out to be integrated and tested with the web-based application. The testing accuracy rate reached 98% from the total dataset of 70 testing images. In this direction, MobileNetV2 can be a viable method to be integrated with web-based applications for classifying an image as the basis for decision-making.

• Chen, J., Zhang, D., Suzauddola, M., & Zeb, A. (2021). Identifying crop diseases using attention embedded MobileNet-V2 model. Applied Soft Computing, 113, Article 107901. https://doi.org/10.1016/J.ASOC.2021.107901

• Chudzik, P., Mitchell, A., Alkaseem, M., Wu, Y., Fang, S., Hudaib, T., Pearson, S., & Al-Diri, B. (2020). Mobile real-time grasshopper detection and data aggregation framework. Scientific Reports, 10, Article 1150. https://doi.org/10.1038/s41598-020-57674-8

• Esgario, J. G. M., de Castro, P. B. C., Tassis, L. M., & Krohling, R. A. (2022). An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning. Information Processing in Agriculture, 9(1), 38-47. https://doi.org/10.1016/J.INPA.2021.01.004

• Griffel, L. M., Delparte, D., Whitworth, J., Bodily, P., & Hartley, D. (2023). Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes. Smart Agricultural Technology, 3, Article 100101. https://doi.org/10.1016/J.ATECH.2022.100101

• Hendrawan, Y., Widyaningtyas, S., Fauzy, M. R., Sucipto, S., Damayanti, R., Riza, D. F. A., Hermanto, M. B., & Sandra, S. (2022). Deep learning to detect and classify the purity level of luwak coffee green beans. Pertanika Journal of Science & Technology, 30(1), 1-18. https://doi.org/10.47836/pjst.30.1.01

• Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. ArXiv. https://doi.org/10.48550/arxiv.1704.04861

• Kamal, K. C., Yin, Z., Wu, M., & Wu, Z. (2019). Depthwise separable convolution architectures for plant disease classification. Computers and Electronics in Agriculture, 165, Article 104948. https://doi.org/10.1016/J.COMPAG.2019.104948

• Koklu, M., Cinar, I., & Taspinar, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, Article 106285. https://doi.org/10.1016/J.COMPAG.2021.106285

• Kumi, S., Kelly, D., Woodstuff, J., Lomotey, R. K., Orji, R., & Deters, R. (2022). Cocoa companion: Deep learning-based smartphone application for cocoa disease detection. Procedia Computer Science, 203, 87-94. https://doi.org/10.1016/J.PROCS.2022.07.013

• Li, W., Zheng, T., Yang, Z., Li, M., Sun, C., & Yang, X. (2021). Classification and detection of insects from field images using deep learning for smart pest management: A systematic review. Ecological Informatics, 66, Article 101460. https://doi.org/10.1016/J.ECOINF.2021.101460

• Li, Z., Zhu, H., Hua, H., Liu, C., Cheng, Y., Guo, Y., Du, P., & Qian, H. (2022). Anti-fatigue activity of Brassica rapa L. extract and correlation among biochemical changes in forced swimming mice. Food Bioscience, 47, Article 101633. https://doi.org/10.1016/J.FBIO.2022.101633

• Luo, T., Zhao, J., Gu, Y., Zhang, S., Qiao, X., Tian, W., & Han, Y. (2021). Classification of weed seeds based on visual images and deep learning. Information Processing in Agriculture, 10(1), 40-51. https://doi.org/10.1016/J.INPA.2021.10.002

• Nair, K. S. S. (Ed.). (2000). Insect Pests and Diseases in Indonesian Forest: An Assessment of the Major Threats, Research Efforts and Literature. Center for International Forestry Research (CIFOR). https://doi.org/10.17528/CIFOR/000700

• Putra, B. T. W., Amirudin, R., & Marhaenanto, B. (2022). The evaluation of deep learning using Convolutional Neural Network (CNN) approach for identifying Arabica and Robusta coffee plants. Journal of Biosystems Engineering, 47, 118-129. https://doi.org/10.1007/S42853-022-00136-Y

• Putra, B. T. W., Wirayuda, H. C., Syahputra, W. N. H., & Prastowo, E. (2022). Evaluating in-situ maize chlorophyll content using an external optical sensing system coupled with conventional statistics and deep neural networks. Measurement, 189, Article 110482. https://doi.org/10.1016/J.MEASUREMENT.2021.110482

• Rahman, C. R., Arko, P. S., Ali, M. E., Khan, M. A. I., Apon, S. H., Nowrin, F., & Wasif, A. (2020). Identification and recognition of rice diseases and pests using convolutional neural networks. Biosystems Engineering, 194, 112-120. https://doi.org/10.1016/J.BIOSYSTEMSENG.2020.03.020

• Sai, K., Sood, N., & Saini, I. (2022). Classification of various nutrient deficiencies in tomato plants through electrophysiological signal decomposition and sample space reduction. Plant Physiology and Biochemistry, 186, 266-278. https://doi.org/10.1016/J.PLAPHY.2022.07.022

• Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4510-4520). IEEE Pubslishing. https://doi.org/10.1109/CVPR.2018.00474

• Sujatha, R., Chatterjee, J. M., Jhanjhi, N. Z., & Brohi, S. N. (2021). Performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems, 80, Article 103615. https://doi.org/10.1016/J.MICPRO.2020.103615

• Sutaji, D., & Yıldız, O. (2022). LEMOXINET: Lite ensemble MobileNetV2 and Xception models to predict plant disease. Ecological Informatics, 70, Article 101698. https://doi.org/10.1016/J.ECOINF.2022.101698

• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1-9). IEEE Publishing. https://doi.org/10.1109/CVPR.2015.7298594

• Wu, Z., Yang, R., Gao, F., Wang, W., Fu, L., & Li, R. (2021). Segmentation of abnormal leaves of hydroponic lettuce based on DeepLabV3+ for robotic sorting. Computers and Electronics in Agriculture, 190, Article 106443. https://doi.org/10.1016/J.COMPAG.2021.106443