Agriculture in Coimbatore forms a significant part of Tamil Nadu's agrarian heritage. It serves as a chief architect of agricultural fortunes for this state and sustains a primary livelihood for a large portion of its people. Being the third-largest district of Tamil Nadu, Coimbatore has further augmented itself in importance through its agricultural prowess, substantially beefing up the economic framework existing within the state. The study aims to devise predictive models to aid the farmer in choosing the best crops in certain subdivisions of Coimbatore. Thereby, with the help of greater data analysis, machine learning techniques, and improved visualization techniques, we try to augment sustainable agricultural development in the area. It becomes clear that while maximizing harvest efficiency, one must ensure that crops are laid out under an environment close to optimum, thus an emphasis on predictive analytics and data-driven decisions. The dataset has been further designed to enhance visualization with key agricultural parameters like soil micronutrient levels and historic crop data, complemented by the models, which take future weather predictions into account for accurate agricultural recommendations that ultimately improve crop yield and resiliency in Coimbatore's agriculture.
H. Akcay, S. Kaya, E. Sertel, and U. Alganci, “Determination of Olive Trees with Multi-sensor Data Fusion,” 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Jul. 2019, doi: 10.1109/agro-geoinformatics.2019.8820712.
Bera S. “Delineation of groundwater prospect zone in hard rock terrine part of purulia district of West Bengal India using remote sensing & GIS approach”. In: IJSRCSEIT 3 (1 2018), pp. 1361–1370.
J. Xu et al., “Trend analysis of the application of multispectral technology in plant yield prediction: a bibliometric visualization analysis (2003–2024),” Frontiers in Sustainable Food Systems, vol. 9, Feb. 2025, doi: 10.3389/fsufs.2025.1513690.
T. Groher, K. Heitkämper, A. Walter, F. Liebisch, and C. Umstätter, “Status quo of adoption of precision agriculture enabling technologies in Swiss plant production,” Precision Agriculture, vol. 21, no. 6, pp. 1327–1350, May 2020, doi: 10.1007/s11119-020-09723-5.
P. Kamath, P. Patil, S. S, Sushma, and S. S, “Crop yield forecasting using data mining,” Global Transitions Proceedings, vol. 2, no. 2, pp. 402–407, Nov. 2021, doi: 10.1016/j.gltp.2021.08.008.
R. Chen, Z. Liu, J. Yang, T. Ma, A. Guo, and R. Shi, “Predicting cadmium enrichment in crops/vegetables and identifying the effects of soil factors based on transfer learning methods,” Ecotoxicology and Environmental Safety, vol. 291, p. 117823, Feb. 2025, doi: 10.1016/j.ecoenv.2025.117823.
K. Umani, C. Zhang, R. J. McGee, G. J. Vandemark, and S. Sankaran, “A pulse crop dataset of agronomic traits and multispectral images from multiple environments,” Data in Brief, vol. 53, p. 110013, Apr. 2024, doi: 10.1016/j.dib.2023.110013.
M. R. Barbosa Júnior, L. de A. Sales, R. G. dos Santos, R. B. S. Vargas, C. Tyson, and L. P. de Oliveira, “Forecasting yield and market classes of Vidalia sweet onions: A UAV-based multispectral and texture data-driven approach,” Smart Agricultural Technology, vol. 10, p. 100808, Mar. 2025, doi: 10.1016/j.atech.2025.100808.
X. Hou et al., “Peanut yield prediction using remote sensing and machine learning approaches based on phenological characteristics,” Computers and Electronics in Agriculture, vol. 232, p. 110084, May 2025, doi: 10.1016/j.compag.2025.110084.
E. Elbasi et al., “Crop Prediction Model Using Machine Learning Algorithms,” Applied Sciences, vol. 13, no. 16, p. 9288, Aug. 2023, doi: 10.3390/app13169288.
M. Garanayak, G. Sahu, S. N. Mohanty, and A. K. Jagadev, “Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods,” International Journal of Agricultural and Environmental Information Systems, vol. 12, no. 1, pp. 1–20, Jan. 2021, doi: 10.4018/ijaeis.20210101.oa1.
S. M. Shawon, F. Barua Ema, A. K. Mahi, and Md. Mohsin Sarker Raihan, “Crop Yield Prediction: Robust Machine Learning Approaches for Precision Agriculture,” 2023 26th International Conference on Computer and Information Technology (ICCIT), pp. 1–6, Dec. 2023, doi: 10.1109/iccit60459.2023.10441634.
M. G. P. S. and B. R., “Performance Evaluation of Best Feature Subsets for Crop Yield Prediction Using Machine Learning Algorithms,” Applied Artificial Intelligence, vol. 33, no. 7, pp. 621–642, Apr. 2019, doi: 10.1080/08839514.2019.1592343.
S. Gupta ., “Machine Learning- and Feature Selection-Enabled Framework for Accurate Crop Yield Prediction,” Journal of Food Quality, vol. 2022, pp. 1–7, May 2022, doi: 10.1155/2022/6293985.
T. Sivaranjani and S. P. Vimal, “AI Method for Improving Crop Yield Prediction Accuracy Using ANN,” Computer Systems Science and Engineering, vol. 47, no. 1, pp. 153–170, 2023, doi: 10.32604/csse.2023.036724.
Priya S and Kausalya N. “Forecasting Sugarcane Productivity In Coimbatore Using Weather Variables: A Comparative Study Between Xg Boost And Recurrent Neural Network Approach”. In: ().
D. Vaithiyanathan and K. Sudalaimuthu, “Spatio-Temporal variability of soil primary macro and micro nutrients – An empirical analysis on Anaimalai block, Coimbatore District, India,” Journal of the Saudi Society of Agricultural Sciences, vol. 23, no. 3, pp. 245–259, Apr. 2024, doi: 10.1016/j.jssas.2023.12.002.
K. Gurubaran et al., “Machine Learning Approach for Soil Nutrient Prediction,” 2023 IEEE Silchar Subsection Conference (SILCON), pp. 1–6, Nov. 2023, doi: 10.1109/silcon59133.2023.10405095.
S. Dasgupta et al., “Developing regional soil micronutrient management strategies through ensemble learning based digital soil mapping,” Geoderma, vol. 433, p. 116457, May 2023, doi: 10.1016/j.geoderma.2023.116457.
L. K. Subramaniam and R. Marimuthu, “Crop yield prediction using effective deep learning and dimensionality reduction approaches for Indian regional crops,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 8, p. 100611, Jun. 2024, doi: 10.1016/j.prime.2024.100611.
S. K. Sharma, D. P. Sharma, and J. K. Verma, “Study on Machine-Learning Algorithms in Crop Yield Predictions specific to Indian Agricultural Contexts,” 2021 International Conference on Computational Performance Evaluation (ComPE), pp. 155–166, Dec. 2021, doi: 10.1109/compe53109.2021.9752260.
CRediT Author Statement
The author reviewed the results and approved the final version of the manuscript.
Conceptualization: Semmalar V I and Roseline R A;
Methodology: Roseline R A;
Data Curation: Semmalar V I;
Writing- Original Draft Preparation: Semmalar V I and Roseline R A;
Visualization: Semmalar V I and Roseline R A;
Investigation: Roseline R A;
Supervision: Roseline R A;
Validation: Semmalar V I and Roseline R A;
Writing- Reviewing and Editing: Semmalar V I and Roseline R A; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Roseline R A for this research completion and support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
The Cropcbe is used in this research, collected from Soil test Laboratory Coimbatore. The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Semmalar V I
Department of Computer Applications, Government Arts College, Coimbatore, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Semmalar V I and Roseline R A, “Advanced Data Visualization and Machine Learning Analytics on Soil Test Parameters for Agricultural Insight”, Journal of Machine and Computing, vol.5, no.4, pp. 2788-2800, October 2025, doi: 10.53759/7669/jmc202505212.
