Identification of plant leaf diseases using a nine-layer deep convolutional neural network

By G. G, Arun Pandian J.. Computers & electrical engineering, 2019

717 citations recorded by the source corpus.

Abstract In this paper, we proposed a novel plant leaf disease identification model based on a deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 39 different classes of plant leaves and background images. Six types of data augmentation methods were used: image flipping, gamma correction, noise injection, principal component analysis (PCA) colour augmentation, rotation, and scaling. We observed that using data augmentation can increase the performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. Compared with popular transfer learning approaches, the proposed model achieves better performance when using the validation data. After an extensive simulation, the proposed model achieves 96.46% classification accuracy. This accuracy of the proposed work is greater than the accuracy of traditional machine learning approaches. The proposed model is also tested with respect to its consistency and reliability.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/239b3f0371d3bd11e555e12328c721dd65f02024

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Plant Science

Citations in the corpus

Plant disease detection and classification techniques: a comparative study of the performances (2024, background): “The authors of [81] have described a novel plant leaf disease identification model based on a DCNN.”
Plant leaf disease classification using EfficientNet deep learning model (2021, methodology): “Geetharamani and Pandian trained the 9-layer CNN architecture in the PlantVillage dataset with different epoch, batch size and dropout and compared the performance of achieved models with popular transfer learning approaches.”
Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, background): “In [35], the authors proposed a nine-layer CNN model to identify plant diseases.”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, background): “It should be noted that 19 studies (S1, S6, S7, S11, S12, S13, S22, S35, S43, S61, S68, S70, S75, S84, S86, S91, S100, S116, S120) propose new architectures and 15 (S9, S18, S19, S23, S32, S34, S39, S41, S42, S49, S50, S51, S53, S65, S103) investigations customize classic algorithms to solve the pro”