India is a major silk producer and buyer; hence silk manufacture is vital to its economy. Given India's substantial reliance on agriculture for GDP growth, sericulture - the process of raising silkworms to produce raw silk—is a vital economic activity. The main steps in sericulture are growing plant life for silkworms, spinning silk cocoons, reeling silk filaments, and weaving them into textiles. Silkworm diseases account for 30-40% of production losses. Silkworm illnesses include Grasserie, which is frequent. Silkworm diseases are diagnosed using several medical and laboratory methods. Technology is changing many aspects of our lives, and the agriculture sector has welcomed it. Sericulture has delayed implementing such advancements. While numerous methods have emerged for detecting silkworm eggs and moths, early detection remains difficult. Early disease detection can help farmers prevent disease spread. This research focuses on silk-worm diseases utilizing photo-type and deep-learning models. A device mastery system has been taught to distinguish healthy and unhealthy silkworms using a deep neural network and a CNN, with promising accuracy. TensorFlow was used to create layers and train the algorithm to learn the version. In conclusion, this study uses image categorization and deep learning to detect silkworm diseases. CNN in a deep neural network and TensorFlow have allowed a device to learn a version that can categorize healthy and harmful silkworms.