Version 1
: Received: 18 July 2024 / Approved: 18 July 2024 / Online: 19 July 2024 (02:51:29 CEST)
How to cite:
Babu, G. R.; Gokuldhev, M.; Brahmanandam, P. S. Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Diseases in a Typical South India Station. Preprints2024, 2024071507. https://doi.org/10.20944/preprints202407.1507.v1
Babu, G. R.; Gokuldhev, M.; Brahmanandam, P. S. Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Diseases in a Typical South India Station. Preprints 2024, 2024071507. https://doi.org/10.20944/preprints202407.1507.v1
Babu, G. R.; Gokuldhev, M.; Brahmanandam, P. S. Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Diseases in a Typical South India Station. Preprints2024, 2024071507. https://doi.org/10.20944/preprints202407.1507.v1
APA Style
Babu, G. R., Gokuldhev, M., & Brahmanandam, P. S. (2024). Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Diseases in a Typical South India Station. Preprints. https://doi.org/10.20944/preprints202407.1507.v1
Chicago/Turabian Style
Babu, G. R., M. Gokuldhev and P. S. Brahmanandam. 2024 "Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Diseases in a Typical South India Station" Preprints. https://doi.org/10.20944/preprints202407.1507.v1
Abstract
This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters—humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K)—essential for assessing soil health and optimizing crop growth. Kendall's correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), and Logistic Regression (LR) were evaluated. A novel hybrid algorithm, 'Bayesian Optimization with KNN', was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
