Crop yield prediction: two-tiered machine learning model approach

Nutrient deficiency analysis is essential to ensure good yield. The crop yield is dependent on the nutrient contents and drastically affects the health of the crop. In this paper the nutrient deficiency of a paddy crop is considered. Tensor Flow’s (Google’s Machine Learning Library) is used to build...

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Bibliographic Details
Published in:International journal of information technology (Singapore. Online) 2021-10, Vol.13 (5), p.1983-1991
Main Authors: Shidnal, Sushila, Latte, Mrityunjaya V., Kapoor, Ayush
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Imaging
Computer Science
Image Processing and Computer Vision
Machine Learning
Original Research
Pattern Recognition and Graphics
Software Engineering
Vision
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Summary:Nutrient deficiency analysis is essential to ensure good yield. The crop yield is dependent on the nutrient contents and drastically affects the health of the crop. In this paper the nutrient deficiency of a paddy crop is considered. Tensor Flow’s (Google’s Machine Learning Library) is used to build a neural network to classify them into nitrogen, potassium, phosphorous deficiencies or healthy independently. It is necessary to have an optimal balance between nitrogen, potassium and phosphorous content. Tensor Flow’s model identifies the deficiency using a set of images. The result is fed to “machine learning driven layer” to estimate the level of deficiency on a quantitative basis. It specifically makes use of k means-clustering algorithm. It is then evaluated through the rule-matrix to estimate the cropland’s yield. A fair prediction of 76–77% was observed with two tired machine learning models.
ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-019-00375-x