VGG-ICNN: A Lightweight CNN model for crop disease identification

• Published in: Multimedia tools and applications Vol. 82; no. 1; pp. 497 - 520
• Main Authors: Thakur, Poornima Singh, Sheorey, Tanuja, Ojha, Aparajita
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2023; Springer Nature B.V
• Subjects: Agricultural production; Artificial neural networks; Computer Communication Networks; Computer Science; Crop diseases; Data Structures and Information Theory; Datasets; Deep learning; Image processing; Lightweight; Machine learning; Medical imaging; Multimedia Information Systems; Plant diseases; Signs and symptoms; Special Purpose and Application-Based Systems; Deep learning; Crop disease identification; Convolutional neural network; Classification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-13144-z

Crop diseases cause a substantial loss in the quantum and quality of agricultural production. Regular monitoring may help in early stage disease detection an d thereby reduction in crop loss. An automatic plant disease identification system based on visual symptoms can provide a smart agriculture solution to such problems. Various solutions for plant disease identification have been provided by researchers using image processing, machine learning and deep learning techniques. In this paper a lightweight Convolutional Neural Network ‘VGG-ICNN’ is introduced for the identification of crop diseases using plant-leaf images. VGG-ICNN consists of around 6 million parameters that are substantially fewer than most of the available high performing deep learning models. The performance of the model is evaluated on five different public datasets covering a large number of crop varieties. These include multiple crop species datasets: PlantVillage and Embrapa with 38 and 93 categories, respectively, and single crop datasets: Apple, Maize, and Rice, each with four, four, and five categories, respectively. Experimental results demonstrate that the method outperforms some of the recent deep learning approaches on crop disease identification, with 99.16% accuracy on the PlantVillage dataset. The model is also shown to perform consistently well on all the five datasets, as compared with some recent lightweight CNN models.