Wheat crop classification using deep learning

• Published in: Multimedia tools and applications 2024-03, Vol.83 (35), p.82641-82657
• Main Authors: Gill, Harmandeep Singh, Bath, Bikramjit Singh, Singh, Rajanbir, Riar, Amarinder Singh
• Format: Article
• Language: English
• Subjects: Agricultural equipment; Agricultural production; Agriculture; Classification; Climate change; Computer Communication Networks; Computer Science; Computer vision; Crop production; Crop yield; Crops; Data Structures and Information Theory; Deep learning; Food supply; High temperature; Machine learning; Multimedia; Multimedia Information Systems; Plant layout; Predictions; Productivity; Special Purpose and Application-Based Systems; Topography; Vision systems; Wheat; Winter
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18617-x

Crop yield forecasting is becoming more essential in the present environment, when food security must be maintained despite climate, population, and climate change concerns. Machine learning is a useful decision-making tool for predicting agricultural yields, as well as for deciding what crops to plant and what to do throughout the crop’s growth season. To aid agricultural production prediction studies, a number of machine learning methods have been used. Wheat is a significant food source in India, particularly in the north. The wheat crop is categorised using deep learning techniques in the proposed research. The suggested system uses deep learning CNN, RNN, and LSTM applications to classify wheat crops. The results showed that the test accuracy ranged from 85 % to 95.68 % for varietal level classification. Hence, the proposed approach results are accurate and reliable, encouraging the deployment of such an approach in practice.