Enhancing Disease Management in Mango Cultivation: A Machine Learning Approach to Classifying Leaf Diseases
Abstract
This study explores the application of machine learning techniques in the agricultural domain, focusing on the classification of two common diseases in mango leaves: Powdery Mildew and Sooty Mould. Utilizing the MangoLeafBD dataset, the research employs a Gradient Boosting Classifier, enhanced with mean shift image segmentation and Hu moments for feature extraction. The performance of the model was rigorously evaluated through 5-fold cross-validation, yielding insights into its accuracy, precision, recall, and F1-score. The results demonstrate moderate success, with the highest accuracy and precision observed in the initial fold, indicating the model's potential for reliable disease identification. The study addresses the challenge of distinguishing between diseases with similar symptomatic appearances, offering a novel, data-driven approach for disease management in mango cultivation. This research contributes to the growing field of precision agriculture, highlighting the potential of machine learning in enhancing disease diagnosis and treatment strategies, thus supporting sustainable agricultural practices.
Downloads
References
D. K. Thai, “Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads,” Eng Comput, vol. 37, no. 1, pp. 597–608, 2021, doi: 10.1007/s00366-019-00842-w.
B. P. Sari, “Classification System for Cervical Cell Images based on Hu Moment Invariants Methods and Support Vector Machine,” 2021 International Conference on Intelligent Technologies, CONIT 2021, 2021, doi: 10.1109/CONIT51480.2021.9498353.
S. S. Gornale, “Automatic Detection and Classification of Knee Osteoarthritis Using Hu’s Invariant Moments,” Front Robot AI, vol. 7, 2020, doi: 10.3389/frobt.2020.591827.
A. Larestani, “Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree,” Alexandria Engineering Journal, vol. 61, no. 10, pp. 7715–7731, 2022, doi: 10.1016/j.aej.2022.01.023.
I. Aulia, “Rice Quality Detection Using Gradient Tree Boosting Based on Electronic Nose Dataset,” AIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems, 2021, doi: 10.1109/AIMS52415.2021.9466073.
R. Tian, “Sobel edge detection based on weighted nuclear norm minimization image denoising,” Electronics (Switzerland), vol. 10, no. 6, pp. 1–15, 2021, doi: 10.3390/electronics10060655.
T. Wu, “Image Edge Detection Based on Sobel with Morphology,” IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021, pp. 1216–1220, 2021, doi: 10.1109/ITNEC52019.2021.9586895.
Y. Harshavardhan, “Comparative analysis of accuracy in identification of bone fracture detection using Prewitt edge detection with Sobel edge detection approach,” AIP Conf Proc, vol. 2822, no. 1, 2023, doi: 10.1063/5.0173412.
S. AbuRass, “Enhancing Convolutional Neural Network using Hu’s Moments,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 12, pp. 130–137, 2020, doi: 10.14569/IJACSA.2020.0111216.
G. Paneiro, “Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling,” Underground Space (China), vol. 6, no. 3, pp. 281–289, 2021, doi: 10.1016/j.undsp.2020.03.002.
Z. P. Brodeur, “Bootstrap Aggregation and Cross-Validation Methods to Reduce Overfitting in Reservoir Control Policy Search,” Water Resour Res, vol. 56, no. 8, 2020, doi: 10.1029/2020WR027184.
D. Cahyanti, A. Rahmayani, and S. Ainy, “Analisis performa metode Knn pada Dataset pasien pengidap Kanker Payudara,” Indonesian Journal of Data and Science, vol. 1, no. 2, pp. 39–43, 2020.
Hasran, “Klasifikasi Penyakit Jantung Menggunakan Metode K-Nearest Neighbor,” Indonesian Journal of Data and Science, vol. 1, no. 1, pp. 1–4, 2020.
D. Pradana, M. Luthfi Alghifari, M. Farhan Juna, and D. Palaguna, “Klasifikasi Penyakit Jantung Menggunakan Metode Artificial Neural Network,” Indonesian Journal of Data and Science, vol. 3, no. 2, pp. 55–60, 2022, doi: 10.56705/ijodas.v3i2.35.
O. Karal, “Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation,” Proceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020, 2020, doi: 10.1109/ASYU50717.2020.9259880.
S. Ortiz-Toquero, “Classification of Keratoconus Based on Anterior Corneal High-order Aberrations: A Cross-validation Study,” Optometry and Vision Science, vol. 97, no. 3, pp. 169–177, 2020, doi: 10.1097/OPX.0000000000001489.
A. A. Ewees, “Performance analysis of Chaotic Multi-Verse Harris Hawks Optimization: A case study on solving engineering problems,” Eng Appl Artif Intell, vol. 88, 2020, doi: 10.1016/j.engappai.2019.103370.
M. Khushi, “A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data,” IEEE Access, vol. 9, pp. 109960–109975, 2021, doi: 10.1109/ACCESS.2021.3102399.
K. Nidhul, “Enhanced thermo-hydraulic performance in a V-ribbed triangular duct solar air heater: CFD and exergy analysis,” Energy, vol. 200, 2020, doi: 10.1016/j.energy.2020.117448.
D. İzci, “Comparative performance analysis of slime mould algorithm for efficient design of proportional–integral–derivative controller,” Electrica, vol. 21, no. 1, pp. 151–159, 2021, doi: 10.5152/ELECTRICA.2021.20077.
B. Cao, “Performance analysis and comparison of PoW, PoS and DAG based blockchains,” Digital Communications and Networks, vol. 6, no. 4, pp. 480–485, 2020, doi: 10.1016/j.dcan.2019.12.001.
M. E. Zayed, “Performance prediction and techno-economic analysis of solar dish/stirling system for electricity generation,” Appl Therm Eng, vol. 164, 2020, doi: 10.1016/j.applthermaleng.2019.114427.
J. N. Archana, “Enhancement of digital chest images using a modified Sobel edge detection algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 3, pp. 1718–1726, 2021, doi: 10.11591/ijeecs.v24.i3.pp1718-1726.
Y. Chung, “Comparison of Approximate Computing with Sobel Edge Detection,” IEIE Transactions on Smart Processing and Computing, vol. 10, no. 4, pp. 355–361, 2021, doi: 10.5573/IEIESPC.2021.10.4.355.
A. Nurul, Y. Salim, and H. Azis, “Analisis performa metode Gaussian Naïve Bayes untuk klasifikasi citra tulisan tangan karakter arab,” Indonesian Journal of Data and Science, vol. 3, no. 3, pp. 115–121, 2022, doi: https://doi.org/10.56705/ijodas.v3i3.54.
H. Azis, F. T. Admojo, and E. Susanti, “Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah,” Techno.Com, vol. 19, no. 3, 2020, [Online]. Available: file:///Users/kbh/Library/Application Support/Mendeley Desktop/Downloaded/Azis, Admojo, Susanti - 2020 - Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah.pdf
Copyright (c) 2024 Indonesian Journal of Data and Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- The work is not under consideration for publication elsewhere.
- The work has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Indonesian Journal of Data and Science agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (CC BY-NC 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
