Aphids Detection on Lemons Leaf Image Using Convolutional Neural Networks

Parraga-Alava, Jorge; Alcivar-Cevallos, Roberth; Riascos, Jaime A.; Becerra, Miguel A.

doi:10.1007/978-3-030-59194-6_2

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…The data can be used to train, test, and validate computational models related to image classification on plant disease studies. In this sense, we already have evidence from a previous work [6], where convolutional neural networks (CNNs) were used to board a binary classification problem related to lemon leaves with aphid presence. The quality of LeLePhid was evidenced by allowing the model to achieve average rates between 81% and 97% of correct aphid classification.…”

Section: User Notesmentioning

confidence: 94%

LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

Parraga-Alava

Alcivar-Cevallos

Carrillo

et al. 2021

Data

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Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea. They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation severity according to the percentage of the affected area on it. Images were collected manually in real-world conditions in a lemon plant field in Junín, Manabí, Ecuador, during the winter, by using a smartphone camera. The dataset is called LeLePhid: lemon (Le) leaf (Le) image dataset for aphid (Phid) detection and infestation severity. The data can facilitate evaluating models for image segmentation, detection, and classification problems related to plant disease recognition.

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Section: User Notesmentioning

confidence: 94%

LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

Parraga-Alava

Alcivar-Cevallos

Carrillo

et al. 2021

Data

Self Cite

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“…CNN is one of the magnificent neural networks in the realm of deep learning technology [16][17][18][19][20]. This method was first put forward by Lecun et al [21].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes

Gao,

Zhang,

Yang

et al. 2023

Electronics

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The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts image analysis technology based on artificial intelligence and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and effectively deal with problems such as the small number of fresco images on the emigrants’ buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.

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“…The presence of aphids on images taken from lemon tree plants in the field, which feature natural background variability and changes in lighting conditions, was solved by Parraga-Alava et al (2021).To do so, 150 images of plants were taken as training data (70 images of healthy plants, 80 images of plants infested with aphids) and a transfer learning approach with a VGG-16 network architecture was used. The authors report a classification of infested and uninfested plants with rates between 81% and 97% on lemon tree plant images of the test data set.…”

Section: Deep Learningmentioning

confidence: 99%

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring

Batz¹,

Will

Thiel³

et al. 2023

Front. Plant Sci.

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Insect monitoring has gained global public attention in recent years in the context of insect decline and biodiversity loss. Monitoring methods that can collect samples over a long period of time and independently of human influences are of particular importance. While these passive collection methods, e.g. suction traps, provide standardized and comparable data sets, the time required to analyze the large number of samples and trapped specimens is high. Another challenge is the necessary high level of taxonomic expertise required for accurate specimen processing. These factors create a bottleneck in specimen processing. In this context, machine learning, image recognition and artificial intelligence have emerged as promising tools to address the shortcomings of manual identification and quantification in the analysis of such trap catches. Aphids are important agricultural pests that pose a significant risk to several important crops and cause high economic losses through feeding damage and transmission of plant viruses. It has been shown that long-term monitoring of migrating aphids using suction traps can be used to make, adjust and improve predictions of their abundance so that the risk of plant viruses spreading through aphids can be more accurately predicted. With the increasing demand for alternatives to conventional pesticide use in crop protection, the need for predictive models is growing, e.g. as a basis for resistance development and as a measure for resistance management. In this context, advancing climate change has a strong influence on the total abundance of migrating aphids as well as on the peak occurrences of aphids within a year. Using aphids as a model organism, we demonstrate the possibilities of systematic monitoring of insect pests and the potential of future technical developments in the subsequent automated identification of individuals through to the use of case data for intelligent forecasting models. Using aphids as an example, we show the potential for systematic monitoring of insect pests through technical developments in the automated identification of individuals from static images (i.e. advances in image recognition software). We discuss the potential applications with regard to the automatic processing of insect case data and the development of intelligent prediction models.

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Aphids Detection on Lemons Leaf Image Using Convolutional Neural Networks

Cited by 7 publications

References 13 publications

LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

The Application of ResNet-34 Model Integrating Transfer Learning in the Recognition and Classification of Overseas Chinese Frescoes

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring

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