Combining AI with IoT has changed the design of smart farming by providing real-time control, monitoring, and automation with data-informed decision-making. One of the major image classification features is splitting intricacies of crops, diseases, and agricultural pests. This review assesses the application of AI-powered image classification in agriculture including disease and pest detection, monitoring crop health, phenotyping, and even weed detection. Classification of visual data is constantly improving through deep learning models like CNNs. With the help of IoT devices both visual and environmental sensors, constant data collection becomes possible, improving the measure response detection. Moreover, edge and cloud computing enable efficient processing of data, making it possible to analyze large numbers of images. This paper focuses on the recent contributions of AI and IoT in the fields of agricultural image classification.