EfficientNet: A Low-bandwidth IoT Image Sensor Framework for Cassava Leaf Disease Classification

Chen, Chih‐Cheng; Ba, Ju Yan; Li, Tie Jun; Chan, Christopher Chun Ki; Wang, Kun Ching; Liu, Zhen

doi:10.18494/sam.2021.3526

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…The researchers achieved an impressive overall accuracy rate of 93% by applying a dataset collected from various fields in Tanzania. Chen et al. (2021) introduced a novel CNN architecture named Efficientnet in their study.…”

Section: Related Workmentioning

confidence: 99%

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images

Mazumder,

Mridha,

Alfarhood

et al. 2024

Front. Plant Sci.

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Leaf diseases are a global threat to crop production and food preservation. Detecting these diseases is crucial for effective management. We introduce LeafDoc-Net, a robust, lightweight transfer-learning architecture for accurately detecting leaf diseases across multiple plant species, even with limited image data. Our approach concatenates two pre-trained image classification deep learning-based models, DenseNet121 and MobileNetV2. We enhance DenseNet121 with an attention-based transition mechanism and global average pooling layers, while MobileNetV2 benefits from adding an attention module and global average pooling layers. We deepen the architecture with extra-dense layers featuring swish activation and batch normalization layers, resulting in a more robust and accurate model for diagnosing leaf-related plant diseases. LeafDoc-Net is evaluated on two distinct datasets, focused on cassava and wheat leaf diseases, demonstrating superior performance compared to existing models in accuracy, precision, recall, and AUC metrics. To gain deeper insights into the model’s performance, we utilize Grad-CAM++.

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Section: Related Workmentioning

confidence: 99%

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images

Mazumder,

Mridha,

Alfarhood

et al. 2024

Front. Plant Sci.

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“…To test the model's anti-interference ability, synthetic noise can be added to the sample and then removed to restore the image to a more realistic quality. In order to validate the proposed rice disease classification model, multiple models, including the ICAI-V4 model as well as classical and recent models, were tested such as MobileNetV3 (Tarek et al, 2022) [42], EfficientNet (Chen et al, 2021) [43], DenseNet121 (Huang et al, 2017) [44], and EfficitentNetV2 (Sunil et al, 2022) [45]. This approach allowed us to establish the reliability and accuracy of our model in comparison to others.…”

Section: Comparison With Other Networkmentioning

confidence: 99%

An Accurate Classification of Rice Diseases Based on ICAI-V4

Zeng

Gong

Zhou

et al. 2023

Plants

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Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network’s ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network’s feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method’s strong performance and feasibility for rice disease classification in real-life scenarios.

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“…For example, the divide-and-conquer approach has been used in several fields: oil prices ( Rădulescu et al, 2020 ; Wang et al, 2018 ) foreign currency exchange rate ( Jin et al, 2021 ; Lin, Chiu & Lin, 2012 ; Wang & Luo, 2021 ), stock market trend ( Cheng & Wei, 2014 ; Na & Kim, 2021 ; Stasiak, 2020 ; Wang & Luo, 2021 ), wind speed ( Hu et al, 2021 ; Wang et al, 2014 ; Xie et al, 2021 ), electronics sales ( Chen & Lu, 2021 ; Lu & Shao, 2012 ), healthcare ( Aileni, Rodica & Valderrama, 2016 ; Dwivedi et al, 2019 ; Singh, Dwivedi & Srivastava, 2020 ), and tourism market ( Chen, Lai & Yeh, 2012 ; Guerra-Montenegro et al, 2021 ; Tang et al, 2021 ). The hybrid EMD combined with the artificial neural network(ANN) method was applied to predict the first, second, and third steps moving forward wind speed time series ( Chen et al, 2021 ; Hu et al, 2021 ; Liu et al, 2012 ; Liu, Hara & Kita, 2021 ). Several predicting powers from low to high frequency and short-term to long-term trend elements were observed for analysis of the accuracy of EMD forecasting combined with ANN in the Baltic Exchange Dry Index ( Gavriilidis et al, 2021 ; Zeng & Qu, 2014 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical mode decomposition using deep learning model for financial market forecasting

Jin

Jin²,

Chen

2022

PeerJ Computer Science

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Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.

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EfficientNet: A Low-bandwidth IoT Image Sensor Framework for Cassava Leaf Disease Classification

Cited by 13 publications

References 13 publications

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images

An Accurate Classification of Rice Diseases Based on ICAI-V4

Empirical mode decomposition using deep learning model for financial market forecasting

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