Crop Rotation based Crop Recommendation System with Soil Deficiency Analysis through Extreme Learning Machine

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : G. Murugesan, B. Radha
  10.14445/22315381/IJETT-V70I4P210

MLA 

MLA Style: G. Murugesan, and B. Radha "Crop Rotation based Crop Recommendation System with Soil Deficiency Analysis through Extreme Learning Machine." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 122-134. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P210

APA Style: G. Murugesan, & B. Radha. (2022). Crop Rotation based Crop Recommendation System with Soil Deficiency Analysis through Extreme Learning Machine. International Journal of Engineering Trends and Technology, 70(4), 122-134. https://doi.org/10.14445/22315381/IJETT-V70I4P210

Abstract
Agriculture is the pillar of the country's economy. Climatic change, soil fertility level, temperature and moisture level, pH value and the crop predecessors often impact crop yield in agriculture. The prediction of the right crop at the right place at the right time will be extremely helpful in increasing the crop yield, which also results in economic proliferation. Machine learning is an emerging technique in the field of agriculture in various ways, including soil classification, soil nutrient analysis, crop prediction or suggestion. This paper presents the crop recommendation system by considering various significant factors, including soil fertility and condition, season and crops predecessor, to recommend appropriate crops for improvising the cultivation based on precision agriculture. Based on the given input, the model applies an extreme learning machine, a fast learning classifier algorithm, for suggesting the appropriate crop to its users. The model also includes the deficiency analysis to identify the deficiency of nutrients in the soil with current crop requirements. The experimental analysis shows that the proposed model provides better accuracy of about 96.5% with a minimum false rate of 3.5% in predicting suitable crops and detecting the deficiency in the soil.

Keywords
Crop predecessor, Crop recommendation, Crop rotation, Extreme learning machine, Soil deficiency, Soil fertility.

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