Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Chen, Jing; Liu, Qi; Gao, Lingwang

doi:10.3390/sym11030343

Cited by 150 publications

(65 citation statements)

References 34 publications

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“…The 80/20 ratio of training/test data is the most commonly used ratio in neural network applications. In addition, a 10% subset of the test dataset was used to validate the dataset [29].…”

Section: Date Acquisitionmentioning

confidence: 99%

Automatic Recognition of Tea Diseases Based on Deep Learning

Chen¹,

Jia²

2020

Advances in Forest Management Under Global Change

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With the rapid development of intelligent agriculture and precision agriculture, computer image processing technology has been widely used to solve various problems in the agricultural field. In particular, the advantages of convolutional neural networks (CNNs) in image classification have also been widely used in the automatic recognition and classification of plant diseases. In this paper, a deep convolutional neural network named LeafNet capable of recognizing the seven types of diseases from tea leaf disease images was established, with an accuracy of up to 90.23%, aiming to provide timely and accurate diagnostic services in the remote and topographic tea plantation in China. At the same time, the traditional machine learning algorithm is applied for comparative analysis, which extracts the dense scale-invariant feature transform (DSIFT) of the image and constructs the bag of visual word (BOVW) model to express the image based on the DSIFT descriptor. The support vector machines (SVMs) and multilayer perceptron (MLP) were used to identify tea leaf diseases, with an accuracy of 60.91 and 70.94%, respectively.

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“…The 80/20 ratio of training/test data is the most commonly used ratio in neural network applications. In addition, a 10% subset of the test dataset was used to validate the dataset [29].…”

Section: Date Acquisitionmentioning

confidence: 99%

Automatic Recognition of Tea Diseases Based on Deep Learning

Chen¹,

Jia²

2020

Advances in Forest Management Under Global Change

Self Cite

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“…For example, the famous GoogLeNet model was improved to achieve better testing accuracy for the identification of maize leaf disease in a small period due to its lesser number of parameters [ 20 ]. Similarly, inspired by the AlexNet model, a modified CNN architecture was proposed that had a lesser number of filters in convolutional layers and number of nodes, which apparently reduced overall parameters as compared to the original model and successfully identified the disease in tea leaves [ 21 ]. By using an extended version of the PlantVillage dataset, two modified versions of MobileNet models were proposed and their performance was compared with the original model (MobileNet), AlexNet, and VGG models [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Saleem

Potgieter

Arif

2020

Plants

161

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Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

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“…The performance of these pre-trained networks is (generally) excellent and in some cases, it resulted in an accuracy greater than 99%. In few other studies, authors have used their own developed datasets for the classification of various crop diseases [21][22][23][24]. Despite these efforts, the dataset for many crops is limited which is the critical input for training the deep learning-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Automated disease classification in (Selected) agricultural crops using transfer learning

Aravind

Raja

2020

Automatika

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The biotic stress of agricultural crops is a major concern across the globe. Especially, its major effects are felt in economically poor countries where advanced facilities for diagnosis of a disease is limited as well as lack of awareness among the farmers. A recent revolution in smartphone technology and deep learning techniques have created an opportunity for automated classification of disease. In this study images acquired through smartphone are transmitted to a personal computer via a wireless Local Area Network (LAN) for classification of ten different diseases using transfer learning in four major agricultural crops which are least explored. Six pre-trained Convolutional Neural Network (CNN) have been used namely AlexNet, Visual Geometry Group 16 (VGG16), VGG19, GoogLeNet, ResNet101 and DenseNet201 with its corresponding results explored. GoogLeNet resulted in the best validation accuracy of 97.3%. The misclassification was mainly due to Tobacco Mosaic Virus (TMV) and two-spotted spider mite. In test conditions, images were classified in real-time and prediction scores have been evaluated for each disease class. It depicted a reduction in accuracy in all models with VGG16 resulting in the best accuracy of 90%. Various factors contributing to the reduction in accuracy and future scope for improvement have been elucidated.

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Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Cited by 150 publications

References 34 publications

Automatic Recognition of Tea Diseases Based on Deep Learning

Automatic Recognition of Tea Diseases Based on Deep Learning

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Automated disease classification in (Selected) agricultural crops using transfer learning

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