Quick Links
|
International Journal of Advanced Innovative Technology in Engineering (IJAITE)A Metaheuristic-based Deep Learning Model for Accurate Crop Recommendation Using Agro-Environmental Data Sachin Narayan Joshi, Dr. Avinash B. Manwar, Dr. Mohammad Atique Abstract : Accurate crop recommendation is essential for sustainable agriculture, yet existing machine learning and deep learning models often struggle with high-dimensional, redundant soil and environmental features, leading to reduced generalization in real-world settings. To address this limitation, this study presents a hybrid Firefly optimized Autoencoder model that integrates nonlinear representation learning with metaheuristic feature refinement. The Autoencoder compresses NPK nutrient values, temperature, humidity, rainfall, and pH into a compact latent space, while the Firefly Algorithm selects the most discriminative latent dimensions by maximizing feature variance. A deep neural classifier trained on the optimized features achieves highly precise multi-class prediction. The final results tested on a benchmark 22-crop dataset, conducted using Google Colab Pro, shows that the proposed model attains 99.32% accuracy, outperforming advanced methods such as TCN (99.09%), CNNβLSTM federated learning (98.77%), and tuned Random Forest (99.05%). The results highlight the significance of combining latent-space learning with swarm intelligence to deliver a robust, scalable, and high-accuracy crop recommendation system. Keywords : Crop Recommendation, Autoencoder, Firefly Algorithm, Feature Selection, Agricultural Decision Support Full Text : Download PDF DOI : 10.65809/IJAITE/26/v11i01/002 Cite this paper : - References : [1] Sardeshmukh, V. S., Patil, S. G., & Bedage, S. (2025). An AI Driven Smart Crop Recommendation and Advisory Framework. Deleted Journal, 3(07), 3209β3218. https://doi.org/10.47392/irjaeh.2025.0472 [2] Shaterian, M. (2023). A Comprehensive Update on Traditional Agricultural Knowledge of Farmers in India (pp. 331β386). https://doi.org/10.1007/978-981-19 6502-9_14 [3] Prity, F. S., Hasan, M. M., Saif, S. H., Hossain, Md. M., Bhuiyan, S. H., Islam, Md. A., & Lavlu, M. T. H. (2024). Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations. Human-Centric Intelligent Systems. https://doi.org/10.1007/s44230 024-00081-3 [4] S. Palei and P. Mohapatra, "Optimizing Crop Selection using Deep Machine Learning Approaches," 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India, 2025, pp. 1-6, doi: 10.1109/IATMSI64286.2025.10985118. [5] MD Shaifullah Sharafat, Nilavro Das Kabya, Rahimul Islam Emu, Mehrab Uddin Ahmed, Jakaria Chowdhury Onik, Mohammad Aminul Islam, Riasat Khan, An IoT-enabled AI system for real-time crop prediction using soil and weather data in precision agriculture, Smart Agricultural Technology, Volume 12, 2025, 101263, https://doi.org/10.1016/j.atech.2025.101263. [6] Devi, M. K., Sam, D., Raj, A., & Sharma, A. K. (2024). Crop Recommendation System Using Machine Learning. Indian Scientific Journal Of Research In Engineering And Management. https://doi.org/10.55041/ijsrem28776 [7] Mavi, H., Upadhyay, S. K., Srivastava, N., Sharma, R., & Bhargava, R. (2024). Crop Recommendation System Based on Soil Quality and Environmental Factors Using Machine [1] Sardeshmukh, V. S., Patil, S. G., & Bedage, S. (2025). An AI Driven Smart Crop Recommendation and Advisory Framework. Deleted Journal, 3(07), 3209β3218. https://doi.org/10.47392/irjaeh.2025.0472 [2] Shaterian, M. (2023). A Comprehensive Update on Traditional Agricultural Knowledge of Farmers in India (pp. 331β386). https://doi.org/10.1007/978-981-19 6502-9_14 [3] Prity, F. S., Hasan, M. M., Saif, S. H., Hossain, Md. M., Bhuiyan, S. H., Islam, Md. A., & Lavlu, M. T. H. (2024). Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations. Human-Centric Intelligent Systems. https://doi.org/10.1007/s44230 024-00081-3 [4] S. Palei and P. Mohapatra, "Optimizing Crop Selection using Deep Machine Learning Approaches," 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India, 2025, pp. 1-6, doi: 10.1109/IATMSI64286.2025.10985118. [5] MD Shaifullah Sharafat, Nilavro Das Kabya, Rahimul Islam Emu, Mehrab Uddin Ahmed, Jakaria Chowdhury Onik, Mohammad Aminul Islam, Riasat Khan, An IoT-enabled AI system for real-time crop prediction using soil and weather data in precision agriculture, Smart Agricultural Technology, Volume 12, 2025, 101263, https://doi.org/10.1016/j.atech.2025.101263. [6] Devi, M. K., Sam, D., Raj, A., & Sharma, A. K. (2024). Crop Recommendation System Using Machine Learning. Indian Scientific Journal Of Research In Engineering And Management. https://doi.org/10.55041/ijsrem28776 [7] Mavi, H., Upadhyay, S. K., Srivastava, N., Sharma, R., & Bhargava, R. (2024). Crop Recommendation System Based on Soil Quality and Environmental Factors Using Machine [8] Learning. 507β512. https://doi.org/10.1109/innocomp63224.2024.00089 [9] Lamba, R., Chauhan, P., Rani, P., Sachdeva, R. K., Jain, A., & Choudhury, T. (2024). Precision Agriculture: A Machine Learning Approach to Crop Recommendation. 1281β1286. https://doi.org/10.1109/ictacs62700.2024.10841092 [10] Gireesh, N. P. (2023). A Comprehensive Study on Crop Recommendation System for Precision Agriculture Using Machine Learning Algorithms. 1, 2(1), 30β36. https://doi.org/10.46632/eae/2/1/5 [11] Saritha, V., Sri, M., Varshitha, P., Kumar, P., & Vinay, T. (2024). An experimental analysis of machine learning techniques for crop recommendation. Nigerian Journal of Technology, 43(2), 301β308. https://doi.org/10.4314/njt.v43i2.13 [12] Ghosh, A., Mohapatra, S. K., Pattanaik, P., Dash, P. K., & Chakravarty, S. (2024). A Comprehensive Crop Recommendation System Integrating Machine Learning and Deep Learning Models. 1β6. https://doi.org/10.1109/ic-cgu58078.2024.10530724 [13] Shingade, S. D., & Mudhalwadkar, R. (2022). Hybrid deepβQ Elman neural network for crop prediction and recommendation based on environmental changes. Concurrency and Computation: Practice and Experience, 34(17). https://doi.org/10.1002/cpe.6991 [14] Srilatha, A., & Praveen, P. (2024). Deep Learning for Farmland Assessment and Developing an Automatic Crop Recommendation System Using GCN. 1735β1741. https://doi.org/10.1109/icicnis64247.2024.10823317 [15] Gopi, S. R., & Karthikeyan, M. (2023). Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning. https://doi.org/10.48084/etasr.6092 [16] Sindhur, N. M., Pavithra, C., & Muchikel, N. (2025). A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting. arXiv.Org, abs/2507.08832. https://doi.org/10.48550/arxiv.2507.08832 [17] Cathciyal, A. G., D., V., Amirtha, S., & P. (2023). Crop Recommendation System using hybrid of KNN and Random Forest Classifier. International Journal For Multidisciplinary Research, 5(2). https://doi.org/10.36948/ijfmr.2023.v05i02.1666 [18] Sivakolunthu, D. A., & Ramajayam, P. K. (2024). Serial Cascaded Deep Feature Extraction-based Adaptive Attention Dilated model for Crop Recommendation Framework. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2024.111790 [19] Sonai Muthu Anbananthen, K., Subbiah, S., Chelliah, D., Sivakumar, P., Somasundaram, V., Velshankar, K. H., & Khan, M. K. A. A. (2021). An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms. F1000Research, 10, 1143. https://doi.org/10.12688/F1000RESEARCH.73009.1 [20] Prity, F. S., Hasan, M. M., Saif, S. H., Hossain, Md. M., Bhuiyan, S. H., Islam, Md. A., & Lavlu, M. T. H. (2024). Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations. Human-Centric Intelligent Systems. https://doi.org/10.1007/s44230-024-00081-3 [21] Karna, N., Hertiana, S. N., Putra, M. A. P., Utami, N. W., Putra, I. G. J. E., Rahyuni, D., Kim, D. S., & Lee, J.-M. (2024). Towards Precision Agriculture Using Federated Learning-Driven Crop Recommendation System. 1538β1542. https://doi.org/10.1109/ictc62082.2024.10827163 [22] Upadhyay, S. K., & Vikas. (2024). Intelligent Crop Recommendation using Machine Learning. 330β335. https://doi.org/10.1109/autocom60220.2024.10486182 [23] Kristuboyina, A., Kornepati, S. S., Mannem, M., Ch Suresh, B., & Siva Satya, S. P. (2024). Soil-Based Crop Recommendation System Using Machine Learning. 1β6. https://doi.org/10.1109/adics58448.2024.10533537 [24] Changela, A., Kumar, Y., & Koul, A. (2023). Machine Learning-based Approaches for Crop Recommendations and Prediction. 370β376. https://doi.org/10.1109/iccsai59793.2023.10421406 [25] Suresh, A., Geetha, B., Lavanya, V. M., & Kumar, R. G. (2024). A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model. 1β7. https://doi.org/10.1109/icicacs60521.2024.10498984 [26] Yang, L. (2022). HIAS: Hybrid Intelligence Approach for Soil Classification and Recommendation of Crops (pp. 81β94). https://doi.org/10.1007/978-3-031-22950-3_7 |