Effectiveness of convolutional neural network models in classifying agricultural threats
Smart farming has recently been gaining traction for more productive and effective farming. However, pests like monkeys and birds are always a potential threat for agricultural goods, primarily due to their nature of destroying and feeding on the crops. Traditional ways of deterring these threats ar...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Springer Science and Business Media Deutschland GmbH
2021
|
| Subjects: |
| _version_ | 1796866841060048896 |
|---|---|
| author | Rahman, Sayem Monzur, Murtoza Ahmad, Nor Bahiah |
| author_facet | Rahman, Sayem Monzur, Murtoza Ahmad, Nor Bahiah |
| author_sort | Rahman, Sayem |
| collection | ePrints |
| description | Smart farming has recently been gaining traction for more productive and effective farming. However, pests like monkeys and birds are always a potential threat for agricultural goods, primarily due to their nature of destroying and feeding on the crops. Traditional ways of deterring these threats are no longer useful. The use of highly effective deep learning models can pave a new way for the growth of smart farming. This study aims to investigate the manner in which deep learning convolutional neural network (CNN) models can be applied to classify birds and monkeys in agricultural environments. The performance of CNN models in this case is also investigated. In this regard, four CNN variants, namely, VGG16, VGG19, InceptionV3 and ResNet50, have been used. Experiments were conducted on two datasets. The experimental results demonstrate that all the models have the capability to perform classification in different situations. Data quality, parameters of the models, used hardware during experiments also influence the performance of the considered models. It was also found that the convolutional layers of the models play a vital role on classification performance. The experimental results achieved will assist smart farming in opening new possibilities that may help a country’s agriculture industry, where efficient classification and detection of threats are of potential importance. |
| first_indexed | 2024-03-05T21:18:14Z |
| format | Article |
| id | utm.eprints-100254 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:18:14Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | utm.eprints-1002542023-03-29T07:05:24Z http://eprints.utm.my/100254/ Effectiveness of convolutional neural network models in classifying agricultural threats Rahman, Sayem Monzur, Murtoza Ahmad, Nor Bahiah QA75 Electronic computers. Computer science Smart farming has recently been gaining traction for more productive and effective farming. However, pests like monkeys and birds are always a potential threat for agricultural goods, primarily due to their nature of destroying and feeding on the crops. Traditional ways of deterring these threats are no longer useful. The use of highly effective deep learning models can pave a new way for the growth of smart farming. This study aims to investigate the manner in which deep learning convolutional neural network (CNN) models can be applied to classify birds and monkeys in agricultural environments. The performance of CNN models in this case is also investigated. In this regard, four CNN variants, namely, VGG16, VGG19, InceptionV3 and ResNet50, have been used. Experiments were conducted on two datasets. The experimental results demonstrate that all the models have the capability to perform classification in different situations. Data quality, parameters of the models, used hardware during experiments also influence the performance of the considered models. It was also found that the convolutional layers of the models play a vital role on classification performance. The experimental results achieved will assist smart farming in opening new possibilities that may help a country’s agriculture industry, where efficient classification and detection of threats are of potential importance. Springer Science and Business Media Deutschland GmbH 2021 Article PeerReviewed Rahman, Sayem and Monzur, Murtoza and Ahmad, Nor Bahiah (2021) Effectiveness of convolutional neural network models in classifying agricultural threats. Lecture Notes on Data Engineering and Communications Technologies, 72 (NA). pp. 384-395. ISSN 2367-4512 http://dx.doi.org/10.1007/978-3-030-70713-2_36 DOI : 10.1007/978-3-030-70713-2_36 |
| spellingShingle | QA75 Electronic computers. Computer science Rahman, Sayem Monzur, Murtoza Ahmad, Nor Bahiah Effectiveness of convolutional neural network models in classifying agricultural threats |
| title | Effectiveness of convolutional neural network models in classifying agricultural threats |
| title_full | Effectiveness of convolutional neural network models in classifying agricultural threats |
| title_fullStr | Effectiveness of convolutional neural network models in classifying agricultural threats |
| title_full_unstemmed | Effectiveness of convolutional neural network models in classifying agricultural threats |
| title_short | Effectiveness of convolutional neural network models in classifying agricultural threats |
| title_sort | effectiveness of convolutional neural network models in classifying agricultural threats |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT rahmansayem effectivenessofconvolutionalneuralnetworkmodelsinclassifyingagriculturalthreats AT monzurmurtoza effectivenessofconvolutionalneuralnetworkmodelsinclassifyingagriculturalthreats AT ahmadnorbahiah effectivenessofconvolutionalneuralnetworkmodelsinclassifyingagriculturalthreats |