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Prediction of Crop Growth using Machine Learning Based on Seed Features


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1 Department of Computer Science and Engineering, Knowledge Institute of Technology, India
     

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The presence of plant species in the wrong place and time is identified as plant weeds. The loss of yield could result from interference with plant crop weed species. To classify the plant weeds among the seeds, seed classification is carried out in this paper. Here the image of seeds and datasets of sample seed are input. In pre-processing stage the seed image is given as input. An unwanted seeds are removed by comparing the features of seed with sample seeds features by using ID3 algorithm. One of the reasons for failure in crop yield production is selecting suitable soil for crop. As sample dataset contains the detail of growth of crop in soil, it will help for selecting the suitable soil for seeds. The features of sample dataset are compared with the features extracted from the affected crop and predict the disease and prevention measures taken place. In this method prediction is done only after the growth of crop which leads to decrease in quality of crop growth. In order to overcome these issues, diseases can be predicted using seed features by comparing the features with sample dataset. Using Support Vector Machine algorithm the seeds are classified based on the growth and predicting the diseases of crop. This is done by training the dataset by comparing the features extracted from new seeds and features of sample seeds and predicting the crop growth and diseases. Based on prediction of crop growth and crop diseases, preventive measures takes place.

Keywords

Data Mining, Big Data, Feature Extraction, Crop Growth
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  • Prediction of Crop Growth using Machine Learning Based on Seed Features

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Authors

N. Nandhini
Department of Computer Science and Engineering, Knowledge Institute of Technology, India
J. Gowri Shankar
Department of Computer Science and Engineering, Knowledge Institute of Technology, India

Abstract


The presence of plant species in the wrong place and time is identified as plant weeds. The loss of yield could result from interference with plant crop weed species. To classify the plant weeds among the seeds, seed classification is carried out in this paper. Here the image of seeds and datasets of sample seed are input. In pre-processing stage the seed image is given as input. An unwanted seeds are removed by comparing the features of seed with sample seeds features by using ID3 algorithm. One of the reasons for failure in crop yield production is selecting suitable soil for crop. As sample dataset contains the detail of growth of crop in soil, it will help for selecting the suitable soil for seeds. The features of sample dataset are compared with the features extracted from the affected crop and predict the disease and prevention measures taken place. In this method prediction is done only after the growth of crop which leads to decrease in quality of crop growth. In order to overcome these issues, diseases can be predicted using seed features by comparing the features with sample dataset. Using Support Vector Machine algorithm the seeds are classified based on the growth and predicting the diseases of crop. This is done by training the dataset by comparing the features extracted from new seeds and features of sample seeds and predicting the crop growth and diseases. Based on prediction of crop growth and crop diseases, preventive measures takes place.

Keywords


Data Mining, Big Data, Feature Extraction, Crop Growth