Carrot grading system using computer vision feature parameters and a cascaded graph convolutional neural network

Bukumira, Milos; Antonijevic, Milos; Jovanovic, Dijana; Živković, Miodrag; Mladenovic, Djordje; Kunjadić, Goran

doi:10.1117/1.jei.31.6.061815

Cited by 25 publications

(7 citation statements)

References 31 publications

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“…In a series of diverse studies employing computer vision and machine learning, the research presents advance-ments and specific outcomes across various domains. The authors of [ 26 ] introduce an optimized cascaded graph convolutional neural network for carrot grading by extracting different features, yielding enhanced automation in agricultural processes. The authors of [ 27 ] focus on early disease detection in tomato plants utilizes machine learning techniques, including Support Vector Machine (SVM), Convolutional Neural Network (CNN), and K-Nearest Neighbor (K-NN), are employed for classification, achieving notable accuracy rates of 88% (SVM), 97% (K-NN), and 99.6% (CNN).…”

Section: Basic Preliminaries and Literature Workmentioning

confidence: 99%

GNViT- An enhanced image-based groundnut pest classification using Vision Transformer (ViT) model

P.,

2024

PLoS ONE

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Crop losses caused by diseases and pests present substantial challenges to global agriculture, with groundnut crops particularly vulnerable to their detrimental effects. This study introduces the Groundnut Vision Transformer (GNViT) model, a novel approach that harnesses a pre-trained Vision Transformer (ViT) on the ImageNet dataset. The primary goal is to detect and classify various pests affecting groundnut crops. Rigorous training and evaluation were conducted using a comprehensive dataset from IP102, encompassing pests such as Thrips, Aphids, Armyworms, and Wireworms. The GNViT model’s effectiveness was assessed using reliability metrics, including the F1-score, recall, and overall accuracy. Data augmentation with GNViT resulted in a significant increase in training accuracy, achieving 99.52%. Comparative analysis highlighted the GNViT model’s superior performance, particularly in accuracy, compared to state-of-the-art methodologies. These findings underscore the potential of deep learning models, such as GNViT, in providing reliable pest classification solutions for groundnut crops. The deployment of advanced technological solutions brings us closer to the overarching goal of reducing crop losses and enhancing global food security for the growing population.

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Section: Basic Preliminaries and Literature Workmentioning

confidence: 99%

GNViT- An enhanced image-based groundnut pest classification using Vision Transformer (ViT) model

P.,

2024

PLoS ONE

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“…Population-based algorithms have lately become a usual choice for addressing different real-world problems. These algorithms are useful for many fields, such as prediction of COVID-19 cases (Zivkovic et al 2021a, b), organizing on demand computational services (Bacanin et al 2019;Bezdan et al 2020a, b;Zivkovic et al 2021c), optimizing wireless sensors and IoT (Zivkovic et al 2020(Zivkovic et al , 2021d, feature selection (Bezdan et al 2021;Bacanin et al 2023a), processing and classifying medical images (Bezdan et al 2020c;Zivkovic et al 2022), addressing global optimization problems (Strumberger et al 2019;Preuss et al 2011), identifying credit card fraud (Jovanovic et al 2022b;Petrovic et al 2022), monitoring and forecasting air pollution (Bacanin et al 2022a;Jovanovic et al 2023a), detecting network and computer system intrusions (Bacanin et al 2022b;Stankovic et al 2022), predicting power generation and energy load (Bacanin et al 2023b;Stoean et al 2023), and optimizing different ML models (Salb et al 2022;Milosevic et al 2021;Gajic et al 2021;Bacanin et al 2022c, d;Jovanovic et al 2022aJovanovic et al , 2023bBukumira et al 2022).…”

Section: Metaheuristics Optimizationmentioning

confidence: 99%

Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting

Pavlov-Kagadejev,

Jovanovic,

Bacanin

et al. 2024

Artif Intell Rev

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Power supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance production and demand to avoid losses. This study proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis. Finally, this study also provides interpretations of the best-performing models on both observed datasets, accompanied by the analysis of the importance and impact each feature has on the predictions.

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“…NP-hard complexity with real world problems is common and hence the application of these algorithms is diverse. Some notable examples are artificial neural network optimization [7][8][9][10]12,14,15,19,21,26,32,36,48,53,54], wireless sensors networks (WSNs) [4,11,13,52,65,75], cryptocurrency trends estimations [44,49], finally the COVID-19 global epidemic-associated applications [22,25,64,66,[69][70][71]73], computer-conducted MRI classification and sickness determination [17,20,24,33,55], cloud-edge and fog computing and task scheduling [3,5,6,16,23,50,67], and lastly securing networks through intrusion detection [2,31,43,62,…”

Section: Swarm Intelligence Applications In Machine Learningmentioning

confidence: 99%

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

Petrovic¹,

Antonijevic²,

Strumberger³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Artificial intelligence and internet of things (IoT) fields have contributed to the flourishment of the industry 4.0 concept. The main benefits include the improvements in terms of device communication, productivity, and efficiency. Nevertheless, there is a downside concerning the security of these systems. The amount of devices and their diversity prove a security risk. Due to this intrusion detection systems are paramount. This paper proposes a novel framework exploiting extreme gradient boosting machine learning model which is optimized by a modified version of the multi-verse optimizer metaheuristic. The UNSW-NB intrusion dataset was used for experimental purposes on which the other cutting-edge techniques were tested and compared. The results provide the proof of improvement as the proposed method outperformed all other overall metaheuristic performances. Furthermore, the units for truthfulness and polarity for the case have been established as a standard evaluation system. True and false positives exist alongside the same negative counterparts. The results provided by these metrics have been visualized and used for further comparison proving the superiority of the performance of the proposed solution.

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Carrot grading system using computer vision feature parameters and a cascaded graph convolutional neural network

Cited by 25 publications

References 31 publications

GNViT- An enhanced image-based groundnut pest classification using Vision Transformer (ViT) model

GNViT- An enhanced image-based groundnut pest classification using Vision Transformer (ViT) model

Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

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