Agriculture, the mainstay for domestic economy has a big impact on human community. Maintaining the conditions of the plants and identifying diseases are critical for agricultural sustainability. A crucial crop which is prone to a number of illnesses that may adversely impact harvest quality and productivity is sunflower. These are typically grown in India during the winter season, which lasts from November to February. This is because sunflowers prefer cool temperatures for germination and early growth stages. Planting during the winter ensures that they avoid the extreme heat of the summer months, allowing for better growth and development. Additionally, sunflowers require a lot of sunlight, which is abundant during the winter season in India. In this work, we examine the prevalence of sunflower diseases in India, focusing on Leaf Scars, Gray Mold, and Downy Mildew. We explore the factors contributing to their occurrence, including climatic conditions, agronomic practices, and socioeconomic factors. We try to solve or reduce this problem by means of Deep Learning techniques with image detection to detect these three sunflowers’ diseases for early detection for effective diseases management. ResNet50 Model, VGG19 Model, DenseNet121 Model, Simple CNN Model along with Xception Model is also used for accuracy and loss of training.