This paper proposes a methodology for the classification of leaf diseases by using the different characterization of shape, and colour properties. Paddy plant diseases are discussed in this research work. Bacterial light, Brown Spot, Leaf smut diseases is identified in paddy crops. A plant leaf is pre-processed at first utilizing the improved BAHE (brightness adapted histogram equalization). Leaf image is segmented using K mean clustering algorithm. The feature extraction is improved by combination of texture feature and colour features. The extracted feature is given as an input for feature selection using optimized firefly algorithm's after that it is used for the classification of diseases in paddy plant. The optimization result is compared with PSO and Genetic algorithm..

Artificial intelligence in education (AIED) applications have been made easier to implement largely to the quick development of computing technologies. In order to improve teaching, learning, or decision-making, AIED refers to the employment of AI technology or application programmes in educational contexts. This study analyses every facet of the education sector's use of AI tools and technologies in great detail. The findings show how we may integrate AI development into these teaching, learning, student assistance, and management technologies. This study attempts to show how AI is being used in learning and teaching in the digital age.