A novel approach in prediction of crop production using recurrent cuckoo search optimization neural networks
- Title
- A novel approach in prediction of crop production using recurrent cuckoo search optimization neural networks
- Creator
- Rajagopal A.; Jha S.; Khari M.; Ahmad S.; Alouffi B.; Alharbi A.
- Description
- Data mining is an information exploration methodology with fascinating and understand-able patterns and informative models for vast volumes of data. Agricultural productivity growth is the key to poverty alleviation. However, due to a lack of proper technical guidance in the agriculture field, crop yield differs over different years. Mining techniques were implemented in different applications, such as soil classification, rainfall prediction, and weather forecast, separately. It is proposed that an Artificial Intelligence system can combine the mined extracts of various factors such as soil, rainfall, and crop production to predict the market value to be developed. Smart analysis and a comprehensive prediction model in agriculture helps the farmer to yield the right crops at the right time. The main benefits of the proposed system are as follows: Yielding the right crop at the right time, balancing crop production, economy growth, and planning to reduce crop scarcity. Initially, the database is collected, and the input dataset is preprocessed. Feature selection is carried out followed by feature extraction techniques. The best features were then optimized using the recurrent cuckoo search optimization algorithm, then the optimized output can be given as an input for the process of classification. The classification process is conducted using the Discrete DBN? VGGNet classifier. The performance estimation is made to prove the effectiveness of the proposed scheme. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Source
- Applied Sciences (Switzerland), Vol-11, No. 21
- Date
- 2021-01-01
- Publisher
- MDPI
- Subject
- Crop production; Data mining; Discrete DBN?VGG Net classifier; Recurrent cuckoo search optimization algorithm
- Coverage
- Rajagopal A., Department of Computer Science and Business Systems, Sethu Institute of Technology, Virudhunagar, Kariapatti, 626115, India; Jha S., School of Sciences, Christ (Deemed to be University), NCR?New Delhi Campus, Ghaziabad, 201003, India; Khari M., School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India; Ahmad S., Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Alouffi B., Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Alharbi A., Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 20763417
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Rajagopal A.; Jha S.; Khari M.; Ahmad S.; Alouffi B.; Alharbi A., “A novel approach in prediction of crop production using recurrent cuckoo search optimization neural networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 28, 2025, https://archives.christuniversity.in/items/show/15581.