Enhanced Disease Identification Model for Tea Plant Using Deep Learning

Jayapal, Santhana Krishnan; Poruran, Sivakumar

doi:10.32604/iasc.2023.026564

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…Te frst challenge is about the visual symptoms. Te brown blight, white blight, and bud blight visual symptoms are similar to the grey-blight disease in tea leaves [13,[27][28][29] that leads to the disease detection models misclassifying the diseases. Second, there is a minimum number of research studies considered to diagnose the grey blight disease in tea crops [30,31].…”

Section: Related Workmentioning

confidence: 99%

Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network

Pandian

Nisha

Kanchanadevi

et al. 2023

Computational Intelligence and Neuroscience

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We proposed a novel deep convolutional neural network (DCNN) using inverted residuals and linear bottleneck layers for diagnosing grey blight disease on tea leaves. The proposed DCNN consists of three bottleneck blocks, two pairs of convolutional (Conv) layers, and three dense layers. The bottleneck blocks contain depthwise, standard, and linear convolution layers. A single-lens reflex digital image camera was used to collect 1320 images of tea leaves from the North Bengal region of India for preparing the tea grey blight disease dataset. The nongrey blight diseased tea leaf images in the dataset were categorized into two subclasses, such as healthy and other diseased leaves. Image transformation techniques such as principal component analysis (PCA) color, random rotations, random shifts, random flips, resizing, and rescaling were used to generate augmented images of tea leaves. The augmentation techniques enhanced the dataset size from 1320 images to 5280 images. The proposed DCNN model was trained and validated on 5016 images of healthy, grey blight infected, and other diseased tea leaves. The classification performance of the proposed and existing state-of-the-art techniques were tested using 264 tea leaf images. Classification accuracy, precision, recall, F measure, and misclassification rates of the proposed DCNN are 98.99%, 98.51%, 98.48%, 98.49%, and 1.01%, respectively, on test data. The test results show that the proposed DCNN model performed superior to the existing techniques for tea grey blight disease detection.

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Section: Related Workmentioning

confidence: 99%

Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network

Pandian

Nisha

Kanchanadevi

et al. 2023

Computational Intelligence and Neuroscience

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“…Chen et al [4] provided a summary of the application of NIRS, EN, ET, and computer vision techniques for this purpose, while Zhu et al [9] focused on the use of NIRS alone. Lin et al [10] reviewed various vibrational spectroscopic techniques, including NIR, MIR, Raman, terahertz (THz) spectroscopy, and HSI technologies, for tea quality and safety analysis. These methods, combined with machine learning and neural networks, have the potential to quantitatively predict tea quality components and evaluate tea safety.…”

Section: Introductionmentioning

confidence: 99%

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Cao,

Zhao,

et al. 2024

International Journal of Agricultural and Biological Engineering

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The tea plant is a valuable and evergreen crop that is extensively cultivated in China and many other countries. Currently, there is growing research interest in this plant. For the tea industry, it is crucial to develop rapid and non-invasive methods to evaluate tea plants in their natural environment. This article provides a comprehensive overview of non-invasive sensing techniques used for in-situ detection of tea plants. The topics covered include leaf, canopy, and field-level assessments, as well as statistical analysis techniques and characteristics specific to the research. Non-invasive testing technology is primarily used for monitoring and predicting tea pests and diseases, monitoring quality, and nutrients, determining tenderness and grade, identifying tea plant varieties, automatically detecting, and identifying tea buds, monitoring tea plant growth, and extracting tea garden areas through remote sensing. It also helps to evaluate planting suitability, assess disasters, and estimate yields. Additionally, the article examines the challenges and prospects of emerging techniques aimed at resolving the in-situ detection problem for tea plants. It can assist researchers and producers in comprehensively understanding the tea environment, quality characteristics, and growth process, thereby enhancing tea production quality, and fostering tea industry development.

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“…In the realm of tea disease and pest recognition, Santhana Krishnan Jayapal et al [ 30 ] introduced an image retrieval model specifically tailored for tea disease recognition. This model, leveraging depth hash and an integrated self-encoder, exhibits superior MAP (Mean Average Precision) scores compared to state-of-the-art methods.…”

Section: Introductionmentioning

confidence: 99%

Deep migration learning-based recognition of diseases and insect pests in Yunnan tea under complex environments

Li,

Sun,

Shen

et al. 2024

Plant Methods

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Background The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche. Results Initially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%. Conclusions These outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan.

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Enhanced Disease Identification Model for Tea Plant Using Deep Learning

Cited by 10 publications

References 23 publications

Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network

Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Deep migration learning-based recognition of diseases and insect pests in Yunnan tea under complex environments

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