Review on AI-Powered Plant Disease Detection: Advancements, Challenges, And Future Directions in Precision Agriculture

Early detection of plant leaf diseases (PLD) is essential for protecting crops, reducing yield losses, and ensuring food security. Traditional disease detection methods depend on manual inspection, which is time-consuming, labor-intensive, and often unreliable for small-scale farmers. To overcome these issues, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have transformed plant disease detection (PDD) by providing fast, automated, and highly accurate image-based classification. This review focuses on AI-based techniques used for detecting diseases in different crops such as corn, mango, tomato, apple, rice, tea, potato, wheat, palm oil, and citrus. It highlights recent progress in DL models, feature fusion methods, and real-time solutions like IoT-based monitoring systems and mobile apps. Although these technologies have greatly improved DD accuracy and speed, challenges still exist, including the need for large labeled datasets, high computational resources, and extensive testing under real-world conditions.