AIoTST-CR : AIoT Based Soil Testing and Crop Recommendation to Improve Yield

Shradha Joshi-Bag, Archana Vyas

Abstract


Agriculture is a backbone of any country. Farmers need to test the soil fertility and nutrients present in the soil for proper growth of the crops. In traditional system, the farmers collect soil sample and submit to soil testing labs for testing the soil nutrients and get the soil test reports manually. Farmers based on his experience and the season; decide which crop to be taken in the farm. Based on soil testing reports farmers decide which fertilizers required for the proper growth of the crop. This process is time consuming and human efforts are required and hence crop yield is affected. The recent technologies in cloud storage, wireless sensors,  and AI based algorithms are very instrumental in decision making process of crop growth life cycle. Farmers can make use of mechanical automation tools for seeding, watering, supplying fertilizers, crop cutting etc. for proper growth of the crop. However, to observe the crop growth during the entire life cycle of crop farmer has to take lot of efforts to check need of water, any problem of disease to the crop, any specific fertilizers required or not and whether there is a need of harvesting. A proper decision support system is needed for helping the farmers in all such activities. Such support can be provided to a farmer so that he will be well updated about the growth of his crop in the farm. To reduce the human efforts and improve the crop yield, Artificial Intelligence and IOT based soil testing and Crop Recommendation system (AIoTST-CR) is designed and developed. AIoT based handheld soil testing system has pH, Nitrogen, Phosphorous, Potassium and Soil moisture sensing capability. A mobile application is developed to fetch the sensed data from AIoT system. A historical data is inputted to give training to ML models. Machine learning algorithm is used to predict and recommend the crop to be taken. The results show AIoTST-CR which is AIoT based soil testing and crop recommendation system provides effortless and accurate recommendations of crop. Our findings indicate that AIoT based system provides high accuracy, which outperforms existing commonly, used machine learning based crop recommendation systems.

Keywords


AIoTST-CR; AIoT; Crop Recommendation; Decision Tree; Random Forest; SVM; Naïve Bays; Crop Yield; Soil Testing

References


Fariha Shahrin, Labiba Zahin, Ramisa Rahman, Jahir Hossain, Abdulla Hil Kafi and A.K.M Abdul Malek Azad, " Agricultural Analysis and Crop Yield Prediction of Habiganj using Multispectral Bands of Satellite Imagery with Machine Learning," IEEE 2020 Proceedings of 11th International Conference on Electrical and Computer Engineering (ICECE) DOI: 10.1109/ICECE51571.2020.9393066

Abhiram MSD, Jyothsnavi Kuppili and N.Alivelu Manga “Smart Farming System using IoT for Efficient Crop

Growth” proceedings of IEEE 2020 International Conference on Electrical, Electronics and Computer Science

Sashant Suhag, Sanskriti jadaun, Ayush Shukla, Nidhi Singh, Prashant Johri and Nidhi Parashar “IoT based Soil

Nutrition and Plant Disease Detection System for Smart Agriculture” 10th IEEE International Conference on

Communication Systems and Network Technologies DOI: 10.1109/CSNT51715.2021.9509719

Siddalinga Nuchhi, Vinaykumar Bagali and Shilpa Annigeri “IOT Based Soil Testing Instrument For Agriculture

Purpose” IEEE explore journal.

Farnaz Babaie Sarijaloo, Michele Porta, Bijan Taslimi and Panos Pardalos “Yield performance estimation of corn

hybrids using machine learning algorithms” Artificial Intelligence in Agriculture 5 (2021) 82–89

A. Subeesh , C.R. Mehta “Automation and digitization of agriculture using artificial intelligence and internet of

things” Artificial Intelligence in Agriculture 5 (2021) 278–291 indexed in ScienceDirect

Liu, D., Mishra, A.K., Yu, Z., 2016b. “Evaluating uncertainties in multi-layer soil moisture estimation with support

vector machines and ensemble Kalman filtering.“ J. Hydrol.538,243–255

https://doi.org/10.1016/j.jhydrol.2016.04.02

Khaki, S., Wang, L., Archontoulis, S.V., 2020. A CNN-RNN framework for crop yield prediction. Front. Plant Sci.

https://doi.org/10.3389/fpls.2019.01750.

Lavanya, G., Rani, C., Ganeshkumar, P., 2019. An automated low cost IoT based Fertilizer Intimation System for

smart agriculture. Sustain. Comput. Inform. Syst. https://doi. org/10.1016/j.suscom.2019.01.002.

Bhuwan Kashyap and Ratnesh Kumar. Sensing Methodologies in Agriculture for Soil Moisture and Nutrient

Monitoring. IEEE Paper DOI 10.1109/ACCESS.2021.3052478. VOLUME 9, 2021

Soil Test parameters and crop recommendation data download from

https://www.kaggle.co/datasets/atharvaingle/crop-recommendation-dataset

A User-friendly AIoT-Based Crop Recommendation system (UACR): concept and architecture. Available from:

https://www.researchgate.net/publication/369938116_A_User-friendly_AIoT- Based_Crop_Recommendation_system_UACR_concept_and_architecture

Akhter, R.; Sofi, S.A. Precision agriculture using IoT data analytics and machine learning. J. King Saud Univ.-

Comput. Inf. Sci. 2021, 34, 5602–5618.

ivakumar, R.; Prabadevi, B.; Velvizhi, G.; Muthuraja, S.; Kathiravan, S.; Biswajita, M.; Madhumathi, A. Internet

of Things and Machine Learning Applications for Smart Precision Agriculture. In IoT Applications Computing;

IntechOpen: London, UK, 2022; p. 135.

Dagar, R.; Som, S.; Khatri, S.K. Smart farming–IoT in agriculture. In Proceedings of the 2018 International

Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 11–12 July 2018.

Priya, R.; Ramesh, D.; Khosla, E. Crop Prediction on the Region Belts of India: A Naïve Bayes MapReduce

Precision Agricultural Model. In Proceedings of the 2018 International Conference on Advances in Computing,

Communications and Informatics (ICACCI), Bangalore, India, 19–22 September 2018; pp. 99–104.

Hu, C.; Zhong, X.; Xu, J. Study on integrated discovery system of sensors for agriculture observation application.

In Proceedings of the 2014 The Third International Conference on Agro-Geoinformatics, Beijing, China, 11–14

August 2014; pp. 1–5.

Pajares, G.; Peruzzi, A.; Gonzalez-De-Santos, P. Sensors in Agriculture and Forestry. Sensors 2013, 13, 12132–

Bhat, S.A.; Huang, N.-F. Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. IEEE

Access 2021, 9, 110209–110222.

Ali, S.M.; Das, B.; Kumar, D. Machine Learning based Crop Recommendation System for Local Farmers of Pakistan. Rev. Geintec-Gest. Inov. Tecnol. 2021, 11, 5735–5746.

Gosai, D.; Raval, C.; Nayak, R.; Jayswal, H.; Patel, A. Crop Recommendation System using Machine Learning. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2021, 7, 554–557.

Mulge, M.; Sharnappa, M.; Sultanpure, A.; Sajjan, D.; Kamani, M. Agricultural crop recommendation system using IoT and ML. Int. J. Anal. Exp. Modal Anal. 2020, 12, 1112–1117.

Viviliya, B.; Vaidhehi, V. The Design of Hybrid Crop Recommendation System using Machine Learning Algorithms. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2019, 9, 4305–4311.

Hasyiya K. A., Muhammad A. R., Khairul N. S., Wan Salihin Wong Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review Special Issue Accelerating Beyond Traditional Farming through Exceptional Approaches of AI and IoT Sensors 2023, 23(7), 3752; https://doi.org/10.3390/s23073752

Javaid, M.; Haleem, A.; Singh, R.P.; Suman, R. Enhancing smart farming through the applications of Agriculture 4.0 technolgies. Int. J. Intell. Netw. 2022, 3, 150–164.

Pant, J.; Pant, R.P.; Singh, M.K.; Singh, D.P.; Pant, H. Analysis of agricultural crop yield prediction using statistical techniques of machine learning. Mater. Today Proc. 2021, 46, 10922–10926.

O’Shaughnessy, S.A.; Kim, M.; Lee, S.G.; Kim, Y.; Kim, H.; Shekailo, J. Towards smart farming solutions in the U.S. and South Korea: A comparison of the current status. Geogr. Sustain. 2021, 2, 312–327.

Khan, J.I.; Khan, J.; Ali, F.; Ullah, F.; Bacha, J.; Lee, S. Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review. IEEE Access 2022, 10, 62613–62660.

Zhang, H.; He, L.; Di Gioia, F.; Choi, D.; Elia, A.; Heinemann, P. LoRa WAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato. Smart Agric. Technol. 2022, 2, 100053.

S. Katiyar and A. Farhana, “Smart agriculture: The future of agriculture using ai and iot,” Journal of Computational Science, vol. 17, no. 10, pp.984–999, 2021.

M. Pathan, N. Patel, H. Yagnik, and M. Shah, “Artificial cognition for applications in smart agriculture: A comprehensive review,” Artificial Intelligence in Agriculture, vol. 4, pp. 81–95, 2020.

S. K. S. Durai and M. D. Shamili, “Smart farming using machine learning and deep learning techniques,” Decision Analytics Journal,vol. 3, p. 100041, 2022.

Y. Liu, X. Ma, L. Shu, G. P. Hancke, and A. M. Abu-Mahfouz, “From industry 4.0 to agriculture 4.0: Current status, enabling technologies, and research challenges,” IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4322–4334, 2020.

O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. Wang, “Internet of things for the future of smart agriculture: a comprehensive survey of emerging technologies,” IEEE/CAA Journal of Automatica Sinica, vol. 8,no. 4, pp. 718–752, 2021.

A. Mitra, S. L. Vangipuram, A. K. Bapatla, V. K. Bathalapalli, S. P.Mohanty, E. Kougianos, and C. Ray, “Everything you wanted to know about smart agriculture,” arXiv preprint arXiv:2201.04754, 2022

Suchithra, M.; Pai, M.L. Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf. Process. Agric. 2020, 7, 72–82.

Dash, R.; Dash, D.K.; Biswal, G. Classification of crop based on macronutrients and weather data using machine learning techniques. Results Eng. 2021, 9, 100203.

Balakrishnan, N.; Muthukumarasamy, G. Crop production-ensemble machine learning model for prediction. Int. J. Comput. Sci. Softw. Eng. 2016, 5, 148.

Pantazi, X.; Moshou, D.; Alexandridis, T.; Whetton, R.; Mouazen, A. Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electron. Agric. 2016, 121, 57–65.


Full Text: PDF

Refbacks

  • There are currently no refbacks.


 

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

Creative Commons Licence

This work is licensed under a Creative Commons Attribution 4.0 International License.

web analytics
View IJEEI Stats

503 Service Unavailable

Service Unavailable

The server is temporarily unable to service your request due to maintenance downtime or capacity problems. Please try again later.

Additionally, a 503 Service Unavailable error was encountered while trying to use an ErrorDocument to handle the request.