Plant disease detection and classification using advanced artificial intelligence and machine learning approaches.

The agricultural sector is crucial to the economy. The agricultural sector continues to play a significant role in India's economy, accounting for around 15% of GDP. In many parts of the world, agricultural automation has become a pressing topic of discussion. Expert systems, language processing, speech recognition, and machine learning are just some of the artificial intelligence (AI) approaches that have altered not only the quantity but also the quality of agricultural sector positions in response to factors like the increasing global population, the rising global demand for food, and shifts in the environment and the availability of water. The most effective concepts that have played a major role in revolutionizing the agricultural economy throughout the pandemic are artificial intelligence (AI), Machine Learning (ML), and the internet of things (IoT). Constant disease attacks pose a threat to agriculture, lowering both the quality and volume of agricultural goods and having a significant negative impact on the national economy. These days, large-scale agricultural monitoring increasingly includes looking for signs of plant diseases. As a result, catching infections early can lessen their impact and save more crops. Manual disease identification, on the other hand, is laborious, prone to mistakes, and necessitates an in-depth understanding of plant pathogens. Changing from one method of disease control to another is a major challenge for farmers. Automated procedures, on the other hand, are more efficient. Traditional methods for detecting and identifying plant diseases rely on the trained eyes of professionals. In this work we design an automated approach for classifying and detecting plant diseases automatically by using our Application. [ABSTRACT FROM AUTHOR]