The health of the plant and the safety of the food are linked closely. Plant diseases become more complex to the farmers if they are not observed in the early stages. This will become more dangerous to the crop yielding and may reduce the production also. Several types of diseases are identified by the researchers that can cause huge losses to the farmers. Machine learning (ML) algorithms are most widely used to detect the accurate patterns of plant diseases but it is very complex to detect the plant diseases accurately. Deep learning (DL) is most widely used to process complex and large datasets efficiently. In this paper, an integrated learning approach (ILA) is introduced to detect plant diseases on leaves. ILA is the approach integrated with noise filters for removing noise from the leaf images and detecting the depth of the infected region from the leaf image. Advanced training is used to train on strong features of plant diseases. This will help us to increase the accurate detection of plant diseases. The experiments are conducted on two publically available datasets as Kaggle plant diseases dataset and the PlantVillage dataset is used and which consists of 87,848 leaf images containing healthy and disease infected plants. The performance is measured by using the sensitivity, specificity, accuracy, and detection rate.