Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Zhang, Jingyao; Rao, Yuan; Chao, Man Chien; Jiang, Zhaohui; Li, Shaowen

doi:10.1177/15501477211007407

Cited by 42 publications

(11 citation statements)

References 42 publications

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“…The author [32] claims the work is a generalized approach for detecting every disease while no evidence is provided. The author [33] has used two-stage segmentation to extract lesions from leafspot. Two-stage segmentation was implemented using GrabCut with the SVM method.…”

Section: Related Workmentioning

confidence: 99%

Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

2022

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Agriculture is essential to the growth of every country. Cotton and other major crops fall into the cash crops. Cotton is affected by most of the diseases that cause significant crop damage. Many diseases affect yield through the leaf. Detecting disease early saves crop from further damage. Cotton is susceptible to several diseases, including leaf spot, target spot, bacterial blight, nutrient deficiency, powdery mildew, leaf curl, etc. Accurate disease identification is important for taking effective measures. Deep learning in the identification of plant disease plays an important role. The proposed model based on meta Deep Learning is used to identify several cotton leaf diseases accurately. We gathered cotton leaf images from the field for this study. The dataset contains 2385 images of healthy and diseased leaves. The size of the dataset was increased with the help of the data augmentation approach. The dataset was trained on Custom CNN, VGG16 Transfer Learning, ResNet50, and our proposed model: the meta deep learn leaf disease identification model. A meta learning technique has been proposed and implemented to provide a good accuracy and generalization. The proposed model has outperformed the Cotton Dataset with an accuracy of 98.53%.

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Section: Related Workmentioning

confidence: 99%

Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

2022

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“…Biotic and abiotic stressors (Dhaka et al, 2021), such as microorganisms (e.g., virus, bacteria, fungi), insects, and environmental factors, negatively affect plant growth and health conditions, leading to the development of plant diseases or disorders and eventually low yield and quality of plant products (Zhang et al, 2021b). Imaging technologies (e.g., RGB, multi-/-hyper-spectral, fluorescence, thermal) offer a non-invasive and objective means for characterization and diagnosis of plant health conditions (e.g., plant disease detection) (Thomas et al, 2018;.…”

Section: Plant Healthmentioning

confidence: 99%

“…The GAN-synthesized data yielded an improvement of 5.2% in classification accuracy as compared to an 0.8% increase with traditional augmentation. The AR-GAN approach was later adopted by Zhang et al (2021b) for improving the identification of cucumber leaf diseases. Liu et al (2020) presented a GAN model with a channel decreasing generator to synthesize 4-class grape leaf images, reporting 98.7% classification accuracy, which is about 3% and 2% better than the models without and with only basic image augmentation, respectively.…”

Section: Plant Healthmentioning

confidence: 99%

Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review

Olaniyi¹,

Chen²,

Lu³

et al. 2022

Preprint

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In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and ground-truthed image datasets, however, are often difficult to obtain to fuel the development of advanced, high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agricultural images, data augmentation plays a crucial role in boosting model performance while reducing manual efforts for data preparation, by algorithmically expanding training datasets. Beyond traditional data augmentation techniques, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representations and generate highly realistic samples. Since 2017, there has been a growth of research into GANs for image augmentation or synthesis in agriculture for improved model performance. This paper presents an overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture (https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest inspection of fruits. Challenges and opportunities of GANs are discussed for future research.

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“…Furthermore, in the past few years, computer vision, especially by employing deep learning, has made remarkable progress. As highlighted by Zhang et al [ 56 ], who focused on identifying cucumber leaf diseases by utilizing deep learning, due to the complex environmental background, it is beneficial to eliminate background before model training. Moreover, accurate image classifiers for disease diagnosis need a large dataset of both healthy and diseased plant images.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

273

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Cited by 42 publications

References 42 publications

Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review

Machine Learning in Agriculture: A Comprehensive Updated Review

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