The Design of Hybrid Crop Recommendation System using Machine Learning Algorithms

Viviliya*, Ms.; V, Prof. Vaidhehi

doi:10.35940/ijitee.b7219.129219

Cited by 15 publications

(2 citation statements)

References 7 publications

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“…This model also warns that the crops which should not be taken due to threat of less yield. In work carried out in [21,22,23] developed a model based on IoT and ML which can be used for soil testing using the wireless sensors. Using ML algorithms it recommends the crop.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Joshi-Bag,

Vyas

2023

IJEEI

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Joshi-Bag,

Vyas

2023

IJEEI

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“…Precision: 65% [62] This paper proposed a hybrid RS based on two classification algorithms by considering various attributes.…”

Section: Rfmentioning

confidence: 99%

Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane,

Rhanbouri,

Chouikh

et al. 2023

Studies in Computational Intelligence

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This paper draws a systematic literature review about the use of Machine learning based recommender systems for crop selection, with respect to the PRISMA protocol for systematic reviews. The second section, describes an overview of existing recommender systems in literature. The outline of this study is explained, as well as the method of content analysis used in this article to sort out the papers is introduced in the third section. In the fourth section, the selection process and the literature review matrix are provided. Additionally, the evolvement of research on crop recommendation over the years is considered, a detailed study of the main input features is done. Further, the current challenges found in crop recommendation are listed. The fth section discusses the outcomes of the study. The last section recapitulates the outcomes of the study, shedding the light of research perspectives worthwhile pursuing.

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Challenges in Crop Selection Using Machine Learning

Padmaja

Sriharsha

et al. 2022

Communications in Computer and Information Science

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The Design of Hybrid Crop Recommendation System using Machine Learning Algorithms

Cited by 15 publications

References 7 publications

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

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

Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature Review

Challenges in Crop Selection Using Machine Learning

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