Enhancing Agriculture Crop Classification with Deep Learning
Yasmin Makki Mohialden, Nadia Mahmood Hussien, Saba Abdulbaqi Salman, Ahmed Bahaaulddin A. Alwahhab, Mumtaz Ali
Abstract
To classify rice crops, the paper applies deep learning to agricultural crop images to classify rice crops. The collection includes images of wheat, rice, sugarcane, jute, and maize. We improved variety by horizontally flipping, rotating, and shifting rice image data sets. A CNN structure classifies rice and non-rice crops. The model has 100% accuracy on training and testing datasets; however, the classification report shows label imbalance problems for precision, recall, and F-score. Deep learning can help classify crops as well as make decisions in agriculture based on research. The study recommends more studies and improvements to enhance model performance and address dataset concerns. The research advances agricultural technology and emphasizes machine learning for crop management and production.