Deep transfer modeling for classification of Maize Plant Leaf Disease

• Published in: Multimedia tools and applications Vol. 81; no. 5; pp. 6051 - 6067
• Main Authors: Singh, Rajeev Kumar, Tiwari, Akhilesh, Gupta, Rajendra Kumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer Nature B.V
• Subjects: Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Deep learning; Economic impact; Feature extraction; Image analysis; Image classification; Iterative methods; Machine learning; Medical imaging; Multimedia Information Systems; Plant diseases; Special Purpose and Application-Based Systems; Deep learning; Convolution layer; Feature extraction; Max pooling; Maize Plant Disease identification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-11763-6

Currently, Deep Learning is playing an influential role for Image analysis and object classification. Maize’s diseases reduce production that subsequently becomes prominent factor for the economic losses in the agricultural industry worldwide. Previously, researchers have used hand-crafted-features for image classification and detection of leaf diseases in Maize plant. Nowadays, the development of the Deep Learning has allowed researchers to drastically improve the accuracy of object identification and classification. Therefore, this paper explores AlexNet model for fast and accurate detection of leaf disease in maize plant. For validating the result, we have used PlantVillage dataset. This dataset contains two categories of maize diseases namely leaf-spot based diseases (Cercospora and Gray) and the Common rust based diseases. The former category contains 1363 images and the latter category contains 929 images. One of the biggest advantages of CNN is to automatically extract the features by processing the raw images directly. By using various iteration such as 25, 50, 75 and 100, our model has obtained an accuracy of 99.16%. In future, the proposed work can be used as a practical tool to help farmer in detecting the aforementioned diseases and protect the maize crops.