Classification of Grain Discoloration via Transfer Learning and Convolutional Neural Networks

Duong-Trung, Nghia; Quach, Luyl-Da; Nguyen, Minh-Hoang; Nguyen, Chi-Ngon

doi:10.1145/3310986.3310997

Cited by 17 publications

(8 citation statements)

References 15 publications

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“…In recent year, numerous works based on CNN have been proposed for the task of rice leaf disease (RLD) classification [5,19,12] and TL [2,20,10]. Our previous work in 2021 is also based on CNN [14].…”

Section: Leaf Disease Detectionmentioning

confidence: 99%

HiT-LIDIA: A Framework for Rice Leaf Disease Classification using Ensemble and Hierarchical Transfer Learning

Putra

Trisnaningrum

Puspitasari

et al. 2022

LKJITI

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Rice is one of the global most critical harvests, and a great many people eat it as a staple eating routine. Different rice plant diseases harm, spread, and drastically reduce crop yields. In extreme situations, they may result in no grain harvest at all, posing a severe threat to food security. In this paper, to amplify the recognition ability for rice leaf disease (RLD) classification, we proposed hierarchical transfer learning (HTL) methods incorporating ensemble models containing two-step. In the first step, an ensemble combining MobileNet and DenseNet was addressed to tackle the diseased leaf problem. Consequently, DenseNet and XceptionNet were fused to identify three RLDs. Here, we compare our models with state-of-the-art deep learning models such as ResNet, DenseNet, InceptionNet, Xception, MobileNet, and EfficientNet. Our framework at top-notch with 89 % and 91 % for accuracy. In future works, RLD segmentation is suggested to pinpoint the illness and quantify the afflicted region.

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Section: Leaf Disease Detectionmentioning

confidence: 99%

HiT-LIDIA: A Framework for Rice Leaf Disease Classification using Ensemble and Hierarchical Transfer Learning

Putra

Trisnaningrum

Puspitasari

et al. 2022

LKJITI

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“…Within this work, the authors briefly present the CNN background that leads to the proposed architecture in Section V-B. Further details on CNN and its next-generation investigating transfer learning [23], [24], [25], [26], [27] leaves to interesting readers.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Maneuverable Autonomy of a Six-legged Walking Robot: Design and Implementation using Deep Neural Networks and Hexapod Locomotion

Huynh¹,

Duong-Trung²,

Nguyen³

et al. 2021

IJACSA

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Automatically real-time synthesizing behaviors for a six-legged walking robot pose several exciting challenges, which can be categorized into mechanics design, control software, and the combination of both. Due to the complexity of controlling and automation, numerous studies choose to gear their attention to a specific aspect of the whole challenge by either proposing valid and low-power assumption of mechanical parts or implementing software solutions upon sensorial capabilities and camera. Therefore, a complete solution associating both mechanical moving parts, hardware components, and software encouraging generalization should be adequately addressed. The architecture proposed in this article orchestrates (i) interlocutor face detection and recognition utilizing ensemble learning and convolutional neural networks, (ii) maneuverable automation of six-legged robot via hexapod locomotion, and (iii) deployment on a Raspberry Pi, that has not been previously reported in the literature. Not satisfying there, the authors even develop one step further by enabling real-time operation. We believe that our contributions ignite multi-research disciplines ranging from IoT, computer vision, machine learning, and robot autonomy.

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“…It develops a mechanism of knowledge transferability in one or more source tasks and uses it to improve the prediction capacity in a new task [18]- [21]. It is like the propagation of knowledge from a well-developed domain with a lot of learning data to a less-developed domain that is limited due to insufficient data [22,23]. The method allows machine learning models to be applied to new data drawn from distributions that are entirely different from the original data sources.…”

Section: Transfer Learningmentioning

confidence: 99%

Towards Classification of Shrimp Diseases Using Transferred Convolutional Neural Networks

Duong-Trung¹,

Quach²,

Ngôn³

2020

Adv. sci. technol. eng. syst. j.

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Vietnam is one of the top 5 largest shrimp exporters globally, and Mekong delta of Vietnam contributes more than 80% of total national production. Along with intensive farming and growing shrimp farming area, diseases are a severe threat to productivity and sustainable development. Timely response to emerging shrimp diseases is critical. Early detection and treatment practices could help mitigate disease outbreaks, leading to on-site diagnostics, instant services recommendation, and front-line treatments. The authors establish a contribution hub for data collection in the ethnographic fieldwork of Mekong delta. Several deep convolutional neural networks are trained by applying the transfer learning technique. We have investigated six common reported shrimp diseases. The classification accuracy is achieved of 90.02%, which is very useful in extremely non-standard images. Throughout the work, we raise the attention of shrimp experts, computer scientists, treatment agencies, and policymakers to develop preventive strategies against shrimp diseases.

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Classification of Grain Discoloration via Transfer Learning and Convolutional Neural Networks

Cited by 17 publications

References 15 publications

HiT-LIDIA: A Framework for Rice Leaf Disease Classification using Ensemble and Hierarchical Transfer Learning

HiT-LIDIA: A Framework for Rice Leaf Disease Classification using Ensemble and Hierarchical Transfer Learning

Maneuverable Autonomy of a Six-legged Walking Robot: Design and Implementation using Deep Neural Networks and Hexapod Locomotion

Towards Classification of Shrimp Diseases Using Transferred Convolutional Neural Networks

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