An artificial neural network approach for non-invasive estimation of essential oil content and composition through considering drying processing factors: A case study in Mentha aquatica

Taheri‐Garavand, Amin; Mumivand, Hasan; Fanourakis, Dimitrios; Fatahi, Soodabeh; Taghipour, Shirin

doi:10.1016/j.indcrop.2021.113985

Cited by 27 publications

(25 citation statements)

References 31 publications

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“…This feature will be more abstract rather than simple geometric features. On a commercial scale, evidently, a capital investment is initially required for adopting the employed approach [52]. Nevertheless, the wide-ranging large-scale commercial applications can provide high returns through considerable improvements in process enhancement and cost reduction.…”

Section: Discussionmentioning

confidence: 99%

Meta-Learning for Few-Shot Plant Disease Detection

Chen

Cui

2021

Foods

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Plant diseases can harm crop growth, and the crop production has a deep impact on food. Although the existing works adopt Convolutional Neural Networks (CNNs) to detect plant diseases such as Apple Scab and Squash Powdery mildew, those methods have limitations as they rely on a large amount of manually labeled data. Collecting enough labeled data is not often the case in practice because: plant pathogens are variable and farm environments make collecting data difficulty. Methods based on deep learning suffer from low accuracy and confidence when facing few-shot samples. In this paper, we propose local feature matching conditional neural adaptive processes (LFM-CNAPS) based on meta-learning that aims at detecting plant diseases of unseen categories with only a few annotated examples, and visualize input regions that are ‘important’ for predictions. To train our network, we contribute Miniplantdisease-Dataset that contains 26 plant species and 60 plant diseases. Comprehensive experiments demonstrate that our proposed LFM-CNAPS method outperforms the existing methods.

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Section: Discussionmentioning

confidence: 99%

Meta-Learning for Few-Shot Plant Disease Detection

Chen

Cui

2021

Foods

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“…The algorithm proposed in this paper is able to detect plant diseases early in their development and take timely control measures, which helps to reduce production costs. At the commercial scale, it is clear that capital investment in the adopted method is initially required [40]. However, broad-scale commercial applications can provide high returns through significant improvements in process improvements and cost reductions.…”

Section: Discussionmentioning

confidence: 99%

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Wang

Yang

et al. 2021

Information

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In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

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“…On a commercial scale, evidently, a capital investment is initially required for adopting the employed approach ( Taheri-Garavand et al, 2021a Industrial Crop Prod 171, 113985). Nevertheless, the wide-ranging large-scale commercial applications can provide high returns through considerable improvements in process enhancement and cost reduction.…”

Section: Discussionmentioning

confidence: 99%

Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning

Fan¹,

Wang

Yang

et al. 2021

Front. Plant Sci.

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Citrus Huanglongbing (HLB), also named citrus greening disease, occurs worldwide and is known as a citrus cancer without an effective treatment. The symptoms of HLB are similar to those of nutritional deficiency or other disease. The methods based on single-source information, such as RGB images or hyperspectral data, are not able to achieve great detection performance. In this study, a multi-modal feature fusion network, combining a RGB image network and hyperspectral band extraction network, was proposed to recognize HLB from four categories (HLB, suspected HLB, Zn-deficient, and healthy). Three contributions including a dimension-reduction scheme for hyperspectral data based on a soft attention mechanism, a feature fusion proposal based on a bilinear fusion method, and auxiliary classifiers to extract more useful information are introduced in this manuscript. The multi-modal feature fusion network can effectively classify the above four types of citrus leaves and is better than single-modal classifiers. In experiments, the highest accuracy of multi-modal network recognition was 97.89% when the amount of data was not very abundant (1,325 images of the four aforementioned types and 1,325 pieces of hyperspectral data), while the single-modal network with RGB images only achieved 87.98% recognition and the single-modal network using hyperspectral information only 89%. Results show that the proposed multi-modal network implementing the concept of multi-source information fusion provides a better way to detect citrus HLB and citrus deficiency.

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An artificial neural network approach for non-invasive estimation of essential oil content and composition through considering drying processing factors: A case study in Mentha aquatica

Cited by 27 publications

References 31 publications

Meta-Learning for Few-Shot Plant Disease Detection

Meta-Learning for Few-Shot Plant Disease Detection

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning

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