Machine learning-based in-season nitrogen status prediction using unmanned aerial vehicle remote sensing

Document Type : Research Paper

Authors

Department of CS & IT, Kalinga University, Raipur, India.

Abstract

Objective
The uniformity of fertilizer application across fields is a common practice, driven by local legislation or by expert opinion. However, this approach might lead to over-application of nitrogen in areas with poor yields. Human health, ecological functions, biodiversity, climate change, and long-term stability are all adversely affected by the increasing release of reactive nitrogen into the environment that may result from the excessive use of fertilizers. The purpose of this work was to show that throughout the growth season, location-specific N proposals may be generated using non-invasive crop status monitoring that is built on Remote Sensing Technologies (RST). This tracking system can accurately assess the position of crop N.
Materials and methods
In this study, two frameworks—Support Vector Machine (SVM) and Artificial Neural Networks (ANN)—that rely solely on data collected from crop sensors, with the goal of improving our ability to predict crop N Nutrition Index (NNI) and crop yield throughout the growing season were compared. This was performed by combining data from soil, weather, and cultivation with information from present detectors using Random Forest (RF).
Results
Through RST, a simple and low-cost tool known as a fixed-wing Unmanned Aerial Vehicle (UAV) may capture wavelength reflection images. This collection of images is invaluable to polystyrene nanoparticles (PNSP). Applying ML techniques enhanced the NNI estimate, as seen by the results.
Conclusions
Utilizing machine learning techniques presents a valuable opportunity to maximize the use of RST data, enabling more effective monitoring of agricultural production factors and directing PNSP strategies

Keywords

References

Angin P, Anisi MH, Göksel F, et al. (2020) Agrilora: a digital twin framework for smart agriculture. J Wirel Mob Netw Ubiquitous Comput Dependable Appl 11(4), 77-96.
Aula L, Omara P, Nambi E, et al. (2020) Review of active optical sensors for improving winter wheat nitrogen use efficiency. Agronomy 10(8), e1157.
Barbedo JGA (2019) A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones 3(2), e40.
Brinkhoff J, Dunn BW, Robson AJ (2021) Rice nitrogen status detection using commercial-scale imagery. Int J Appl Earth Obs Geoinf 105, e102627.
Camgözlü Y, Kutlu Y (2023) Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Natural and Engineering Sciences 8(3), 214-232.
Chen Z, Miao Y, Lu J, et al. (2019) In-season diagnosis of winter wheat nitrogen status in smallholder farmer fields across a village using unmanned aerial vehicle-based remote sensing. Agron 9(10), 619. https://doi.org/10.3390/agronomy9100619
Chlingaryan A, Sukkarieh S, Whelan B (2018) Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput Electron Agric 151, 61-69.
Dong R, Miao Y, Wang X, Kusnierek K (2024) An active canopy sensor-based in-season nitrogen recommendation strategy for maize to balance grain yield and lodging risk. Eur J Agron 155, e127120. https://doi.org/10.1016/j.eja.2024.127120
Ghotbaldini H, Mohammadabadi MR, Nezamabadi-pour H, et al. (2019) Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed. Acta Scientiarum Anim Sci 41, e45282. https://doi.org/10.4025/actascianimsci.v41i1.45282
Havlin JL (2020) Soil: Fertility and nutrient management. In Landscape and land capacity, CRC Press 251-265.
Huang S, Yuxin MIAO, Qiang CAO, et al. (2018) A new critical nitrogen dilution curve for rice nitrogen status diagnosis in Northeast China. Pedosphere 28(5), 814-822.
Kapoor M, Katsanos E, Nalpantidis L, et al. (2021) Structural health monitoring and management with unmanned aerial vehicles: review and potentials. (BYG; No. R-454). Technical University of Denmark, DTU. https://www.byg.dtu.dk/forskning/publikationer/byg_rapporter
Liakos KG, Busato P, Moshou D, et al. (2018) Machine learning in agriculture: A review. Sens 18(8), 2674. https://doi.org/10.3390/s18082674
Mohammadabadi M, Kheyrodin H, Afanasenko V, et al. (2024) The role of artificial intelligence in genomics. Agric Biotechnol J 16 (2), 195-279.
Mumtaj BH (2022) Scientometric Analysis of the Research Paper Output on Artificial Intelligence: A Study. Indian Journal of Information Sources and Services 12(1), 52–58.
Padilla FM, Farneselli M, Gianquinto G, et al. (2020) Monitoring nitrogen status of vegetable crops and soils for optimal nitrogen management. Agric Water Manag 241, e106356. https://doi.org/10.1016/j.agwat.2020.106356
Pour Hamidi S, Mohammadabadi MR, Asadi Foozi M, Nezamabadi-pour H (2017) Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. J Livestock Sci Technol 5 (2), 53-61.
Radhika A, Masood MS (2022) Crop Yield Prediction by Integrating Et-DP Dimensionality Reduction and ABP-XGBOOST Technique. J Internet Serv Inf Secur 12(4), 177-196.
Raschka S, Liu YH, Mirjalili V (2022) Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. Packt Publishing Ltd.
Surendar A, Saravanakumar V, Sindhu S, Arvinth N (2024) A Bibliometric Study of Publication- Citations in a Range of Journal Articles. Indian Journal of Information Sources and Services 14(2), 97–103. https://doi.org/10.51983/ijiss-2024.14.2.14
Veerasamy K, Fredrik ET (2023) Intelligent Farming based on Uncertainty Expert System with Butterfly Optimization Algorithm for Crop Recommendation. J Internet Serv Inf Secur 13(3), 158-169.
Wang T, Chandra A, Jung J, Chang A (2022) UAV remote sensing based estimation of green cover during turfgrass establishment. Comput Electron Agric 194, 106721. https://doi.org/10.1016/j.compag.2022.106721
Zoran G, Nemanja A, Srđan B (2022) Comparative Analysis of Old-Growth Stands Janj and Lom Using Vegetation Indices. Arch Tech Sci 2(27), 57-62.
Agricultural Biotechnology Journal
Volume 16, Issue 3
September 2024
Pages 243-256

Files

Share

How to cite

Statistics