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Machine learning ensemble with image processing for pest identification and classification in field crops

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Abstract

In agriculture field, yield loss is a major problem due to attack of various insects in field crops. Traditional insect identification and classification methods are time-consuming and require entomologist experts. Early information about the attack of insects helps farmers to control the crop damage to improve the productivity and reduce the use of pesticides. This research work focuses on the classification of crop insects by applying machine vision and knowledge-based techniques with image processing by using different feature descriptors including texture, color, shape, histogram of oriented gradients (HOG) and global image descriptor (GIST). A combination of all these features was used in the classification of insects. In this research, several machine learning algorithms including both base classifiers and ensemble classifiers were applied for three different insect datasets and the performances of classification results were evaluated by majority voting. Naive bayes (NB), support vector machine (SVM), K-nearest-neighbor (KNN) and multi-layer perceptron (MLP) were used as base classifiers. Ensemble classifiers include random forest (RF), bagging and XGBoost were utilized; 10-fold cross-validation test was conducted to achieve a better classification and identification of insects. The experimental results showed that the classification accuracy is improved by majority voting with ensemble classifiers in the combination of texture, color, shape, HOG and GIST features.

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Acknowledgements

This work was supported by the Department of Science and Technology, India, under women scientist B (WOS-B), Grant No. DST/Disha/SoRF-PM/059/2013. Authors thankful to the Machine Learning and Data Analytics Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, for their infrastructural support.

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  1. Machine Learning and Data Analytics Lab, Centre of Excellence in Artificial Intelligence, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tiruchirappalli, Tamil Nadu, 620015, India

    Thenmozhi Kasinathan & Srinivasulu Reddy Uyyala

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  1. Thenmozhi Kasinathan
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  2. Srinivasulu Reddy Uyyala
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Correspondence to Thenmozhi Kasinathan or Srinivasulu Reddy Uyyala.

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Kasinathan, T., Uyyala, S.R. Machine learning ensemble with image processing for pest identification and classification in field crops. Neural Comput & Applic 33, 7491–7504 (2021). https://doi.org/10.1007/s00521-020-05497-z

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