i-PomDiagnoser: A Real-Time Pomegranate Disease Management System

Vaishali Nirgude; Sheetal Rathi

doi:10.17485/IJST/v17i14.57

Article

i-PomDiagnoser: A Real-Time Pomegranate Disease Management System

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i14.57

Year: 2024, Volume: 17, Issue: 14, Pages: 1391-1401

Original Article

i-PomDiagnoser: A Real-Time Pomegranate Disease Management System

Vaishali Nirgude^1*, Sheetal Rathi²

¹Research Scholar, Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, Maharashtra, India
²Professor, Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:11 January 2024, Accepted Date:27 February 2024, Published Date:30 March 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Designing and developing i-PomDiagnoser: a real-time pomegranate disease management system for disease detection, classification, prediction, recommending preventive measures, and analyzing abrupt climatic changes and their impact on pomegranates. Methods: A data collection framework has been designed and developed using an agriculture drone, sensors, camera, and other equipment to collect real field pomegranate images and micro-level parameters. Comprehensive Exploratory Data Analysis (EDA) and Feature Selection (FS) processes were carried out to improve the accuracy of disease classification and forecasting models. ML-based Binary, Multimodel, and Multilabel classifiers were implemented for disease classification. The models were trained on 11 years of historical data and tested on 5 months of actual field data. A hybrid pomegranate disease forecasting model has been developed for accurately forecasting micro-level parameters for the next 45 days to predict diseases. Findings: Micro-level (weather, soil, water) parameters specific to the agro-climatic zone were collected. The five most prominent distinct diseases are considered for experimentation namely Bacterial Blight (Telya), Anthracnose, Fruit spot, Fusarium Wilt, and Fruit borer. The proposed Improved Ensemble Multilabel Classifier (i-Ensemble-MLC) with a modified voting scheme has achieved a high classification accuracy of 95.82%, addressing model overfitting and data imbalance. Moreover, the hybrid pomegranate disease forecasting model, combining LSTM and i-Ensemble-MLC, demonstrated better performance with minimal error rates (MSE: 0.003, RMSE: 0.071, MAE: 0.048, R2: 0.7) compared to the existing model1 (MSE:0.037, MAE:0.028). Novelty: The novelty lies in the creation of the all-in-one model, i-PomDiagnoser. This innovative system helps the farmers to correctly detect and predict the most prominent diseases of pomegranate.

Keywords: Pomegranate, Agriculture, Disease Forecasting, Machine Learning, Deep Learning

References

Varsha M, Poornima B, Kumar MP, Basavarajappa S. Novel Hybrid ARIMA-BiLSTM Model for Forecasting of Rice Blast Disease Outbreaks for Sustainable Rice Production. Research Square Platform LLC. 2022. Available from: https://doi.org/10.21203/rs.3.rs-2103058/v1
Ankit P, Rashmi C, Krishan K. Analysis of agriculture input consumption by Indian Farmers. International Journal of Economic Plants. 2;2020(7):86–90. Available from: http://dx.doi.org/10.23910/2/2020.0369
Joshi PK, Varshney D. Agricultural Technologies in India: A Review. (pp. 1-92) National Bank for Agriculture and Rural Development . 2022.
Shekhar M, Singh N. The Impact of Climate Change on Changing Pattern of Maize Diseases in Indian Subcontinent: A Review. In: El-Esawi MA., ed. Maize Genetic Resources - Breeding Strategies and Recent Advances. IntechOpen. 2021.
Liliane TN, Charles MS. Factors Affecting Yield of Crops. In: Amanullah., ed. Agronomy - Climate Change & Food Security. IntechOpen. 2020.
Phadke M, Karandikar B, Gulati A. Grapes and Pomegranate Value Chains. In: Agricultural Value Chains in India, India Studies in Business and Economics. (pp. 145-193) Singapore. Springer. 2022.
Yadav DB, Sanap DJ, Pokharkar VG. Total Factor Productivity Growth in Pomegranate Crop of Maharashtra. Indian Journal of Agriculture Business. 2020;6(1):9–14. Available from: https://journals.indexcopernicus.com/api/file/viewByFileId/1073776.pdf
Nirgude V, Rathi S. Improving the accuracy of real field pomegranate fruit diseases detection and visualization using convolution neural networks and grad-CAM. International Journal of Data Analysis Techniques and Strategies. 2023;15(1-2):57–75. Available from: https://doi.org/10.1504/IJDATS.2023.132562
Xiao Q, Li W, Kai Y, Chen P, Zhang J, Wang B. Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network. In: BMC Bioinformatics: Proceedings of the 2018 International Conference on Intelligent Computing (ICIC 2018) and Intelligent Computing and Biomedical Informatics (ICBI) 2018 conference: bioinformatics. (Vol. 20, Supplement 25, pp. 1-15) Springer Science and Business Media LLC. 2019.
Haque ASMFA, Islam GMR, Hafiz R, Hakim MA. A Computer Vision System for Guava Disease Detection and Recommend Curative Solution Using Deep Learning Approach. In: 22nd International Conference on Computer and Information Technology (ICCIT). (pp. 1-6) IEEE. 2020.
Militante SV, Gerardo BD. Detecting Sugarcane Diseases through Adaptive Deep Learning Models of Convolutional Neural Network. In: 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS). (pp. 1-5) IEEE. 2020.
Liu B, Ding Z, Zhang Y, He D, He J. Kiwifruit Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. In: 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). (pp. 1267-1272) IEEE. 2020.
Ahmad J, Jan B, Farman H, Ahmad W, Ullah A. Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions. Sensors. 2020;20(19):1–18. Available from: https://doi.org/10.3390/s20195569
Yebasse M, Shimelis B, Warku H, Ko J, Cheoi KJ. Coffee Disease Visualization and Classification. Plants. 2021;10(6):1–13. Available from: https://doi.org/10.3390/plants10061257
Wakhare PB, Kandalkar JA, Kawtikwar RS, Kalme SA, Patil RV. Development of Automated Leaf Disease Detection in Pomegranate Using Alexnet Algorithm. Current Agriculture Research Journal. 2023;11(1):177–185. Available from: http://dx.doi.org/10.12944/CARJ.11.1.15
Wakhare PB, Neduncheliyan S. Using Image Processing and Deep Learning Techniques Detect and Identify Pomegranate Leaf Diseases. Indian Journal of Science and Technology. 2023;16(18):1323–1331. Available from: https://doi.org/10.17485/IJST/v16i18.768
Jawade P, Chaugule D, Patil DM, Shinde HV. Disease Prediction of Mango Crop Using Machine Learning and IoT. In: Advances in Decision Sciences, Image Processing, Security and Computer Vision, Learning and Analytics in Intelligent Systems. (Vol. 3, pp. 254-260) Springer, Cham. 2019.
TW, YH, HS, BSA. Enhanced LSTM Multivariate Time Series Forecasting for Crop Pest prediction. ICIC Express Letters. 2020;14(10):943–949. Available from: http://www.icicel.org/ell/contents/2020/10/el-14-10-02.pdf
Suleman MAR, Shridevi S. Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model. IEEE Access. 2022;10:82456–82468. Available from: https://doi.org/10.1109/ACCESS.2022.3196381
Liu B, Ding Z, Tian L, He D, Li S, Wang H. Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. Frontiers in Plant Science. 2020;11:1–14. Available from: https://doi.org/10.3389/fpls.2020.01082
Vasumathi MT, Kamarasan M. An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models. Indian Journal of Science and Technology. 2021;14(16):1310–1319. Available from: https://doi.org/10.17485/IJST/v14i16.432
VN, SR. Exploratory Data Analysis and Optimal Feature Selection on Pomegranate Dataset for Enhancing the Performance of Pomegranate Disease Prediction. Annals of Forest Research. 2022;65(1):746–764. Available from: https://www.e-afr.org/login/pdf/Vaishali%20Nirgude.pdf
Doddaraju P, Kumar P, Gunnaiah R, Gowda AA, Lokesh V, Pujer P, et al. Reliable and early diagnosis of bacterial blight in pomegranate caused by Xanthomonas axonopodis pv. punicae using sensitive PCR techniques. Scientific Reports. 2019;9(1):1–9. Available from: https://doi.org/10.1038/s41598-019-46588-9

Copyright

© 2024 Nirgude & Rathi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)