A transfer learning-based artificial intelligence model for leaf disease assessment
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Sustainability
Abstract
The paddy crop is the most essential and consumable agricultural produce. Leaf disease
impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible
is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have
been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns
in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and
color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networksbased
Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural
research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet,
and VGG19, were considered to carry out disease detection in paddy plants. The approach started
with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region
of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra,
fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf
disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and
bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models
with different variants of TL architectures. After analysis of the outcomes, the study concluded that
the anticipated model outperforms other existing models.
Description
Keywords
artificial intelligence; transfer learning; paddy leaf disease detection; crop disease classification