Comparison of Detection With Transfer Learning Architecture Restnet18, Restnet50, Restnet101 on Corn Leaf Disease

Hindarto, Djarot

doi:10.20527/jtiulm.v8i2.174

Cited by 5 publications

(3 citation statements)

References 9 publications

(9 reference statements)

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“…Understanding the strengths and weaknesses of each model will help create more effective and efficient weed management strategies. Deep learning [11] and agricultural applications promise breakthroughs in weed identification. This research should lead to better and more efficient agricultural practices and new weed detection systems that support sustainable and productive agriculture.…”

Section: Figure 1 Soybean Cropsmentioning

confidence: 99%

Model Accuracy Analysis: Comparing Weed Detection in Soybean Crops with EfficientNet-B0, B1, and B2

Hindarto

2023

jtik

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Comparison of EfficientNet models B0, B1, and B2 for soybean weed detection. This study examines how well these models distinguish weeds from soybeans. Precision, recall, and F1 score metrics evaluate each model through extensive testing and experiments. The method trains these models on datasets with images of soybean fields overrun by various weeds. Results show subtle differences in model accuracy, showing what they're good at and what they can't do to find soybean weeds. EfficientNet-B2 detects and classifies weeds better than B1 and B0 in soybean fields. It could improve weed management systems' accuracy and reliability. This comparison helps us choose the best weed-finding model to improve soybean farming and prevent crop yield losses. Training accuracy measurements on EfficientNet models B0 = 100%, B1 = 100%, and B2 = 100%.

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Section: Figure 1 Soybean Cropsmentioning

confidence: 99%

Model Accuracy Analysis: Comparing Weed Detection in Soybean Crops with EfficientNet-B0, B1, and B2

Hindarto

2023

jtik

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“…A variety of neural network architectures, Convolutional Neural Networks (CNN) [6][7], have demonstrated remarkable efficacy in image recognition tasks, including Flower Recognition. This architecture is crucial to the implementation of Flower Recognition with CNN to identify abstract and complex visual patterns in flower images.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Implementation of Flower Recognition using Convolutional Neural Networks

Hindarto,

Amalia

2023

ijsecs

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The recognition of flowers holds significant importance within the realms of ecological research, horticulture, and diverse technological applications. This study presents "Blossom Insight," an innovative methodology for flower identification that employs Convolutional Neural Networks within the Keras framework. This study aims to examine the necessity of precise and effective flower categorization, considering the extensive range of floral species. The methodology encompasses a rigorous procedure of data preprocessing, utilizing sophisticated techniques to augment the model's capacity to distinguish intricate characteristics of flowers. The crux of the study revolves around the amalgamation of a Convolutional Neural Network, a robust deep learning methodology, with Keras, a user-accessible open-source framework for machine learning. The integration of these components enables the development of a resilient flower recognition model that possesses the ability to acquire complex patterns and characteristics from input images. The training of the model encompasses exposure to a wide range of flower datasets, which enhances its ability to generalize across different species and environmental conditions effectively. The findings illustrate the effectiveness of "Blossom Insight" in attaining a notable level of precision in tasks related to the identification of flowers. The implementation not only contributes to the advancement of the field of computer vision but also offers a valuable resource for researchers, horticulturists, and enthusiasts seeking a comprehensive understanding and accurate identification of floral species. The development of "Blossom Insight" signifies a notable advancement in utilizing deep learning techniques to augment our understanding and admiration of the wide variety present in the realm of flowers.

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“…The utilization of CNN is expected to witness a surge in its application across diverse domains pertaining to the study of animal life, owing to the continuous advancements in technology. The objective of this research is to examine the effect of varying the quantity of data on the ResNet50V2 [6] model pertaining to five distinct animal species: cats, cows, dogs, elephants, and pandas. The objective of this study is to analyze the influence of an unequal distribution of data among different classes on the efficacy of deep learning models in classification assignments.…”

Section: Introductionmentioning

confidence: 99%

Use ResNet50V2 Deep Learning Model to Classify Five Animal Species

Hindarto

2023

jtik

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This study employs the ResNet50V2 Deep Learning model for the purpose of classifying five distinct animal species. To gain insights into the model's proficiency in visual recognition, we conducted training and testing procedures on a dataset comprising diverse images of animal species. The utilization of ResNet50V2 in this classification task is intended to discern visual distinctions among these species by leveraging the distinctive characteristics present in the input images. A meticulous and comprehensive training procedure was undertaken on the model, employing fine-tuning techniques to adjust its internal representation in order to accommodate diverse animal characteristics. The experimental findings illustrate the model's capacity to effectively discern and categorize various animal species with a notable degree of precision, thereby presenting encouraging outcomes for the potential utilization of this model in broader animal classification contexts. This study emphasizes the significant potential of employing Deep Learning models, specifically ResNet50V2, for the purpose of comprehending and identifying diverse fauna through visual cues.

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Comparison of Detection With Transfer Learning Architecture Restnet18, Restnet50, Restnet101 on Corn Leaf Disease

Cited by 5 publications

References 9 publications

Model Accuracy Analysis: Comparing Weed Detection in Soybean Crops with EfficientNet-B0, B1, and B2

Model Accuracy Analysis: Comparing Weed Detection in Soybean Crops with EfficientNet-B0, B1, and B2

Implementation of Flower Recognition using Convolutional Neural Networks

Use ResNet50V2 Deep Learning Model to Classify Five Animal Species

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