Testing the Suitability of Automated Machine Learning for Weeds Identification

Espejo-García, Borja; Malounas, Ioannis; Vali, Eleanna; Fountas, Spyros

doi:10.3390/ai2010004

Cited by 15 publications

(14 citation statements)

References 30 publications

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“…Since both datasets in this study were multi-class problems with a class imbalance, in comparison, we calculated the micro-averaging F1 grade, which is a preferable aggregation method over the macro-average. To conduct statistical comparisons, we used robust, paired non-parametric statistical tests: the Li test [17] and the Espejo signed-rank test [1]. These tests were employed to prevent drawing too optimistic conclusions.…”

Section: F Analysismentioning

confidence: 99%

Performance Evaluation Of The Deep Learning System For Weed

Mosslah,

Mahdi,

Al-Barzinji

2023

Preprint

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Numerous approaches based on machine learning have emerged in recent years to enhance crop protection efficiency. One example is the utilisation of deep neural networks to differentiate between various weed types in actual event scenarios. Nevertheless, these methods often need substantial input from experts who work iteratively to design robust deep learning systems. To simplify such processes and conserve resources, researchers have explored a fresh method known as automated deep learning. the Our technology's recognition of weeds through the use of machine learning was evaluated using plant seedlings and weed collections from plants as a dataset to address the issue of weed recognition. The study compared various configurations, including plant segmentation, using a collection of classifiers in place of Softmax, and training with datasets that contain noise. The findings indicated ensuring performance, with F1-scores of 93.1% and 90.2% based on the dataset utilised. These results align with AutoML-linked studies while falling short of manually fine-tuned deep-learning-based systems created by human specialists. To conclude, exploring the potential of combining manual expert work and automated deep learning could be a promising direction for enhancing efficiency in plant defence.

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Section: F Analysismentioning

confidence: 99%

Performance Evaluation Of The Deep Learning System For Weed

Mosslah,

Mahdi,

Al-Barzinji

2023

Preprint

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“…Machine learning workflows or workflow sub-modules are often automated, i.e. automated classification [35], Automated Machine Learning (AutoML) in healthcare [36], aviation [37,38], biology [39] and agriculture [40,41].…”

Section: Of 36mentioning

confidence: 99%

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Kampezidou,

Tikayat Ray,

Bhat

et al. 2023

Preprint

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This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.

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“…Kang et al [50] produced a unique neural network to classify butterflies based on the shape of their wings, with an accuracy of 80.3%. Bouzalmat et al [51] used PCA and SVM to analyze two feature collections, the ATT and IFD datasets, which achieved 90.24 % and 66.8% accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

Orban

Vadivel³

et al. 2022

Electronics

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In comparison to the competitors, engineers must provide quick, low-cost, and dependable solutions. The advancement of intelligence generated by machines and its application in almost every field has created a need to reduce the human role in image processing while also making time and labor profit. Lepidopterology is the discipline of entomology dedicated to the scientific analysis of caterpillars and the three butterfly superfamilies. Students studying lepidopterology must generally capture butterflies with nets and dissect them to discover the insect’s family types and shape. This research work aims to assist science students in correctly recognizing butterflies without harming the insects during their analysis. This paper discusses transfer-learning-based neural network models to identify butterfly species. The datasets are collected from the Kaggle website, which contains 10,035 images of 75 different species of butterflies. From the available dataset, 15 unusual species were selected, including various butterfly orientations, photography angles, butterfly lengths, occlusion, and backdrop complexity. When we analyzed the dataset, we found an imbalanced class distribution among the 15 identified classes, leading to overfitting. The proposed system performs data augmentation to prevent data scarcity and reduce overfitting. The augmented dataset is also used to improve the accuracy of the data models. This research work utilizes transfer learning based on various convolutional neural network architectures such as VGG16, VGG19, MobileNet, Xception, ResNet50, and InceptionV3 to classify the butterfly species into various categories. All the proposed models are evaluated using precision, recall, F-Measure, and accuracy. The investigation findings reveal that the InceptionV3 architecture provides an accuracy of 94.66%, superior to all other architectures.

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Testing the Suitability of Automated Machine Learning for Weeds Identification

Cited by 15 publications

References 30 publications

Performance Evaluation Of The Deep Learning System For Weed

Performance Evaluation Of The Deep Learning System For Weed

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

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