PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• 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