Advancing Green AI: Efficient and Accurate Lightweight CNNs for Rice Leaf Disease Identification

• URL: http://arxiv.org/abs/2408.01752v1
• Date: Sat, 3 Aug 2024 11:16:00 GMT
• Title: Advancing Green AI: Efficient and Accurate Lightweight CNNs for Rice Leaf Disease Identification
• Authors: Khairun Saddami, Yudha Nurdin, Mutia Zahramita, Muhammad Shahreeza Safiruz,
• Abstract summary: Rice plays a vital role as a primary food source for over half of the world's population. In this study, we explore three mobile-compatible CNN architectures for rice leaf disease classification. The best performance was achieved by the EfficientNet-B0 model with an accuracy of 99.8%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Rice plays a vital role as a primary food source for over half of the world's population, and its production is critical for global food security. Nevertheless, rice cultivation is frequently affected by various diseases that can severely decrease yield and quality. Therefore, early and accurate detection of rice diseases is necessary to prevent their spread and minimize crop losses. In this research, we explore three mobile-compatible CNN architectures, namely ShuffleNet, MobileNetV2, and EfficientNet-B0, for rice leaf disease classification. These models are selected due to their compatibility with mobile devices, as they demand less computational power and memory compared to other CNN models. To enhance the performance of the three models, we added two fully connected layers separated by a dropout layer. We used early stop creation to prevent the model from being overfiting. The results of the study showed that the best performance was achieved by the EfficientNet-B0 model with an accuracy of 99.8%. Meanwhile, MobileNetV2 and ShuffleNet only achieved accuracies of 84.21% and 66.51%, respectively. This study shows that EfficientNet-B0 when combined with the proposed layer and early stop, can produce a high-accuracy model. Keywords: rice leaf detection; green AI; smart agriculture; EfficientNet;

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers [0.0]
  This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3.
  arXiv  Detail & Related papers  (2024-09-17T05:32:01Z)
• PanicleNeRF: low-cost, high-precision in-field phenotypingof rice panicles with smartphone [4.441945709704536]
  PanicleNeRF is a novel method that enables high-precision and low-cost reconstruction of rice panicle models in the field using smartphone. Result: PanicleNeRF effectively addressed the 2D image segmentation task, achieving a mean F1 Score of 86.9% and a mean Intersection over Union (IoU) of 79.8%. This method provides a low-cost solution for high-cost phenotyping of rice panicles, accelerating the efficiency of rice breeding.
  arXiv  Detail & Related papers  (2024-08-04T15:01:16Z)
• Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification [0.520707246175575]
  This study uses different models like MobileNetV2, ResNet152V2, Xecption, Custom CNN. A Convolutional Neural Network (CNN) based on the ResNet152V2 architecture is constructed and evaluated in this work. The results highlight its potential for real-world applications in insect classification and entomology studies.
  arXiv  Detail & Related papers  (2024-06-11T20:52:42Z)
• PotatoPestNet: A CTInceptionV3-RS-Based Neural Network for Accurate Identification of Potato Pests [0.0]
  We propose an efficient PotatoPestNet AI-based automatic potato pest identification system. We leveraged the power of transfer learning by employing five customized, pre-trained transfer learning models. Among the models, the Customized Tuned Inception V3 model, optimized through random search, demonstrated outstanding performance.
  arXiv  Detail & Related papers  (2023-05-27T17:38:16Z)
• Rice paddy disease classifications using CNNs [0.0]
  Rice is a staple food in the world, and yet huge percentages of crop yields are lost each year to disease. To combat this problem, people have been searching for ways to automate disease diagnosis. Here, we extend on previous modelling work by analysing how disease-classification accuracy is sensitive to both model architecture and computer vision techniques.
  arXiv  Detail & Related papers  (2023-03-15T07:31:06Z)
• Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding [82.46024259137823]
  We propose a cross-model comparative loss for a broad range of tasks. We demonstrate the universal effectiveness of comparative loss through extensive experiments on 14 datasets from 3 distinct NLU tasks.
  arXiv  Detail & Related papers  (2023-01-10T03:04:27Z)
• Publishing Efficient On-device Models Increases Adversarial Vulnerability [58.6975494957865]
  In this paper, we study the security considerations of publishing on-device variants of large-scale models. We first show that an adversary can exploit on-device models to make attacking the large models easier. We then show that the vulnerability increases as the similarity between a full-scale and its efficient model increase.
  arXiv  Detail & Related papers  (2022-12-28T05:05:58Z)
• Network Augmentation for Tiny Deep Learning [73.57192520534585]
  We introduce Network Augmentation (NetAug), a new training method for improving the performance of tiny neural networks. We demonstrate the effectiveness of NetAug on image classification and object detection.
  arXiv  Detail & Related papers  (2021-10-17T18:48:41Z)
• Effective Model Sparsification by Scheduled Grow-and-Prune Methods [73.03533268740605]
  We propose a novel scheduled grow-and-prune (GaP) methodology without pre-training the dense models. Experiments have shown that such models can match or beat the quality of highly optimized dense models at 80% sparsity on a variety of tasks.
  arXiv  Detail & Related papers  (2021-06-18T01:03:13Z)
• Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference [119.19779637025444]
  Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) This paper studies multi-exit networks associated with input-adaptive inference, showing their strong promise in achieving a "sweet point" in cooptimizing model accuracy, robustness and efficiency.
  arXiv  Detail & Related papers  (2020-02-24T00:40:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.