Plant Leaf Disease Identification by Deep Convolutional Autoencoder as a Feature Extraction Approach

Trang, Kien; TonThat, Long; Thao, Nguyen Gia Minh

doi:10.1109/ecti-con49241.2020.9158218

Cited by 13 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, this system has been found to be time-consuming. Trang et al 18 introduced the use of an autoencoder as a feature extraction method for plant leaf disease detection, employing a dataset of leaf images. Initially, the original input leaf image was reconstructed using the encoder, and the features were extracted through the encoder.…”

Section: Machine Learning (Ml) Based Techniques For Plants Disease Id...mentioning

confidence: 99%

Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI

Natarajan,

Chakrabarti,

Margala

2024

Sci Rep

View full text Add to dashboard Cite

Deep learning has emerged as a highly effective and precise method for classifying images. The presence of plant diseases poses a significant threat to food security. However, accurately identifying these diseases in plants is challenging due to limited infrastructure and techniques. Fortunately, the recent advancements in deep learning within the field of computer vision have opened up new possibilities for diagnosing plant pathology. Detecting plant diseases at an early stage is crucial, and this research paper proposes a deep convolutional neural network model that can rapidly and accurately identify plant diseases. Given the minimal variation in image texture and color, deep learning techniques are essential for robust recognition. In this study, we introduce a deep, explainable neural architecture specifically designed for recognizing plant diseases. Fine-tuned deep convolutional neural network is designed by freezing the layers and adjusting the weights of learnable layers. By extracting deep features from a down sampled feature map of a fine-tuned neural network, we are able to classify these features using a customized K-Nearest Neighbors Algorithm. To train and validate our model, we utilize the largest standard plant village dataset, which consists of 38 classes. To evaluate the performance of our proposed system, we estimate specificity, sensitivity, accuracy, and AUC. The results demonstrate that our system achieves an impressive maximum validation accuracy of 99.95% and an AUC of 1, making it the most ideal and highest-performing approach compared to current state-of-the-art deep learning methods for automatically identifying plant diseases.

show abstract

Section: Machine Learning (Ml) Based Techniques For Plants Disease Id...mentioning

confidence: 99%

Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI

Natarajan,

Chakrabarti,

Margala

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Jogin et al modified the AlexNet model for feature extraction on the CIFAR-10 dataset [38], and reported an image classification accuracy of 85.97%. Tranget et al classified plant diseases using a convolutional autoencoder [39] with two kernels of coder layer to extract the plant leaf images. Although the classification accuracy reached 98.8% with the SVM classifier, the number of encoder layers and pooling functions were optimized to the dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

View full text Add to dashboard Cite

Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

show abstract

“…Estas técnicas también han sido aplicadas conéxito para la detección de plagas como se demuestra en diversos trabajos [13], [3] y enfermedades de plantas [16]. Además de los autoencoders [18], que permiten reconstruir patrones a partir de muy escasa información de entrada, por lo que es posible detectar plagas en plantaciones hortofrutícolas con pequeños patrones. Otros enfoques, aunque en menor medida, también utilizan Differential Recurrent Neural Networks (DRNN), Long Short-Term Memory (LSTM), y deep belief networks (DBN).…”

Section: Técnicas De Visión Por Computador E Inteligencia Artificialunclassified

Revisión de tecnologías habilitadoras para el control biológico y de plagas en el sector hortofrutícola

Martin¹,

Llopis²,

Rubio³

et al. 2021

Xlii Jornadas De Automática : Libro De Actas

View full text Add to dashboard Cite

La monitorización continua tanto de plagas que afectan a los cultivos hortofrutícolas en agricultura intensiva de invernadero, como de la lucha biológica, utilizada en este tipo de cultivos para combatir las plagas de una forma sostenible con el medio ambiente son esenciales en este sector. Actualmente, en la mayoría de los casos este control se realiza de forma manual (no siempre sin dificultad), lo que requiere a los agricultores de una supervisión continua para poder detectar, en el menor tiempo posible, cualquier brote que afecte a sus cultivos. En este trabajo se presenta una revisión de tecnologías habilitadoras para permitir una monitorización continua, sostenible y de forma autónoma en este sector estratégico en España. Estas tecnologías se integran en una propuesta de sistema de ayuda a la decisión para el control de plagas y control biológico.

show abstract

Plant Leaf Disease Identification by Deep Convolutional Autoencoder as a Feature Extraction Approach

Cited by 13 publications

References 19 publications

Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI

Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Revisión de tecnologías habilitadoras para el control biológico y de plagas en el sector hortofrutícola

Contact Info

Product

Resources

About