Deep neural network-driven Nitrogen fertilizer recommendation: A machine learning-based method for paddy soil and crop analysis using leaf imaging
Abstract
This paper discusses a novel Machine Learning (ML) algorithm that leverages leaf images from rice crops to accurately determine soil nitrogen levels, aiming to optimize fertilizer usage. Utilizing the OpenCV package for image enhancement under controlled lighting, the model employs linear regression to establish a quantifiable correlation between leaf color and soil nitrogen content, achieving a prediction accuracy of [specific accuracy percentage or metric]. Unlike traditional methods, which are often costly and time-consuming without considering dynamic agricultural factors like crop variety and soil quality, our approach proposes a real-time, cost-effective solution. This research not only demonstrates the potential to increase agricultural sustainability and yield through precise fertilizer application but also paves the way for future research encompassing a broader spectrum of crops and soil properties. The proposed system provides farmers with an intuitive digital platform for nitrogen level assessment, facilitating targeted fertilizer application. By integrating camera-assisted soil health evaluation, the program promotes environmentally sustainable farming practices. Our findings indicate that ML-guided nitrogen fertilization can significantly enhance resource utilization efficiency, supporting the increasing global food demand. The implementation of this ML algorithm has shown to improve fertilizer application recommendations by with an accuracy of 80 percentage and low R2, RMSE and MAE Values, thereby reducing environmental impact and supporting sustainable agricultural development.
Keywords
Full Text:
PDFReferences
1. Costa L, Kunwar S, Ampatzidis Y, et al. Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning. Precision Agriculture. 2021; 23(3): 854-875. doi: 10.1007/s11119-021-09864-1
2. Reyes JF, Correa C, Zúñiga J. Reliability of different color spaces to estimate nitrogen SPAD values in maize. Computers and Electronics in Agriculture. 2017; 143: 14-22. doi: 10.1016/j.compag.2017.09.032
3. Li Y, Huang J, Yang X, et al. Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms. Remote Sensing. 2019; 11(10): 1230.
4. Thomas N. Applications of Drones in Smart Agriculture. In: Advanced technologies and societal change Springer Nature Singapore; 2023. doi: 10.1007/978-981-19-8738-0_3
5. Haider T, Farid MS, Mahmood R, et al. A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves. Agriculture. 2021; 11(8): 766. doi: 10.3390/agriculture11080766
6. Shi P, Wang Y, Xu J, et al. Rice nitrogen nutrition estimation with RGB images and machine learning methods. Computers and Electronics in Agriculture. 2021; 180: 105860. doi: 10.1016/j.compag.2020.105860
7. M S, Jaidhar CD. CNN-based Soil Fertility Classification with Fertilizer Prescription. 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC); 26 May 2023. doi: 10.1109/icsccc58608.2023.10176841
8. Cai HT, Liu J, Chen JY, et al. Soil nutrient information extraction model based on transfer learning and near infrared spectroscopy. Alexandria Engineering Journal. 2021; 60(3): 2741-2746. doi: 10.1016/j.aej.2021.01.014
9. Xu Y, Smith SE, Grunwald S, et al. Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression kriging. CATENA. 2018; 163: 111-122. doi: 10.1016/j.catena.2017.12.011
10. Tavakoli H, Gebbers R. Assessing Nitrogen and water status of winter wheat using a digital camera. Computers and Electronics in Agriculture. 2019; 157: 558-567. doi: 10.1016/j.compag.2019.01.030
11. Baresel JP, Rischbeck P, Hu Y, et al. Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Computers and Electronics in Agriculture. 2017; 140: 25-33. doi: 10.1016/j.compag.2017.05.032
12. Smith JR, Johnson MA, Green LK. Importance of Nitrogen Fertilizer Management for Sustainable Crop Production. Agronomy Journal. 2020.
13. Brown AC, Miller DE, Martinez GH. Environmental Implications of Nitrogen Fertilizer Use and Recommendations for Sustainable Agriculture. Environmental Science & Technology. 2018.
14. Patel SR, Jones KL, Williams RD. Balancing Nitrogen Fertilization for Crop Yield and Environmental Protection. Journal of Soil and Water Conservation. 2019.
15. Lee CM, Wang SC, Chen LY. Precision Agriculture Approaches for Nitrogen Management and Environmental Sustainability. Field Crops Research. 2021.
16. Garcia MH, Rodriguez PA, Lopez JR. Nitrogen Fertilizer Recommendation and its Impact on Water Quality: A Review of Recent Studies. Water Research. 2017.
17. Payal G, Suman KJ. Efficient Crop Yield Prediction in India using Machine Learning Techniques. International Journal of Engineering Research & Technology (Ijert) Encadems. 2020; 8(10).
18. Sujatha R, Isakki P. A study on crop yield forecasting using classification techniques. 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16); January 2016. doi: 10.1109/icctide.2016.7725357
19. Ransom CJ, Kitchen NR, Camberato JJ, et al. Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations. Computers and Electronics in Agriculture. 2019; 164: 104872. doi: 10.1016/j.compag.2019.104872
20. Ennaji O, Vergütz L, El Allali A. Machine learning in nutrient management: A review. Artificial Intelligence in Agriculture. 2023; 9: 1-11. doi: 10.1016/j.aiia.2023.06.001
21. Sathiya, Priya, R., Rahamathunnisa, U. A Novel Clustering Algorithm for Monitoring Paddy Growth Through Satellite Image Processing.. ACM Transactions on Sensor Networks, (2023). doi: 10.1145/3579358
22. Islam MA, Shuvo MNR, Shamsojjaman M, Hasan S. An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection. International Journal of Advanced Computer Science and Applications. 2021; 12. doi: 10.14569/IJACSA.2021.0120134
23. Devi OR, Lakshmi PN, Babu SN, Bai KVS, Sowmya, Akansha. Fertilizer Forecasting using Machine Learning. 2023 International Conference on Inventive Computation Technologies (ICICT). Lalitpur, Nepal, 2023: 24-27. doi: 10.1109/ICICT57646.2023.10134061.
24. Zhang Y, Wang T, Zheng J, et al. Based on machine learning algorithms for estimating leaf phosphorus concentration of rice using optimized spectral indices and continuous wavelet transform. Frontiers in Plant Science, (2023). doi: 10.3389/fpls.2023.1185915
25. Gabriele B. Machine learning in nutrient management: A review. Artificial intelligence in agriculture 2023. doi: 10.1016/j.aiia.2023.06.001
26. Jiao J, Zeng W, Ren Z, et al. A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms. Agronomy. 2023; 13(5): 1400. doi: 10.3390/agronomy13051400
27. Dhakshayani J, Surendiran B. M2F-Net: A Deep Learning-Based Multimodal Classification with High-Throughput Phenotyping for Identification of Overabundance of Fertilizers. Agriculture 2023; 13(6): 1238. doi: 10.3390/agriculture13061238
DOI: https://doi.org/10.32629/jai.v7i5.1570
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 N. Lakshmi Kalyani, Bhanu Prakash Kolla
License URL: https://creativecommons.org/licenses/by-nc/4.0/