Insect classification and detection in field crops using modern machine learning techniques
| Autor: | Srinivasulu Reddy Uyyala, Dakshayani Singaraju, Thenmozhi Kasinathan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
020209 energy Agriculture (General) 02 engineering and technology Information technology Aquatic Science Machine learning computer.software_genre 01 natural sciences Convolutional neural network Field (computer science) S1-972 Naive Bayes classifier Crop insect detection Image processing Crop pest classification 0202 electrical engineering electronic engineering information engineering Image segmentation Artificial neural network business.industry 010401 analytical chemistry Forestry T58.5-58.64 Class (biology) 0104 chemical sciences Computer Science Applications Support vector machine Statistical classification Identification (information) Animal Science and Zoology Artificial intelligence business Agronomy and Crop Science computer |
| Zdroj: | Information Processing in Agriculture, Vol 8, Iss 3, Pp 446-457 (2021) |
| ISSN: | 2214-3173 |
| Popis: | The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food. Crop insect detection is a challenging task for farmers as a significant portion of the crops are damaged, and the quality is degraded due to the pest attack. Traditional insect identification has the drawback of requiring well-trained taxonomists to identify insects based on morphological features accurately. Experiments were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbors (KNN), naive bayes (NB) and convolutional neural network (CNN) model. This paper presents the insect pest detection algorithm that consists of foreground extraction and contour identification to detect the insects for Wang, Xie, Deng, and IP102 datasets in a highly complex background. The 9-fold cross-validation was applied to improve the performance of the classification models. The highest classification rate of 91.5% and 90% was achieved for nine and 24 class insects using the CNN model. The detection performance was accomplished with less computation time for Wang, Xie, Deng, and IP102 datasets using insect pest detection algorithm. The comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy, computation time performance while apply more efficiently in field crops to recognize the insects. The results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture. |
| Databáze: | OpenAIRE |
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