Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks

Jahanbakhshi, Ahmad; Momeny, Mohammad; Mahmoudi, Majid; Zhang, Yudong

doi:10.1016/j.scienta.2019.109133

Cited by 118 publications

(56 citation statements)

References 22 publications

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“…Qualitative assessment of postharvest quality is similar to plant stress phenotyping, with its unique emphasis on fruit rather than plant. Most studies have investigated the use of CNNs to detect defects for fruits such as cucumbers [ 129 ], apples [ 130 , 131 ], dates [ 132 ], pears [ 133 ], blueberries [ 134 ], lemons [ 135 ], and peaches [ 136 ]. These studies reported detection accuracies from 87.85% to 98.6%, which were usually 10% to 20% higher than conventional ML methods, demonstrating the advantages of using CNNs for qualitative assessment of postharvest quality.…”

Section: Cnn-based Analytical Approaches For Image-based Plant Phementioning

confidence: 99%

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

2020

Plant Phenomics

251

180

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Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.

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Section: Cnn-based Analytical Approaches For Image-based Plant Phementioning

confidence: 99%

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

2020

Plant Phenomics

251

180

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“…In the standard convolutional neural networks, pooling is an essential component after each convolution layer, which was applied to reduce the size of feature maps (FMs). SP was shown to give better performance than average pooling and max pooling in recent publications [18][19][20][21]. Recently, strided convolution (SC) is commonly used, which also can shrink the FMs [22,23].…”

Section: Improvement I: N-conv Stochastic Poolingmentioning

confidence: 99%

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

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“…[191] Smart visual sensing systems in agriculture visually detect and identify defects and anomalies in fresh produces and plants. [192,194,[223][224][225] Ferentinos [192] created a system that identifies Black Sigatoka diseases found in banana leaves. The system uses the images of healthy and diseased banana leaves to create a smart model based on a CNN using the VGG framework.…”

Section: Precision Agriculturementioning

confidence: 99%

“…Detecting and segmentation of land and crop types (water, grassland, bare land, winter wheat, soybeans, maize, sunflowers, and sugar beet) [193] Photographs CNN Classification Identifying damaged sour lemons [194] Photographs…”

Section: Precision Agriculturementioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

124

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Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

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Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks

Cited by 118 publications

References 22 publications

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Machine Learning‐Enabled Smart Sensor Systems

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