Crop Prediction Model Using Machine Learning Algorithms
Source abstract
Abstract
Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Currency Recognition and Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
References (61)
Show source-provided references
1. Developing machine learning models with multi-source environmental data to predict wheat yield in China10.1016/j.compag.2022.106790 ↗
2. Crop yield prediction using machine learning: A systematic literature review10.1016/j.compag.2020.105709 ↗
3. Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., and Uwamahoro, A. (2023). Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize. Agriculture, 13.10.3390/agriculture13010225 ↗
4. Xu, W., Kaili, Z., and Tianlei, W. (2021, January 7–10). Smart Farm Based on Six-Domain Model. Proceedings of the IEEE 4th International Conference on Electronics Technology (ICET), Chengdu, China.10.1109/ICET51757.2021.9451003 ↗
5. Moysiadis, V., Tsakos, K., Sarigiannidis, P., Petrakis, E.G.M., Boursianis, A.D., and Goudos, S.K. (2022, January 8–10). A Cloud Computing web-based application for Smart Farming based on microservices architecture. Proceedings of the 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), Bremen, Germany.10.1109/MOCAST54814.2022.9837727 ↗
6. Ranjan, P., Garg, R., and Rai, J.K. (2022, January 21–23). Artificial Intelligence Applications in Soil & Crop Management. Proceedings of the IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India.10.1109/IATMSI56455.2022.10119362 ↗
7. Oré, G., Alcântara, M.S., Góes, J.A., Oliveira, L.P., Yepes, J., Teruel, B., and Castro, V. (2020). Crop Growth Monitoring with Drone-Borne DInSAR. Remote Sens., 12.10.3390/rs12040615 ↗
8. Gehlot, A., Sidana, N., Jawale, D., Jain, N., Singh, B.P., and Singh, B. (2022, January 24–25). Technical analysis of crop production prediction using Machine Learning and Deep Learning Algorithms. Proceedings of the International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India.10.1109/ICSES55317.2022.9914206 ↗
9. Vashisht, S., Kumar, P., and Trivedi, M.C. (2022, January 27–29). Improvised Extreme Learning Machine for Crop Yield Prediction. Proceedings of the 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, UK.10.1109/ICIEM54221.2022.9853054 ↗
10. OpenAI (2023, April 01). New and Improved Content Moderation Tooling. OpenAI. Available online: https://openai.com/blog/new-and-improved-content-moderation-tooling/.
11. Google (2023, April 02). Bard Chatbox. Google. Available online: https://bard.google.com.
12. Dean, J. (2020, January 16–20). The deep learning revolution and its implications for computer architecture and chip design. Proceedings of the IEEE International Solid-State Circuits Conference-(ISSCC), San Francisco, CA, USA.10.1109/ISSCC19947.2020.9063049 ↗
13. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting10.1109/TITS.2019.2950416 ↗
14. Shahrin, F., Zahin, L., Rahman, R., Hossain, A.J., Kaf, A.H., and Abdul Malek Azad, A.K.M. (2020, January 17–19). Agricultural Analysis and Crop Yield Prediction of Habiganj using Multispectral Bands of Satellite Imagery with Machine Learning. Proceedings of the 11th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh.10.1109/ICECE51571.2020.9393066 ↗
15. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming10.1016/j.compag.2022.107119 ↗
16. Smart farming using Machine Learning and Deep Learning techniques10.1016/j.dajour.2022.100041 ↗
17. An Internet of Things-based Efficient Solution for Smart Farming10.1016/j.procs.2023.01.252 ↗
18. Technological revolutions in smart farming: Current trends, challenges & future directions10.1016/j.compag.2022.107217 ↗
19. Machine learning based crop growth management in greenhouse environment using hydroponics farming techniques10.1016/j.measen.2023.100665 ↗
20. A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches with Special Emphasis on Palm Oil Yield Prediction10.1109/ACCESS.2021.3075159 ↗
21. Babber, J., Malik, P., Mittal, V., and Purohit, K.C. (2022, January 29–31). Analyzing Supervised Learning Algorithms for Crop Prediction and Soil Quality. Proceedings of the 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.10.1109/ICCMC53470.2022.9753798 ↗
22. Ishak, M., Rahaman, M.S., and Mahmud, T. (2021, January 17–20). FarmEasy: An Intelligent Platform to Empower Crops Prediction and Crops Marketing. Proceedings of the 13th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia.10.1109/ICTS52701.2021.9608436 ↗
23. Patel, K., and Patel, H.B. (2022, January 15–17). A Comparative Analysis of Supervised Machine Learning Algorithm for Agriculture Crop Prediction. Proceedings of the Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India.10.1109/ICECCT52121.2021.9616731 ↗
24. Memon, R., Memon, M., Malioto, N., and Raza, M.O. (2021, January 26–27). Identification of growth stages of crops using mobile phone images and machine learning. Proceedings of the International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, Pakistan.10.1109/ICECube53880.2021.9628197 ↗
25. Chandraprabha, M., and Dhanaraj, R.K. (2021, January 17–18). Soil Based Prediction for Crop Yield using Predictive Analytics. Proceedings of the 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India.10.1109/ICAC3N53548.2021.9725758 ↗
26. Ray, R.K., Das, S.K., and Chakravarty, S. (2022, January 27–28). Smart Crop Recommender System-A Machine Learning Approach. Proceedings of the 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.10.1109/Confluence52989.2022.9734173 ↗
27. Priyadharshini, K., Prabavathi, R., Devi, V.B., Subha, P., Saranya, S.M., and Kiruthika, K. (2022, January 10–11). An Enhanced Approach for Crop Yield Prediction System Using Linear Support Vector Machine Model. Proceedings of the International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India.10.1109/IC3IOT53935.2022.9767994 ↗
28. Malathy, S., Vanitha, C.N., Kotteswari, S., and Mohankkanth, E. (2022, January 9–11). Rainfall Prediction for Enhancing Crop-Yield based on Machine Learning Techniques. Proceedings of the International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India.10.1109/ICAAIC53929.2022.9792793 ↗
29. Chowdary, V.T., Robinson Joel, M., Ebenezer, V., Edwin, B., Thanka, R., and Jeyaraj, A. (2022, January 16–18). A Novel Approach for Effective Crop Production Using Machine Learning. Proceedings of the International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India.10.1109/ICEARS53579.2022.9751798 ↗
30. Yamparla, R., Shaik, H.S., Guntaka, N., Marri, P., and Nallamothu, S. (2022, January 22–24). Crop Yield Prediction using Random Forest Algorithm. Proceedings of the 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.10.1109/ICCES54183.2022.9835756 ↗
31. Apeksha, R.G., and Swati, S.S. (2021, January 18–19). A brief study on the prediction of crop disease using machine learning approaches. Proceedings of the 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), Nagpur, India.
32. Kumar, R., Shukla, N. (2022, January 10–11). Plant Disease Detection and Crop Recommendation Using CNN and Machine Learning. Proceedings of the International Mobile and Embedded Technology Conference (MECON), Noida, India.10.1109/MECON53876.2022.9752173 ↗
33. Bhosale, S.V., Thombare, R.A., Dhemey, P.G., and Chaudhari, A.N. (2018, January 16–18). Crop Yield Prediction Using Data Analytics and Hybrid Approach. Proceedings of the Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India.10.1109/ICCUBEA.2018.8697806 ↗
34. A survey on smart farming data, applications and techniques10.1016/j.compind.2022.103624 ↗
35. Lyu, Y., Li, J., Hou, R., Zhang, Y., Hang, S., Zhu, W., Zhu, H., and Ouyang, Z. (2022). Precision Feeding in Ecological Pig-Raising Systems with Maize Silage. Animals, 12.10.3390/ani12111446 ↗
36. Ghobadi, F., and Kang, D. (2023). Application of Machine Learning in Water Resources Management: A Systematic Literature Review. Water, 15.10.3390/w15040620 ↗
37. Machine learning and soil sciences: A review aided by machine learning tools10.5194/soil-6-35-2020 ↗
38. Automatic fruit count on coffee branches using computer vision10.1016/j.compag.2017.03.010 ↗
39. Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions10.1016/j.biosystemseng.2013.07.007 ↗
40. Support vector machine-based open crop model (SBOCM): Case of rice production in China10.1016/j.sjbs.2017.01.024 ↗
41. Li, S.Z., and Jain, A. (2009). Encyclopedia of Biometrics, Springer.10.1007/978-0-387-73003-5 ↗
42. Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data—A Machine Learning Approach10.1109/JSTARS.2016.2561618 ↗
43. Jadhav, M., Kolambe, N., Jain, S., and Chaudhari, S. (2021, January 21–23). Farming Made Easy using Machine Learning. Proceedings of the 2nd International Conference for Emerging Technology (INCET), Belagavi, India.10.1109/INCET51464.2021.9456351 ↗
44. (2023, August 01). How Is ML Is Used in Agriculture?. Available online: https://www.dtn.com/how-is-machine-learning-used-in-agriculture/.
45. Sawhney, D. (2023, August 01). Redefining Agriculture through Artificial Intelligence: Predicting the Unpredictable. p9. GG—May 2022—M&C 19416. Available online: https://www.pwc.in/assets/pdfs/grid/agriculture/redefining-agriculture-through-artificial-intelligence.pdf.
46. Pyingkodi, M., Thenmozhi, K., Karthikeyan, M., Kalpana, T., Palarimath, S., and Kumar, G.B.A. (2022, January 17–19). IoT-based Soil Nutrients Analysis and Monitoring System for Smart Agriculture. Proceedings of the 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India.10.1109/ICESC54411.2022.9885371 ↗
47. Scientific development of smart farming technologies and their application in Brazil
48. Patel, N.S., and Kumar, H.P.M. (2022, January 26–27). Soil Quality Identifying and Monitoring Approach for Sugarcane Using Machine Learning Techniques. Proceedings of the Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India.
49. Puengsungwan, S. (2020, January 14–16). IoT-based Soil Moisture Sensor for Smart Farming. Proceedings of the International Conference on Power, Energy, and Innovations (ICPEI), Chiangmai, Thailand.10.1109/ICPEI49860.2020.9431455 ↗
50. A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop10.1109/ACCESS.2022.3199926 ↗
51. Xiao, F., Wang, H., Xu, Y., and Zhang, R. (2023). Fruit Detection and Recognition Based on Deep Learning for Automatic Harvesting: An Overview and Review. Agronomy, 13.10.3390/agronomy13061625 ↗
52. Cravero, A., Pardo, S., Sepúlveda, S., and Muñoz, L. (2022). Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review. Agronomy, 12.10.20944/preprints202202.0345.v1 ↗
53. Machine learning with Big Data: Challenges and approaches10.1109/ACCESS.2017.2696365 ↗
54. On the use of Map Reduce for imbalanced Big Data using Random Forest10.1016/j.ins.2014.03.043 ↗
55. Salma, C.A., Tekinerdogan, B., and Athanasiadis, I.N. (2017). Software Architecture for Big Data and the Cloud, Morgan Kaufmann.
56. Taheri, M., Schreiner, H.K., Mohammadian, A., Shirkhani, H., Payeur, P., Imanian, H., and Cobo, J.H. (2023). A Review of Machine Learning Approaches to Soil Temperature Estimation. Sustainability, 15.10.3390/su15097677 ↗
57. A review: Data pre-processing and data augmentation techniques10.1016/j.gltp.2022.04.020 ↗
58. (2023, March 30). Available online: https://www.kaggle.com/code/theeyeschico/crop-analysis-and-prediction.
59. Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review10.1109/ACCESS.2022.3232485 ↗
60. Elbasi, E., Zreikat, A.I., Mathew, S., and Topcu, A.E. (2021, January 26–28). Classification of influenza H1N1 and COVID-19 patient data using machine learning. Proceedings of the 44th International Conference on Telecommunications and Signal Processing (TSP), Brno, Czech Republic.10.1109/TSP52935.2021.9522591 ↗
61. Detecting Multicollinearity in Regression Analysis10.12691/ajams-8-2-1 ↗