Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks

Chen, Jian; Fan, Yangyang; Wang, Tao; Zhang, Chu; Qiu, Zhengjun; He, Yong

doi:10.3390/agronomy8080129

Cited by 40 publications

(21 citation statements)

References 24 publications

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“…In experiments, 102 aphid nymph images in practical experimental environments were collected and analyzed to detect the number of aphid nymphs on each image for the evaluation of the proposed method. The results showed that the mean count error and F1-score of the proposed method were 1.2 and 0.9606, respectively [41].…”

Section: Dl-based Image Recognition Techniques For Agronomy Applicationsmentioning

confidence: 98%

“…Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) "Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks," by Chen et al [41]; (2) "Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques," by Alvarez et al [42]; and (3) "Development of a mushroom growth measurement system applying deep learning for image recognition," by Lu et al [43].…”

Section: Dl-based Image Recognition Techniques For Agronomy Applicationsmentioning

confidence: 99%

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Deep Learning Techniques for Agronomy Applications

2019

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This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al.

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Section: Dl-based Image Recognition Techniques For Agronomy Applicationsmentioning

confidence: 98%

Section: Dl-based Image Recognition Techniques For Agronomy Applicationsmentioning

confidence: 99%

Deep Learning Techniques for Agronomy Applications

2019

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“…Although the concept of neighborhood density is introduced to initialize the clustering center in the literature and it does show higher segmentation accuracy than the above two, however, FCM is effective only for the recognition of mature tomatoes in close shots similar to [14]. As shown in image 4,7,8,10,17,19, and 20, all tomatoes have very high visual saliency in the images. In our database, the existence of green tomatoes and leaves under a busy background in different growth cycles is common, which brings great interference to the initial clustering center in the color space.…”

Section: Analysis Of Comparative Experimentsmentioning

confidence: 99%

“…The vision information of greenhouse crops is of great significance for plant phenotype analysis, which has been applied to provide guidance to improve crop cultivation in many studies [1][2][3][4][5]. It is obvious that an automatic, accurate, and high-throughput imaging processing technique-with high sensitivity for plant phenotypic research-not only lays a visual foundation for analyzing the effect on the environment in production, but is also conducive to comprehensive research of internal and external factors on the physical and biochemical characteristics of plants.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera

Cao

2019

Agronomy

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It has long been a great concern in deep learning that we lack massive data for high-precision training sets, especially in the agriculture field. Plants in images captured in greenhouses, from a distance or up close, not only have various morphological structures but also can have a busy background, leading to huge challenges in labeling and segmentation. This article proposes an unsupervised statistical algorithm SAI-LDA (self-adaptive iterative latent Dirichlet allocation) to segment greenhouse tomato images from a field surveillance camera automatically, borrowing the language model LDA. Hierarchical wavelet features with an overlapping grid word document design and a modified density-based method quick-shift are adopted, respectively, according to different kinds of images, which are classified by specific proportions between fruits, leaves, and the background. We also utilize the feature correlation between several layers of the image to make further optimization through three rounds of iteration of LDA, with updated documents to achieve finer segmentation. Experiment results show that our method can automatically label the organs of the greenhouse plant under complex circumstances, fast and precisely, overcoming the difficulty of inferior real-time image quality caused by a surveillance camera, and thus obtain large amounts of valuable training sets.

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“…(d) Segmentação utilizando o método 2. (e) Segmentação usando o método proposto Fonte: Adaptado de(CHEN et al, 2018)…”

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Método automático para detecção de nematóides em lavoura cafeeira usando imagens aéreas.

Oliveira¹

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Agradeço imensamente a Deus que me ampara a cada passo desta jornada. Minha gratidão ao meu orientador Prof o. Jefferson Rodrigo de Souza pela oportunidade de trabalhar e me aprofundar nesta temática, pelos ensinamentos, incentivo e disponibilidade. Meu sincero agradecimento a minha coorientadora Prof a. Elaine Ribeiro Faria pelos ensinamentos, paciência e incentivo, além dos feedbacks que foram fundamentais para o meu crescimento. Obrigado aos professores e funcionários da PPGCO, em especial aos professores Fabiano Dorça, Gina Maira Barbosa, Márcia Aparecida e André Backes e também ao Erisvaldo e a Sônia pelo grande auxílio nas questões administrativas. Minha profunda gratidão a minha família Ana Carolina, Claudemira e Ricardo pelo carinho, cuidado e amor. Em especial, a minha mãe Claudemira pelo empenho e apoio incondicional em toda minha carreira acadêmica. Obrigado aos meus amigos, em especial ao Antônio Martins pelas valiosas conversas e conselhos, além de toda a amizade. Agradeço à CNPq pelo apoio financeiro durante a realização do trabalho. A todos, o meu muito obrigado! "A última medida de um homem não é onde ele se posiciona em momentos de conforto e conveniência, mas onde ele se posiciona em momentos de desafio e controvérsia."

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Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks

Cited by 40 publications

References 24 publications

Deep Learning Techniques for Agronomy Applications

Deep Learning Techniques for Agronomy Applications

Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera

Método automático para detecção de nematóides em lavoura cafeeira usando imagens aéreas.

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