PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network

Liang, Qiaokang; Shao, Xiang; Hu, Yucheng; Coppola, Gianmarc; Zhang, Dan; Sun, Wei

doi:10.1016/j.compag.2019.01.034

Cited by 176 publications

(65 citation statements)

References 23 publications

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“…But, they only analyzed one disease of crop, which was not extensive and was not suitable for the classification of disease levels. Liang et al 38 used the ResNet network to join shuffle units 39 to classify nine kinds of plants for health and disease. He divided the disease into general and serious, the results show that the accuracy on the validation set can reach 91%.…”

Section: Introductionmentioning

confidence: 99%

SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

Zhao

Chen

et al. 2021

Concurrency and Computation

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Summary Early and accurate assessment of plant disease severity is key to preventing disease attack. Traditional detection methods rely on manual vision to distinguish between types of disease infection, but this is time consuming, laborious and inaccurate. To address this problem, this paper proposes a deep learning‐based attentional network model (SEV‐Net) for plant disease severity identification and classification. The network embeds the improved channel and spatial attention module into the residual block of ResNet. The proposed attention module reduces the redundancy of information between channels and focuses on the most information‐rich regions of the feature map. In this experiment, SEV‐Net achieved an accuracy of 97.59% and 95.37% for multiple and single plant (Tomato) disease severity classification, which was better than existing attentional networks (SE‐Net and CBAM). Moreover, the combination of visualization techniques showed that SEV‐Net was adept at distinguishing small variations between plant diseases, proving the feasibility and effectiveness of the network. Furthermore, we have also designed and developed an Android application for real‐time classification of plant disease severity. The system deploys the SEV‐Net network model, which has higher classification accuracy and faster recognition speed.

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Section: Introductionmentioning

confidence: 99%

SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

Zhao

Chen

et al. 2021

Concurrency and Computation

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“…e designed framework also included an unsupervised method to extract high-resolution feature maps that isolate visual symptoms used to measure stress severity. Liang et al [24] proposed a multitasking system, called PD 2 SE-Net, able to recognize plant species, to diagnose diseases, and to estimate the severity of diseases.…”

Section: Introductionmentioning

confidence: 99%

Using Multioutput Learning to Diagnose Plant Disease and Stress Severity

Fenu

2021

Complexity

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Early diagnosis of leaf diseases is a fundamental tool in precision agriculture, thanks to its high correlation with food safety and environmental sustainability. It is proven that plant diseases are responsible for serious economic losses every year. The aim of this work is to study an efficient network capable of assisting farmers in recognizing pear leaf symptoms and providing targeted information for rational use of pesticides. The proposed model consists of a multioutput system based on convolutional neural networks. The deep learning approach considers five pretrained CNN architectures, namely, VGG-16, VGG-19, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0, as feature extractors to classify three diseases and six severity levels. Computational experiments are conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. The results obtained confirm the robustness of the proposed model in automatically extracting the discriminating features of diseased leaves by adopting the multitasking learning paradigm.

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“…The results of this study using the PlantVillage dataset [ 14 ] indicated that DenseNet121 [ 15 ] outperformed all other models and achieved an accuracy score of

was proposed in [ 16 ], based on ResNet50 [ 17 ], to identify plant species, diagnose diseases, and estimate disease severity. This approach achieved an accuracy score of

for plant disease classification.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data

Afifi

Alhumam

Abdelwahab

2020

Plants

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Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images. These approaches usually require the collection and annotation of many images, which is difficult and costly process especially in the case of new or rare diseases. Therefore, in this study, we developed and evaluated several methods for identifying plant diseases with little data. Convolutional Neural Networks (CNNs) are used due to their superior ability to transfer learning. Three CNN architectures (ResNet18, ResNet34, and ResNet50) were used to build two baseline models, a Triplet network and a deep adversarial Metric Learning (DAML) approach. These approaches were trained from a large source domain dataset and then tuned to identify new diseases from few images, ranging from 5 to 50 images per disease. The proposed approaches were also evaluated in the case of identifying the disease and plant species together or only if the disease was identified, regardless of the affected plant. The evaluation results demonstrated that a baseline model trained with a large set of source field images can be adapted to classify new diseases from a small number of images. It can also take advantage of the availability of a larger number of images. In addition, by comparing it with metric learning methods, we found that baseline model has better transferability when the source domain images differ from the target domain images significantly or are captured in different conditions. It achieved an accuracy of 99% when the shift from source domain to target domain was small and 81% when that shift was large and outperformed all other competitive approaches.

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PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network

Cited by 176 publications

References 23 publications

SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

Using Multioutput Learning to Diagnose Plant Disease and Stress Severity

Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data

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