COMPARISON OF MOBILENET AND CNN METHODS FOR IDENTIFYING TOMATO LEAF DISEASES

Diky Andrianto; Rastri Prathivi; Meifang Liu

doi:10.52436/1.jutif.2024.5.6.3477

Diky Andrianto Informatics, Faculty of Information Technology and Communication, Semarang University, Indonesia
Rastri Prathivi Informatics Faculty of Information Technology and Communication, Semarang University, Indonesia
Meifang Liu School of Computer Science and Technology, Anhui University of Technology, China

DOI: https://doi.org/10.52436/1.jutif.2024.5.6.3477

Keywords: cnn, deep learning, leaf, identification, tomato

Abstract

Tomato plants are usually easily attacked by diseases, either viruses or fungi, resulting in a significant reduction in the quality and quantity of crop production. Tomato production is at risk from various diseases affecting the leaves. Early diagnosis of these diseases allows farmers to take preventive action and protect their crops. The use of artificial intelligence, especially deep learning, has greatly improved plant disease detection systems. Advances in computer vision, particularly Convolutional Neural Networks (CNN), have shown reliable results in image classification and identification. Below is previous research on identifying tomato leaf diseases.

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References

FAO Food and Agriculture Organization of United States. Available online: https://www.fao.org/land-water/databases-and-software/crop-information/tomato/en/ (accessed on 2 January 2023).

ALNAMOLY, Mahmoud H., et al. “FL-ToLeD: An Improved Lightweight Attention Convolutional Neural Network Model for Tomato Leaf Diseases Classification for Low-end Devices”. IEEE Access, 2024, vol 12, pp. 73561 – 73580, doi: 10.1109/ACCESS.2024.3401733.

Chen, H.C.; Widodo, A.M.; Wisnujati, A.; Rahaman, M.; Lin, J.C.W.; Chen, L.; Weng, C.E. “AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf” Electronics, 2022, vol. 11, no. 6, pp. 951, doi: 10.3390/electronics11060951.

Vadivel, T.; Suguna, R. “Automatic recognition of tomato leaf disease using fast enhanced learning with image processing” Acta Agric. Scand. Sect. B Soil Plant Sci , 2022,vol. 72, pp. 312–324, doi: 10.1080/09064710.2021.1976266.

J. van Heghe and C. Sirois, “Tomato Processing Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2023–2028”. 2023. Accessed: 19 September, 2024. Available: https://www. giiresearch.com/report/imarc1206560-tomato-processing-market-global-industry-trends.html?

International Market Analysis Research and Consulting Group, “Tomato Processing Market: Industry Trends & Forecast 2024-2032”. 2023.Accessed: Dec. 22, 2023. Available: https://www.imarcgroup. com/tomato-processing-plant.

Sanida, M. V., Sanida, T., Sideris, A., & Dasygenis, M. “An efficient hybrid CNN classification model for tomato crop disease” Technologies, 2023, vol. 11, no. 1, pp. 10, doi: 10.3390/technologies11010010.

Nguyen, H. T., Luong, H. H., Huynh, L. B., Le, B. Q. H., Doan, N. H., & Le, D. T. D. “An Improved MobileNet for disease detection on tomato leaves” Advances in Technology Innovation, 2023, vol. 8, no. 3, pp.192-209. doi:10.46604/aiti.2023.11568.

Oyesola, O. L.; Tonjock, R. K.; Bello, A. O.; Taiwo, O. S.; Obembe, O. O. Trichoderma: A Review of Its Mechanisms of Action in Plant Disease Control. Preprints 2024,2024051378, doi:10.20944/preprints202405.1378.v1.

Shalaby T.A., Taha N.A., Taher D.I., Metwaly M.M., El-Beltagi H.S., Rezk A.A., El-Ganainy S.M., et al. “Paclobutrazol Improves the Quality of Tomato Seedlings to Be Resistant to Alternaria solani Blight Disease: Biochemical and Histological Perspectives”.Plants, 2022, vol. 11, no. 3, pp. 425,doi: 10.3390/plants11030425.

HUANG, Mei-Ling; CHUANG, Tzu-Chin; LIAO, Yu-Chieh. “Application of transfer learning and image augmentation technology for tomato pest identification”. Sustainable Computing: Informatics and Systems, 2022, vol 33, pp.100646. doi: 10.1016/j.suscom.2021.100646.

Belouz, Khaled, et al. Prediction of greenhouse tomato yield using artificial neural networks combined with sensitivity analysis. Scientia Horticulturae, 2022, vol. 293, pp.110666, doi: 10.1016/j.scienta.2021.110666.

Bi, C., Wang, J., Duan, Y. et al. “MobileNet Based Apple Leaf Diseases Identification”. Mobile Netw Appl, 2022, vol. 27, pp. 172–180, doi:10.1007/s11036-020-01640-1.

Gokulnath, B. V. “Identifying and classifying plant disease using resilient LF-CNN”. Ecological Informatics, 2021, vol 63, pp. 101283, doi: 10.1016/j.ecoinf.2021.101283.

Singh, V., Chug, A., & Singh, A. P. “Classification of beans leaf diseases using fine tuned cnn model”. Procedia Computer Science, 2023, vol. 218, pp. 348-356, doi: 10.1016/j.procs.2023.01.017.

Li M, Zhou G, Chen A, Li L, Hu Y.”Identification of tomato leaf diseases based on LMBRNet”. Engineering Applications of Artificial Intelligence, 2023, vol. 123, 1 Aug, pp. 106195, doi: 10.1016/j.engappai.2023.106195.

Amitava, Priya Raj, Sukumar. “Mobile app-based tomato disease identification with fine-tuned convolutional neural networks”. Computers and Electrical Engineering, 2023, 112: 108995.

Zhang L, Sun J, Yang M. “An Improved MobileNet Model Integrated Spatial and Channel Attention Mechanisms for Tea Disease”. International Journal of Advanced Computer Science & Applications, 2024, vol. 15, issue 5, pp.601, doi:10.14569/ijacsa.2024.0150560.

An Overview on MobileNet: An Efficient Mobile Vision CNN, https://medium.com/@godeep48/an-overview-on-mobilenet-an-efficient-mobile-vision-cnn-f301141db94d (accessed on 3 September 2024)

Ulutaş, H., & Aslantaş, V.”Design of efficient methods for the detection of tomato leaf disease utilizing proposed ensemble CNN model”. Electronics 2023, vol. 12, issue 4, pp. 827. doi: 10.3390/electronics12040827.