This review paper addresses the critical need for advanced rice disease detection methods
by integrating artificial intelligence, specifically convolutional neural networks (CNNs). Rice …

The recognition and classification of rice plant diseases require an accurate system to
generate classification data. Types of rice diseases can be identified in several ways, one of …

More than 80% of the human diet depends on plants. Diseases in plants can cause food
insecurity, colossal crop loss, and low profit for growers. Therefore, autonomous systems are …

This study is based on a convolutional neural network model built with deep learning
technology, and compares the mainstream target detection algorithms ResNet v2, Mask R …

With the digital revolution, thanks to data already prepared or collected via sensors-a
camera, for example with transmission systems, computer data constitutes a resource of …

Plant pests and diseases have always been an important factor affecting agricultural
production, and how to automatically identify them is one of the current research hotspots …

Crop disease diagnosis is of great significance for crop yield and agricultural production.
Deep learning methods have become a major research direction for solving crop disease …

Rice cultivation faces significant challenges due to diseases such as bacterial blight, Blast,
brown spot, and tungro, which diminish the production and quality of crops. Additionally …