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Tomato Anomalies Detection in Greenhouse Scenarios Based on YOLO-Dense
2021
Frontiers in Plant Science
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By adding a dense connection module in the network architecture, the network inference speed of the proposed model can be effectively improved. ...
Automated identification of tomato anomalies is still a challenging task because of its small size and complex background. ...
Deep convolutional neural networks (DCNNs), which have emerged in recent years, provide a new idea for tomato anomaly object detection. ...
doi:10.3389/fpls.2021.634103
pmid:33897724
pmcid:PMC8063041
fatcat:qadylhpxsbfulpeqivs2vdgsly
Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques
2022
Mathematical Problems in Engineering
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This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. ...
In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model's implementation, and the challenges of using deep learning to detect and categorize fruits ...
Guragai for the useful discussion of the study. ...
doi:10.1155/2022/9210947
fatcat:tm4ivc3pbrc5npthxs7oicquwa
Application of Deep Learning in Plant–Microbiota Association Analysis
2021
Frontiers in Genetics
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Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis. ...
The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. ...
DEEP LEARNING MODELS FOR PREDICTION The DL models with high computational efficiencies include convolutional neural networks (CNN), recurrent neural networks (RNN), and graph convolutional neural networks ...
doi:10.3389/fgene.2021.697090
pmid:34691142
pmcid:PMC8531731
fatcat:poo5bdc2ynetzafqoydz6psc2e
Machine learning techniques for analysis of hyperspectral images to determine quality of food products: a review
2021
Current Research in Food Science
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Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. ...
However, the field of deep learning is relatively new and need further research for its full utilization. ...
Qiu et al. (2018) reported the use of one padding layer in the convolutional neural network. ...
doi:10.1016/j.crfs.2021.01.002
pmid:33659896
pmcid:PMC7890297
fatcat:atiplsxm2bdgxnovnmlo3lyecq
Machine Vision based Fruit Classification and Grading - A Review
2017
International Journal of Computer Applications
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Deep Learning / Convolutional Neural Networks For the image classification and recognition tasks, development in deep learning and convolution neural network (CNN) have been proved to be ...
Weed identification based on K-means feature learning combined with convolutional neural network is performed in [71] . To train CNN, we simply need to provide input image and associated label. ...
doi:10.5120/ijca2017914937
fatcat:aoy6mq7zrrdltogwbg27o3dicy
A Review of Convolutional Neural Network Applied to Fruit Image Processing
2020
Applied Sciences
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Convolutional Neural Networks (CNN) is the main DL architecture for image classification. ...
We observe that in the last two years (2019–2020), the use of CNN for fruit recognition has greatly increased obtaining excellent results, either by using new models or with pre-trained networks for transfer ...
[57] proposed a fruit recognition algorithm based on Deep Convolution Neural Network (DCNN). They used a fruit image database with 15 different categories comprising of 44,406 images. ...
doi:10.3390/app10103443
fatcat:7j4ytsmjvvdz3pyiffilpkwpgu
Lightweight Fruit-Detection Algorithm for Edge Computing Applications
2021
Frontiers in Plant Science
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The algorithm is based on Light-CSPNet as the backbone network, an improved feature-extraction module, a down-sampling method, and a feature-fusion module, and it ensures real-time detection on edge devices ...
In recent years, deep-learning-based fruit-detection technology has exhibited excellent performance in modern horticulture research. ...
The target information focused on deep and shallow networks will be different in solving machine-vision tasks with convolutional neural networks. ...
doi:10.3389/fpls.2021.740936
pmid:34721466
pmcid:PMC8548576
fatcat:sfx3qjzinvcapn5zvu7sccuuom
Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision
2020
Remote Sensing
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In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. ...
The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. ...
Mask region convolutional neural network (Mask-RCNN) is a machine vision based deep structural learning algorithm to directly solve the problem of instance segmentation [49] . ...
doi:10.3390/rs13010026
fatcat:imh4frczfnaj5poqgxz42naznq
Machine vision Systems in Precision Agriculture for Crop Farming
2019
Journal of Imaging
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Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. ...
Acknowledgments: We would like to thank the anonymous reviewers for their constructive comments that allowed us to further improve this work. ...
Lastly, the classification of citrus fruit from the background is implemented using AlexNet convolutional neural network [44] . ...
doi:10.3390/jimaging5120089
pmid:34460603
pmcid:PMC8321169
fatcat:d7fzfos7jjbkhg4tbo54eb6mxy
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review
2020
Frontiers in Plant Science
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The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. ...
The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. ...
Kushtrim et al. (2019) proposed that using deep convolution neural network architecture based on singlestage detectors to realize real-time detection of fruits in trees was adopted to improve the detection ...
doi:10.3389/fpls.2020.00510
pmid:32508853
pmcid:PMC7250149
fatcat:zaxmjf6cjjff5du7f2n7wunmvy
MNet: A Framework to Reduce Fruit Image Misclassification
2021
Ingénierie des Systèmes d'Information
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To achieve the same, the most popular technique used to build a classification model is "Transfer Learning", in which the weights of pretrained models are used in a new model to solve different but related ...
This paper proposed a novel framework called "MNet: Merged Net" which not only improves the accuracy, but also addresses the misclassification problem. ...
[17] developed two improved model, namely, GoogLeNet and Cifar10, to identify 9 types of maize leaf diseases. ...
doi:10.18280/isi.260203
fatcat:dt6b2jwry5cwdgafc4yqevmble
Electronic Nose and Its Applications: A Survey
2019
International Journal of Automation and Computing
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In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. ...
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution ...
Some additional deep learning based examples include convolutional recurrent neural networks (consisting in both CNNs and recurrent neural networks (RNNs)) to perform fast gas recognition [114] and ( ...
doi:10.1007/s11633-019-1212-9
fatcat:thwuyithund4ledlgxjxsmdrty
Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm
2021
Agriculture
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At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. ...
accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using ...
The technology of deep learning represented by convolutional neural networks has developed rapidly. Perugachi-Diaz et al. ...
doi:10.3390/agriculture11121238
fatcat:pj4fffafhnavpoh4nnfaer6pbi
Selective Harvesting Robotics: Current Research, Trends, and Future Directions
2021
Current Robotics Reports
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For the image analysis, two convolutional neural networks (CNNs) were used. ...
[18] , for instance, showed the successful application of a deep neural network to detect different types of fruits, including apple, avocado, mango, and orange, with F1-scores above 0.93 and an average ...
doi:10.1007/s43154-020-00034-1
fatcat:z25iray765hgbesvatags3x5j4
Automatic Formative Assessment in Computer Science: Guidance to Model-Driven Design
2020
2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)
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Leaf Disease Identification Using Improved Deep Convolutional Neural Networks 1267 Bin Liu (Northwest A&F University), Zefeng Ding (Northwest A&F University), Yun Zhang (Sun Yat-sen University), Dongjian ...
Deep Learning 1285 Victor Parque (Waseda University) and Tomoyuki Miyashita (Waseda University) DADA An Approach to Improving the Effectiveness of Data Augmentation for Deep Neural Networks 1290 Seunghui ...
doi:10.1109/compsac48688.2020.00035
dblp:conf/compsac/MarchisioMS20
fatcat:nudzvwviyrcklectabhuyranq4
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