The economic growth of a nation cannot expand without agricultural output, yet plant diseases pose significant obstacles to both the quantity and quality of food. For the sake of everyone's health and well-being, it is crucial to identify plant illnesses as soon as possible. Typically, a pathologist would visit the site and physically inspect each plant to make a diagnosis. However, manual examination of different plant diseases is not always feasible due to lower precision and a lack of human resources. The development of automated technologies capable of rapidly identifying and classifying various plant diseases is crucial to address these challenges. Factors that complicate accurate identification include low-intensity background and foreground information in images, very similar colors in healthy and diseased plant areas, noise in the samples, and differences in the size, position, chrominance, and structure of plant leaves.Our reliable plant disease categorization system, built on the InceptionV3 architecture, addresses these challenges. Our study proposes an InceptionV3-based deep learning approach for detecting plant leaf diseases, achieving a 99% accuracy rate in disease prediction. Our objective is to identify and categorize plant diseases accurately. A total of 70,295 plant image sourced from the Kaggle website were used in the research. These images depicted various crops including apples, blueberries, cherries, corn (maize), grapes, oranges, peaches, bell peppers, potatoes, raspberries, soybeans, strawberries, and tomatoes. When applied to various plant regions, this approach can handle complex scenarios and accurately diagnose a variety of illnesses