Pest and Disease Monitoring System for Banana Lakatan Farming using CNN Algorithm
DOI:
https://doi.org/10.54536/ajaset.v10i2.7712Keywords:
Automated Spraying, CNN Algorithm, Lakatan Banana, Pest Disease Monitoring System, Smart AgricultureAbstract
Pest and disease control is crucial in maintaining the quality and productivity of Lakatan bananas. But conventional pest and disease monitoring approaches are inefficient and may delay the detection of pests and diseases. To overcome this problem, we built a farm control system, the Pest and Disease Monitoring System for Banana Lakatan Farming using Convolutional Neural Network (CNN) algorithm. This system employs a camera to take a photo of the banana leaves, then the CNN model classifies the image to determine the presence of pests and diseases. If a pest or disease is detected, the system automatically turns on a pump to apply the correct chemical to treat the fruit. It also includes an ultrasonic sensor to detect the level of chemicals in the tank and a voltage sensor to detect the power system and the battery. Users can also monitor and analyses the data obtained. In summary, the system offers an efficient and automated approach to pest and disease control for Lakatan banana farming, which saves farmers' time and help them increase their fruit yield with the timely detection and response.
Downloads
References
Alcázar-Fernández, A., De-La-Llana-Calvo, Á., Lázaro-Galilea, J. L., Pérez-Navarro, A., Gil-Vera, R., & Gardel-Vicente, A. (2024). Seamless mobile indoor navigation with VLP-PDR. IEEE Sensors Journal, 24(7), 11504–11514. https://doi.org/10.1109/JSEN.2024.3368169
Ancheta, J. B., Santos, L. Q., Avelino, A. M., De Jesus, L. C. M., Grande, M. E. A., & David, J. V. (2020). Development of a classification model for banana leaf disease using Google Teachable Machine. Lyceum of the Philippines University – Cavite, Asia Pacific College, and Ateneo de Manila University.
Ancheta, J. B., et al., (2025). Implemented banana leaf disease classification model using MobileNetV2 and Google Teachable Machine. Dataset of 3,264 images across seven disease categories; achieved 98.17% accuracy.
Arifin, N. (2025). Classification of banana leaf diseases using a GoogleNet-based convolutional neural network architecture. Jurnal Krisnadana, 4(2), 95–102.https://doi.org/10.58982/krisnadana.v4i2.749
Bhuiyan, M. A. B., Abdullah, H. M., Arman, S. E., Rahman, S. S., & Mahmud, K. A. (2023). BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases. Smart Agricultural Technology, 3, 100214. https://doi.org/10.1016/j.atech.2023.100214
Elinisa, C. A., & Mduma, N. (2024). Mobile-based convolutional neural network model for the early identification of banana diseases. Smart Agricultural Technology, 7, 100423. https://doi.org/10.1016/j.atech.2024.100423
Elinisa, C. A., Maina, C. W., Vodacek, A., & Mduma, N. (2025). Image segmentation deep learning model for early detection of banana diseases. Applied Artificial Intelligence, 39(1), e2440837. https://doi.org/10.1080/08839514.2024.2440837
Erasmo, L. G. A., & Perito, R. M. L. (2025). AI-based banana disease classification using CNN MobileNetV2 model. SSRN Electronic Journal., https://ssrn.com/abstract=4891589
Figorilli, S., Moscovini, L., Vasta, S., Tocci, F., Violino, S., Abraham, D., … & Pallottino, F. (2025). Smart IoT device for in-field Black Sigatoka disease recognition and mapping. Smart Agricultural Technology, 10, 100762. https://doi.org/10.1016/j.atech.2024.100762
Geneta, Y. H., Sinshaw, N. T., Assefa, B. G., & Mohapatra, S. K. (2024). Sigatoka and Xanthomonas banana leaf disease detection via transfer learning. Scientia Iranica, 31(21), 1939–1947. https://doi.org/10.24200/sci.2024.62306.7766
Geslani et al., (2023). Performance analysis of Cavendish banana ripeness detection using deep neural networks (DNN) and convolutional neural networks (CNN). Mapúa Institute of Technology at Laguna.
Gerance et al., (2025). SaBaTech: A banana fruit pest and disease detection web application. American Journal of Data Science and Artificial Intelligence, 1(1), 14–20. https://journals.e-palli.com/home/index.php/ajdsai
Ibarra et al., (2023). Detection of Panama disease on banana leaves using the YOLOv4 algorithm. Proceedings of the 15th International Conference on Computer and Automation Engineering (ICCAE). IEEE. https://doi.org/10.1109/ICCAE56788.2023.10111416
Jiménez et al., (2025). Detection of leaf diseases in banana crops using deep learning techniques. AI, 6(3), 61. https://doi.org/10.3390/ai6030061
Kalaivani et al., (2024). Mobile application for banana leaf spot disease detection using CNN. Journal of Agricultural Informatics, 15(2), 45–53.
Keerthana et al., (2024). Framework to relate soil mineral deficiencies with banana diseases using predictive algorithms. Agricultural Systems Research, 12(4), 210–223.
Lantican et al., (2023). Comparative RNA-seq analysis of resistant and susceptible banana genotypes reveals molecular mechanisms in response to banana bunchy top virus (BBTV) infection. Scientific Reports, 13, 18719. https://doi.org/10.1038/s41598-023-45937-z
Lasco et al., (2024). Detection of diseases on bananas (Musa sp.) using image processing and machine learning techniques. International Journal of Advanced Research, 12(12), 697–711. https://doi.org/10.21474/IJAR01/20069
Narayanan, L., et al., (2022). Banana plant disease classification using hybrid convolutional neural network. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/9153699
Pauya et al., (2024). Enhancing growth and yield of ‘Lakatan’ banana (Musa acuminata) using fish amino acid (FAA) application. International Journal on Agricultural Sciences, 15(1), 30–37. https://www.researchgate.net/publication/381671258_ENHANCING_GROWTH_AND_YIELD_OF_%27LAKATAN%27_BANANA_Musa_acuminata_USING_FISH_AMINO_ACID_FAA_APPLICATION
Pine, W. V., & Ricaña, J. (2025). Development and performance evaluation of the deep learning model for classifying banana pest and diseases. Ignatian International Journal for Multidisciplinary Research, 3(1). https://doi.org/10.5281/zenodo.14637055
Rajalakshmi et al., (2025). Early detection of banana leaf disease using novel deep convolutional neural network. Journal of Data Science and Intelligent Systems, 3(3), 192–199. https://doi.org/10.47852/bonviewJDSIS42021530
Rohini, & Raghavendra (2025). CNN-based automated system for diagnosing four diseases of banana leaves. International Journal of Advanced Agricultural Research, 11(2), 101–110.
Shafay, R., et al., (2025). Review of deep learning methods for plant disease detection using RGB and hyperspectral imaging. Computers and Electronics in Agriculture, 215, 108933. https://doi.org/10.1016/j.compag.2025.108933
Syihad et al., (2023). CNN method to identify banana plant diseases based on banana leaf images using ResNet50 and VGG-19. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(6), 1309–1318. https://doi.org/10.29207/resti.v7i6.5000
Yan et al., (2023). A transfer learning-based deep convolutional neural network for detection of Fusarium wilt in banana crops. AgriEngineering, 5, 2381–2394. https://doi.org/10.3390/agriengineering5040146
