Identification of tea leaf diseases by using an improved deep convolutional neural network

Hu, Gensheng; Yang, Xin-She; Zhang, Yan; Wan, Mingzhu

doi:10.1016/j.suscom.2019.100353

Cited by 75 publications

(29 citation statements)

References 10 publications

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“…There has been an increasing growth of research focusing on plant disease classification in the past years aiming to develop effective plant disease diagnostics systems for farmers. Artificial intelligence (AI) methods have been adopted in classifying and detecting various plant diseases such as olive (Cruz et al, 2017), pomegranate (Pawar & Jadhav, 2017), rice (Li et al, 2020), tomato (Fuentes et al, 2017;Tran et al, 2019), cassava (Ramcharan et al, 2019), mango (Singh et al, 2019), orange (Capizzi et al, 2016), tea leaf (Hu et al, 2019;Chen et al, 2019), apple leaf (Jiang et al, 2019;Rehman et al, 2021), citrus (Dhawale et al, 2016;Iqbal et al, 2018), and so on. Liang et al (2019), and .…”

Section: Related Workmentioning

confidence: 99%

Cassava disease recognition from low‐quality images using enhanced data augmentation model and deep learning

et al. 2021

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Improvement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real-world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low-quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour-based features, which are less sensitive to the deficiencies of low-quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down-sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower-quality testing images when compared with the baseline network.The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.

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Section: Related Workmentioning

confidence: 99%

Cassava disease recognition from low‐quality images using enhanced data augmentation model and deep learning

et al. 2021

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“…Thus, combined with the corresponding agricultural knowledge, images of healthy and diseased leaves can be used as the input of CNN to train the identification model. Methods have been applied to a variety of food and cash crops including but not limited to rice [12,13], corn [14], tea [15][16][17], cannabis [18], and apple [19].…”

Section: Related Workmentioning

confidence: 99%

A Sparse Deep Transfer Learning Model and Its Application for Smart Agriculture

Chen

Zhang

Chen

et al. 2021

Wireless Communications and Mobile Computing

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The introduction of deep transfer learning (DTL) further reduces the requirement of data and expert knowledge in various uses of applications, helping DNN-based models effectively reuse information. However, it often transfers all parameters from the source network that might be useful to the task. The redundant trainable parameters restrict DTL in low-computing-power devices and edge computing, while small effective networks with fewer parameters have difficulty transferring knowledge due to structural differences in design. For the challenge of how to transfer a simplified model from a complex network, in this paper, an algorithm is proposed to realize a sparse DTL, which only transfers and retains the most necessary structure to reduce the parameters of the final model. Sparse transfer hypothesis is introduced, in which a compressing strategy is designed to construct deep sparse networks that distill useful information in the auxiliary domain, improving the transfer efficiency. The proposed method is evaluated on representative datasets and applied for smart agriculture to train deep identification models that can effectively detect new pests using few data samples.

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“…The VGGNet achieved a success rate of 99.53% accuracy on that dataset. Gensheng et al [13] implemented a pre-trained CIFAR10-quick CNN model for tea leaf disease identification. Their experimental results indicate that identification accuracy is 92.5%.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Apple Leaf Diseases using Faster R-CNN

Sardoğan

Özen

Tuncer

2020

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Image recognition-based automated disease detection systems play an important role in the early detection of plant leaf diseases. In this study, an apple leaf disease detection system was proposed using Faster Region-Based Convolutional Neural Network (Faster R-CNN) with Inception v2 architecture. Applications for the detection of diseases were carried out in apple orchards in Yalova, Turkey. Leaf images were obtained from different apple orchards for two years. In our observations, it was determined that apple trees of Yalova had black spot (venturia inaequalis) disease. The proposed system in the study detects a large number of leaves in an image, then successfully classifies diseased and healthy ones. The disease detection system trained has achieved an average of 84.5% accuracy.

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Identification of tea leaf diseases by using an improved deep convolutional neural network

Cited by 75 publications

References 10 publications

Cassava disease recognition from low‐quality images using enhanced data augmentation model and deep learning

Cassava disease recognition from low‐quality images using enhanced data augmentation model and deep learning

A Sparse Deep Transfer Learning Model and Its Application for Smart Agriculture

Detection of Apple Leaf Diseases using Faster R-CNN

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