Bhartiya Krishi Anusandhan Patrika, volume 37 issue 4 (december 2022) : 363-368

IoT-based Recommender Engine for Yielding Better Crops

Aaditya Aggarwal, Durgansh Sharma
1School of Business and Management, Christ University, Delhi NCR, Ghaziabad-201 003, Uttar Pradesh, India.
  • Submitted21-05-2022|

  • Accepted15-09-2022|

  • First Online 11-10-2022|

  • doi 10.18805/BKAP540

Cite article:- Aggarwal Aaditya, Sharma Durgansh (2022). IoT-based Recommender Engine for Yielding Better Crops. Bhartiya Krishi Anusandhan Patrika. 37(4): 363-368. doi: 10.18805/BKAP540.
Background: Artificial Intelligence (AI) operations have evolved over the last two decades in improvising the operations for an agriculture-based economy. The operations in this area face many challenges in maximizing their crop yield while facing the challenges like deficient soil treatment, problems from pests and crop-based diseases. Technical challenges for the requirements of real-time data which can be further transformed to big data. Its impact is fetching low yield, because of the knowledge gap between farmers and technology. These points are the key motivators of introducing an ecosystem with artificial intelligence to agriculture in this research work. IoT devices are capable to generate large amounts of data that could be transformed into information about environmental parameters like the temperature of the field, which acts as an engine to provide data. All the data shall be collected, stored and further analyzed for better decision-making. One way the business uses the data collected is by nourishing it into the AI systems, which grasp the IoT data and further use it to make predictions.
Methods: This research work enables to cater the solution for supporting the farmers with IT as an enabler using data analytics on the collected information. It uses a web application that would help to monitor the soil fertility and suggest the producers to select the best crop(s) that can be grown in that geographical region.
Result: The result from our crop recommender engine successfully predicted which crop(s) we could choose to grow in our gardens or farm fields. An extensive soil study along with meteorological models helped us to launch a smart agricultural system in a much smarter way. This has further helped to bridge the gap between production and quantity yield.

  1. Chetana, A. Kestikar, R.M. (2012). Automated wireless watering system (AWWS). International Journal of Applied Information  Systems. (IJAIS). 7.

  2. FAO in India. (2021). Retrieved from FAO:

  3. Feng Yang, K.W. (2018). A cloud-based digital farm management system for vegetable production process management and quality traceability. Researchgate. Net. 19.

  4. India, G.O. (2021). Agriculture in India: Information About Indian Agriculture and Its Importance. Retrieved from aspx.

  5. Kalpataru, B., Patil, M.M. (2018). Agriculture environment monitoring system using android Wi-Fi. IJSRD-International Journal for Scientific Research and Development. 6(3): 2018. ISSN (online): 2321-0613, 6. 

  6. Ngozi Clara, E.C. (2019). Applications of artificial intelligence in agriculture: A Review. Researchgate. 8.

  7. Nalini, S., Durga, M.R. (2018). Smart irrigation system based on soil moisture using IoT. International Research Journal of Engineering and Technology. (IRJET). 5.

  8. Sai Sree Laya Chukkapalli, A.P. (2020). A smart-farming ontology for attribute based access control. Researchgate. Net. 34.

  9. Sankha Subhra Debnath, A.D. (2020). Implementation of IoT for studying different types of soils for efficient irrigation. Researchgate. 8.

  10. Sharma, M. (2021, 4). The future of Indian agriculture. Retrieved  from downtoearth:

  11. Smart Farming. Retrieved from cropin: smart-farming/.

  12. University of Vermont Extension. pH for the Garden. Retrieved from https://pss.

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