This paper presents a comprehensive cotton leaf disease dataset for enhanced detection and classification. The dataset includes 2137 original images and 7000 augmented images, covering eight disease classes such as bacterial blight, curl virus, herbicide damage, leaf hopper, leaf reddening, and leaf variegation. The images were collected from cotton fields in Gazipur, Bangladesh, between October 2023 and January 2024, under diverse environmental conditions. The dataset is designed to support the development of accurate machine learning models for early disease detection, reducing manual inspections and enabling timely interventions. The Inception V3 model achieved an overall accuracy of 96.03%, demonstrating the dataset's potential in advancing automated disease detection in cotton agriculture. The dataset serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture. It contributes to the development of disease-resistant cotton varieties and effective management strategies, ultimately reducing economic losses and promoting sustainable farming. The dataset is available for public access and is a valuable resource for researchers, agricultural scientists, and policymakers. The study highlights the importance of data augmentation in improving model performance and the potential of the dataset in advancing precision agriculture practices. The dataset also supports the development of remote sensing technologies for monitoring cotton leaf health and disease dynamics. The study emphasizes the need for global collaboration in agricultural research and the role of data-driven insights in developing integrated disease management strategies. The dataset is a significant contribution to the field of agricultural technology and has the potential to enhance crop health and productivity in cotton-growing regions.This paper presents a comprehensive cotton leaf disease dataset for enhanced detection and classification. The dataset includes 2137 original images and 7000 augmented images, covering eight disease classes such as bacterial blight, curl virus, herbicide damage, leaf hopper, leaf reddening, and leaf variegation. The images were collected from cotton fields in Gazipur, Bangladesh, between October 2023 and January 2024, under diverse environmental conditions. The dataset is designed to support the development of accurate machine learning models for early disease detection, reducing manual inspections and enabling timely interventions. The Inception V3 model achieved an overall accuracy of 96.03%, demonstrating the dataset's potential in advancing automated disease detection in cotton agriculture. The dataset serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture. It contributes to the development of disease-resistant cotton varieties and effective management strategies, ultimately reducing economic losses and promoting sustainable farming. The dataset is available for public access and is a valuable resource for researchers, agricultural scientists, and policymakers. The study highlights the importance of data augmentation in improving model performance and the potential of the dataset in advancing precision agriculture practices. The dataset also supports the development of remote sensing technologies for monitoring cotton leaf health and disease dynamics. The study emphasizes the need for global collaboration in agricultural research and the role of data-driven insights in developing integrated disease management strategies. The dataset is a significant contribution to the field of agricultural technology and has the potential to enhance crop health and productivity in cotton-growing regions.