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Tomato Anomalies Detection in Greenhouse Scenarios Based on YOLO-Dense

Xuewei Wang, Jun Liu
2021 Frontiers in Plant Science  
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

Chiagoziem C. Ukwuoma, Qin Zhiguang, Md Belal Bin Heyat, Liaqat Ali, Zahra Almaspoor, Happy N. Monday, Dost Muhammad Khan
2022 Mathematical Problems in Engineering  
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

Zhiyu Deng, Jinming Zhang, Junya Li, Xiujun Zhang
2021 Frontiers in Genetics  
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

Dhritiman Saha, Manickavasagan Annamalai
2021 Current Research in Food Science  
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

Sapan Naik, Bankim Patel
2017 International Journal of Computer Applications  
International 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

José Naranjo-Torres, Marco Mora, Ruber Hernández-García, Ricardo J. Barrientos, Claudio Fredes, Andres Valenzuela
2020 Applied Sciences  
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

Wenli Zhang, Yuxin Liu, Kaizhen Chen, Huibin Li, Yulin Duan, Wenbin Wu, Yun Shi, Wei Guo
2021 Frontiers in Plant Science  
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

Wen-Hao Su, Jiajing Zhang, Ce Yang, Rae Page, Tamas Szinyei, Cory D. Hirsch, Brian J. Steffenson
2020 Remote Sensing  
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

Mavridou, Vrochidou, Papakostas, Pachidis, Kaburlasos
2019 Journal of Imaging  
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

Yunchao Tang, Mingyou Chen, Chenglin Wang, Lufeng Luo, Jinhui Li, Guoping Lian, Xiangjun Zou
2020 Frontiers in Plant Science  
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

Vishal A. Meshram, Kailas Patil, Sahadeo D. Ramteke
2021 Ingénierie des Systèmes d'Information  
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

Diclehan Karakaya, Oguzhan Ulucan, Mehmet Turkan
2019 International Journal of Automation and Computing  
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 (, 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

Xiaoyu Li, Yuefeng Du, Lin Yao, Jun Wu, Lei Liu
2021 Agriculture  
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

Gert Kootstra, Xin Wang, Pieter M. Blok, Jochen Hemming, Eldert van Henten
2021 Current Robotics Reports  
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

Marina Marchisio, Tiziana Margaria, Matteo Sacchet
2020 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)  
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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