Cotton Crop Disease Detection Using Inception v3 Model

In recent years, the agricultural sector has witnessed a¯ surge in innovative technologies aimed at enhancing crop health and productivity. Among these advancements, the de- velopment of crop disease detection methods has emerged as a crucial area of research. This paper provides a comprehen- sive review of the latest techniques and technologies employed in crop disease detection, ranging from traditional methods to stateof-the-art approaches leveraging artificial intelligence and machine learning. We delve into the challenges faced in early disease detection, explore the role of remote sensing and imag- ing technologies, and examine the integration of data analytics and sensor networks. Furthermore, we discuss the implications of these advancements for sustainable agriculture and food se- curity. By synthesizing existing literature and highlighting cur- rent trends, this paper aims to provide researchers and practi- tioners with valuable insights into the evolving landscape of crop disease detection.