Root anatomy based on root cross-section image analysis with deep learning

Wang, Chaoxin; Li, Xukun; Caragea, Doina; Bheemanahalli, Raju

doi:10.1016/j.compag.2020.105549

Cited by 11 publications

(6 citation statements)

References 65 publications

(95 reference statements)

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“…In addition, roots often grow along the path of least resistance, so it has been suggested that roots grow along windows, possibly skewing aberrance and distribution data. Because of the difficulties with nondestructive techniques, a combined approach that involves both the removal of plants from their growing substrate and AI techniques that analyze root system images is growing in use, where images of root systems acquired by shovelomics are analyzed with the aid of computers using machine learning (ML) technologies These techniques have been applied to several major agricultural crops and plant types in studies concerning roots and RSA, such as alfalfa [ 14 , 17 , 33 ], apple ( Malus domestica ) [ 34 ], cotton ( Gossypium herbaceum L.) [ 35 ], maize [ 36 , 37 ], millet ( Setaria italica ) [ 38 ], rice ( Oryza sativa L.) [ 39 , 40 ], and soybean [ 16 ].…”

Section: Root Acquisitionmentioning

confidence: 99%

“…Automated approaches to identifying anatomical traits in microscopy root cross-sections by investigating rice roots were also focused on by Wang et al. [ 40 ]. They used and compared the Faster R-CNN and Mask R-CNN ML models producing an IoU of 0.95 for both root and stele objects, showing that the Faster R-CNN models can accurately detect and predict anatomical objects in images while concurrently producing more accurate results with smaller amounts training data than the competing model.…”

Section: Root Image Analysis Using Ai and MLmentioning

confidence: 99%

See 1 more Smart Citation

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Weihs,

Heuschele,

Tang

et al. 2024

Plant Phenomics

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Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the “Second Green Revolution”. To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

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Section: Root Acquisitionmentioning

confidence: 99%

Section: Root Image Analysis Using Ai and MLmentioning

confidence: 99%

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Weihs,

Heuschele,

Tang

et al. 2024

Plant Phenomics

View full text Add to dashboard Cite

show abstract

“…There are an equal number of output neurons in the last fully connected layer compared to the targeted classes. This layer uses a function called “softmax.” Several pretrained CNN architectures are currently available, including the VGG-16 [ 46 ]. The VGG-16 network provides outstanding efficiency regarding the ImageNet competition, in which the network is trained with countless images in one thousand categories.…”

Section: System Implementationmentioning

confidence: 99%

“…Moreover, the VGG-16 was utilized with proper results in our previous work, that is, the Faster R-CNN paper, giving us the impetus to reuse it in the current study. This VGG-16 has thirteen layers of convolutions + reLU, five layers of pooling, and three layers of fully connected layers [ 46 ] (see Figure 8 ).…”

Section: System Implementationmentioning

confidence: 99%

Deep Hybrid Multimodal Biometric Recognition System Based on Features‐Level Deep Fusion of Five Biometric Traits

Safavipour

Doostari

2023

Computational Intelligence and Neuroscience

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The need for information security and the adoption of the relevant regulations is becoming an overwhelming demand worldwide. As an efficient solution, hybrid multimodal biometric systems utilize fusion to combine multiple biometric traits and sources with improving recognition accuracy, higher security assurance, and to cope with the limitations of the uni-biometric system. In this paper, three strategies for dealing with a feature-level deep fusion of five biometric traits (face, both irises, and two fingerprints) derived from three sources of evidence are proposed and compared. In the first two proposed methodologies, each feature vector is mapped from the feature space into the reproducing kernel Hilbert space (RKHS) separately by selecting the appropriate reproducing kernel. In this higher space, where the result is the conversion of nonlinear relations to linear ones, dimensionality reduction algorithms (KPCA, KLDA) and quaternion-based algorithms (KQPCA, KQPCA) are used for the fusion of the feature vectors. In the third methodology, the fusion of feature spaces based on deep learning is administered by combining feature vectors in in-depth and fully connected layers. The experimental results on 6 databases in the proposed hybrid multibiometric system clearly show the multimodal template obtained from the deep fusion of feature spaces; while being secure against spoof attacks and making the system robust, they can use the low dimensionality of the fused vector to increase the accuracy of a hybrid multimodal biometric system to 100%, showing a significant improvement compared with uni-biometric and other multimodal systems.

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“…ResNet is the first classification network that surpasses human accuracy in classification tasks ( Russakovsky et al, 2015 ). At present, the ResNet-based classification model has been widely used in plant image research, such as plant age judgment ( Yue et al, 2021 ), flowering pattern analysis ( Jiang et al, 2020 ), and root image analysis ( Wang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification

Wang

Tian²,

Zhang³

et al. 2022

Front. Plant Sci.

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The traditional Chinese large-flowered chrysanthemum is one of the cultivar groups of chrysanthemum (Chrysanthemum × morifolium Ramat.) with great morphological variation based on many cultivars. Some experts have established several large-flowered chrysanthemum classification systems by using the method of comparative morphology. However, for many cultivars, accurate recognition and classification are still a problem. Combined with the comparative morphological traits of selected samples, we proposed a multi-information model based on deep learning to recognize and classify large-flowered chrysanthemum. In this study, we collected the images of 213 large-flowered chrysanthemum cultivars in two consecutive years, 2018 and 2019. Based on the 2018 dataset, we constructed a multi-information classification model using non-pre-trained ResNet18 as the backbone network. The model achieves 70.62% top-5 test accuracy for the 2019 dataset. We explored the ability of image features to represent the characteristics of large-flowered chrysanthemum. The affinity propagation (AP) clustering shows that the features are sufficient to discriminate flower colors. The principal component analysis (PCA) shows the petal type has a better interpretation than the flower type. The training sample processing, model training scheme, and learning rate adjustment method affected the convergence and generalization of the model. The non-pre-trained model overcomes the problem of focusing on texture by ignoring colors with the ImageNet pre-trained model. These results lay a foundation for the automated recognition and classification of large-flowered chrysanthemum cultivars based on image classification.

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Root anatomy based on root cross-section image analysis with deep learning

Cited by 11 publications

References 65 publications

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Deep Hybrid Multimodal Biometric Recognition System Based on Features‐Level Deep Fusion of Five Biometric Traits

Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification

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