Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning

Frontiers in Applied Mathematics and Statistics, Journal Year: 2024, Volume and Issue: 9

Published: Jan. 3, 2024

In the era of advancing artificial intelligence (AI), its application in agriculture has become increasingly pivotal. This study explores integration AI for discriminative classification corn diseases, addressing need efficient agricultural practices. Leveraging a comprehensive dataset, encompasses 21,662 images categorized into four classes: Broken, Discolored, Silk cut, and Pure. The proposed model, an enhanced iteration MobileNetV2, strategically incorporates additional layers—Average Pooling, Flatten, Dense, Dropout, softmax—augmenting feature extraction capabilities. Model tuning techniques, including data augmentation, adaptive learning rate, model checkpointing, dropout, transfer learning, fortify model's efficiency. Results showcase exceptional performance, achieving accuracy ~96% across classes. Precision, recall, F1-score metrics underscore proficiency, with precision values ranging from 0.949 to 0.975 recall 0.957 0.963. comparative analysis state-of-the-art (SOTA) models, outshines counterparts terms precision, F1-score, accuracy. Notably, base architecture, achieves highest values, affirming superiority accurately classifying instances within disease dataset. not only contributes growing body applications but also presents novel effective classification. robust combined competitive edge against SOTA positions it as promising solution crop management.

Language: Английский

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Enhancing soybean classification with modified inception model: A transfer learning approach

Emirates Journal of Food and Agriculture, Journal Year: 2024, Volume and Issue: 36, P. 1 - 9

Published: April 18, 2024

The impact of deep learning (DL) is substantial across numerous domains, particularly in agriculture. Within this context, our study focuses on the classification problematic soybean seeds. dataset employed encompasses five distinct classes, totaling 5513 images. Our model, based InceptionV3 architecture, undergoes modification with addition supplementary layers to enhance efficiency and performance. Techniques such as transfer learning, adaptive rate adjustment (to 0.001), model checkpointing are integrated optimize accuracy. During initial evaluation, achieved 88.07% accuracy training 86.67% validation. Subsequent implementation tuning strategies significantly improves Augmenting architecture additional layers, including Average Pooling, Flatten, Dense, Dropout, Softmax, plays a pivotal role enhancing Evaluation metrics, precision, recall, F1-score, underscore model’s effectiveness. Precision ranges from 0.9706 1.0000, while recall values demonstrate high capture all classes. reflecting balance between precision exhibits remarkable performance ranging 0.9851 1.0000. Comparative analysis existing studies reveals competitive 98.73% by proposed model. While variations exist specific purposes datasets among studies, showcases promising seed classification, contributing advancements agricultural technology for crop health assessment management.

Language: Английский

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Artificial intelligence in groundwater management: Innovations, challenges, and future prospects

International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(1), P. 502 - 512

Published: Jan. 26, 2024

The integration of Artificial Intelligence (AI) in groundwater management is a transformative stage, characterized by innovation and challenges. This research paper explores the multilayered application AI this field, dividing its contributions, addressing associated challenges, revealing prospects future potential. AI-driven innovations are designed to revolutionize management, providing precise predictive modeling, real-time monitoring, data integration. However, these face challenges such as interpretability issues, specialized technical expertise requirements, limited quality quantity for effective model performance. In future, holds significant promise management. Advanced models can yield improved predictions behavior, identify vulnerable areas prone pollution depletion, prompt proactive interventions, foster collaborative platforms among scientists, policymakers, local communities. Collaborative driven offer potential synergistic engagement communities, collectively guiding resource Embracing AI's while remains pivotal sustainable resilient practices. By embracing landscape will continue evolve.

Language: Английский

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Exploring Transfer Learning for Enhanced Seed Classification: Pre-trained Xception Model

Lecture notes in civil engineering, Journal Year: 2024, Volume and Issue: unknown, P. 137 - 147

Published: Jan. 1, 2024

Language: Английский

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A systematic review of deep learning applications for rice disease diagnosis: current trends and future directions

Frontiers in Computer Science, Journal Year: 2024, Volume and Issue: 6

Published: Sept. 11, 2024

Background The occurrence of diseases in rice leaves presents a substantial challenge to farmers on global scale, hence jeopardizing the food security an expanding population. timely identification and prevention these are utmost importance order mitigate their impact. Methods present study conducts comprehensive evaluation contemporary literature pertaining diseases, covering period from 2008 2023. process selecting pertinent studies followed guidelines outlined by Kitchenham, which ultimately led inclusion 69 for purpose review. It is worth mentioning that significant portion research endeavours have been directed towards studying such as brown spot, blast, bacterial blight. primary performance parameter emerged was accuracy. Researchers strongly advocated combination hybrid deep learning machine methodologies improve rates recognition leaf diseases. Results collection scholarly investigations focused detection characterization affecting leaves, with specific emphasis prominence accuracy measure highlights precision diagnosis Furthermore, efficacy employing combine techniques exemplified enhancing capacities leaves. Conclusion This systematic review provides insight into conducted scholars field disease during previous decade. text underscores significance calls implementation augment identification, presenting possible resolutions obstacles presented agricultural hazards.

Language: Английский

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A Novel Hybrid Approach Based on CNN for Corn Diseases Detection

Optimization in agriculture., Journal Year: 2024, Volume and Issue: 1, P. 94 - 104

Published: May 24, 2024

Corn is one of the most economically important crops globally, significantly improving food security and agricultural productivity. However, corn plants face various foliar diseases, which can reduce crop Accurate early detection diseases an imperative task for maintaining health ensuring security. In this study, we propose a novel approach disease by integrating DenseNet121, powerful convolutional neural network (CNN) architecture, with deep (DNN) classifier. This hybrid model, called DenseNetDNN, combines feature extraction capabilities DenseNet121 classification DNN, aiming to enhance accuracy. The proposed model’s performance compared against four widely used pre-trained CNN models: ResNet50, MobileNet, EfficientNetB0, Xception. All models are evaluated using accuracy, precision, recall. Additionally, study employs GradeCam, advanced grading system, automate standardize evaluation model. Results demonstrate that DenseNetDNN model outperformed all other in terms identifying diseases; it achieves superior accuracy 96.1%, precession 0.952, recall 0.958. demonstrates efficiency advancing detection. research contributes development automated solutions monitoring, implications management practices global

Language: Английский

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Development of deep learning models for climate change within python framework

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 159 - 185

Published: Jan. 1, 2025

Language: Английский

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Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(2), P. 32 - 32

Published: Jan. 24, 2025

This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. dataset contains 6334 images UAV (unmanned vehicles) satellite then used train Deep Learning (DL) models identify disasters. Four different Machine (ML) used: convolutional neural network (CNN), DenseNet201, VGG16, ResNet50. These ML trained on our so that their performance could be compared. DenseNet201 chosen for optimization. All four performed well. ResNet50 achieved the highest testing accuracies of 99.37% 99.21%, respectively. project demonstrates potential AI address environmental challenges, such as climate change-related study’s approach is novel creating a new dataset, optimizing an model, cross-validating, presenting one DL detection. Three categories (Flooded, Desert, Neither). Our relates Environmental Sustainability. Drone emergency response would practical application project.

Language: Английский

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An Improved Deep Learning Framework Based on Multi-Scale Convolutional Architecture for Road Crack Detection

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 109 - 121

Published: Jan. 1, 2025

Language: Английский

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Next-generation approach to skin disorder prediction employing hybrid deep transfer learning

Frontiers in Big Data, Journal Year: 2025, Volume and Issue: 8

Published: Feb. 19, 2025

Skin diseases significantly impact individuals' health and mental wellbeing. However, their classification remains challenging due to complex lesion characteristics, overlapping symptoms, limited annotated datasets. Traditional convolutional neural networks (CNNs) often struggle with generalization, leading suboptimal performance. To address these challenges, this study proposes a Hybrid Deep Transfer Learning Method (HDTLM) that integrates DenseNet121 EfficientNetB0 for improved skin disease prediction. The proposed hybrid model leverages DenseNet121's dense connectivity capturing intricate patterns EfficientNetB0's computational efficiency scalability. A dataset comprising 19 conditions 19,171 images was used training validation. evaluated using multiple performance metrics, including accuracy, precision, recall, F1-score. Additionally, comparative analysis conducted against state-of-the-art models such as DenseNet121, EfficientNetB0, VGG19, MobileNetV2, AlexNet. HDTLM achieved accuracy of 98.18% validation 97.57%. It consistently outperformed baseline models, achieving precision 0.95, recall 0.96, F1-score an overall 98.18%. results demonstrate the model's superior ability generalize across diverse categories. findings underscore effectiveness in enhancing classification, particularly scenarios significant domain shifts labeled data. By integrating complementary strengths provides robust scalable solution automated dermatological diagnostics.

Language: Английский

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