Rice Disease Detection Research Articles

Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification

To address the computational complexity and deployment challenges of traditional convolutional neural networks in rice disease identification, this paper proposes an efficient and lightweight model: Ghost Channel Spatial Attention ShuffleNet with Mish-ReLU Adaptive Activation Function (GCA-MiRaNet). Based on ShuffleNet V2, we effectively reduced the model’s parameter count by streamlining convolutional layers, decreasing stacking depth, and optimizing output channels. Additionally, the model incorporates the Ghost Module as a replacement for traditional 1 × 1 convolutions, further reducing computational overhead. Innovatively, we introduce a Channel Spatial Attention Mechanism (CSAM) that significantly enhances feature extraction and generalization aimed at rice disease detection. Through combining the advantages of Mish and ReLU, we designed the Mish-ReLU Adaptive Activation Function (MAAF), enhancing the model’s generalization capacity and convergence speed. Through transfer learning and ElasticNet regularization, the model’s accuracy has notably improved while effectively avoiding overfitting. Sufficient experimental results indicate that GCA-MiRaNet attains a precision of 94.76% on the rice disease dataset, with a 95.38% reduction in model parameters and a compact size of only 0.4 MB. Compared to traditional models such as ResNet50 and EfficientNet V2, GCA-MiRaNet demonstrates significant advantages in overall performance, especially on embedded devices. This model not only enables efficient and accurate real-time disease monitoring but also provides a viable solution for rice field protection drones and Internet of Things management systems, advancing the process of contemporary agricultural smart management.

Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet’s lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.

Comparing CNN Models for Rice Disease Detection: ResNet50, VGG16, and MobileNetV3-Small

The Oryza sativa (rice) plant is an important staple food source, especially in the Asian region. Rice production is often disrupted by diseases such as Brown Spot, Leaf Scald, Rice Blast, Rice Tungro, and Sheath Blight, which can reduce yield and crop quality. This research aims to classify rice plant diseases using a deep learning approach with Convolutional Neural Networks (CNN) architecture, namely ResNet50, VGG16, and MobileNetV3-Small. The dataset used is Rice Leaf Disease Classification which consists of 1305 images with five disease labels. The data is divided into training, validation, and testing sets with proportions of 70%, 15%, and 15%. The results showed that the MobileNetV3-Small model provided the best accuracy on the test data of 79%, while VGG16 achieved the validation accuracy of 78.84%. Based on these results, MobileNetV3-Small is considered the most superior model for rice disease classification. This research shows the great potential of applying deep learning in automatic rice disease detection.

Pyramid-YOLOv8: a detection algorithm for precise detection of rice leaf blast

Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods’ time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model’s computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.

Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images

Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images

Identification of rice disease under complex background based on PSOC-DRCNet

Rice is a crucial agricultural crop, yet it frequently suffers from various diseases, leading to decreased yields and, in severe cases, crop failure. Diseases significantly affect rice growth and yield, resulting in economic losses and food security challenges. The role of image recognition in identifying rice diseases is critical in agricultural production. It enables automated and efficient detection of rice diseases, which is essential for effective management, ensuring food security and sustainable agriculture. To address issues like background noise and edge blurring in rice disease image capture, as well as challenges in determining the optimal learning rate during the training of traditional rice disease recognition networks, a novel method based on PSOC-DRCNet is proposed for rice disease recognition.. First, tto solve the problem of background interference, Dual Mode Attention (DMA) is proposed to adaptively capture meaningful regions in rice disease images. Second, the Residual Adaptive Block(RAB) is proposed, which utilizes dimensional changes and channel attention to solve edge blur problems. Then, a Cross entropy and regularized mixed Loss function (CerLoss), is proposed to optimize the learning strategy of the model in the process of processing datasets and enhance the performance and generalization ability of the model to avoid overfitting problems. Ultimately, In response to the cumbersome problem of finding the optimal learning rate, we propose using Particle Swarm Optimization Chameleon (PSOC) to find the optimal learning rate and train the PSOC-DRCNet model on our custom dataset and compare it with other existing methods and the final average classification accuracy of PSOC-DRCNet is 93.88% with an F1 score of 0.940. We compare it with other existing methods. It is proved that the average classification accuracy of our model under hyper-parameter unification is 92.65% F1 score is 0.928. We validated the PSOC-DRCNet by conducting comparative analyses with other models and through generalization experiments and module effectiveness tests. Additionally, the practicality of PSOC-DRCNet was confirmed through its application in real-world scenarios. The methods proposed in this paper successfully enable the identification of various diseases in rice leaves, offering a practical solution for incorporating deep learning into the agricultural production process. Furthermore, these findings serve as a valuable reference for disease identification in other crops.

Augmented Rice Plant Disease Detection with Convolutional Neural Networks

The recognition and classification of rice plant diseases require an accurate system to generate classification data. Types of rice diseases can be identified in several ways, one of which is leaf characterization. One method that has high accuracy in identifying plant disease types is Convolutional Neural Networks (CNN). However, the rice disease data used has unbalanced data which affects the performance of the method. Therefore, the purpose of this research was to apply data augmentation to handle unbalanced rice disease data to improve the performance of the Convolutional Neural Network (CNN) method for rice disease type detection based on leaf images. The method used in this research is the CNN method for detecting rice disease types based on leaf images. The result of this research was the CNN method with 100 epochs able to produce an accuracy of 99.7% in detecting rice diseases based on leaf images with a division of 80% training data (2438 data) and 20% testing data (608 data). The conclusion is that the CNN method with the augmentation process can be used in rice disease detection because it has very high accuracy.

Advancements in Artificial Intelligence (AI) for enhanced insights and automation in rice agriculture: A systematic review

With the rising global demand for rice, improving production efficiency through advanced technologies like artificial intelligence (AI) is crucial. This systematic review gathered recent literatures on learning algorithm models applied to automate rice agriculture tasks. The objectives were to analyze the performance accuracy of different machine learning algorithms for rice classification and determine the most effective models. The 116 studies from 2016-2023 were screened and 70 were included. The algorithms were evaluated by weighted mean accuracy percentage across studies while maintaining consideration to sample sizes. The results showed the DenseNet121 deep convolutional neural network achieved the overall highest accuracy of 99.98%, also topping rice disease detection. For variety classification, Deep Neural Networks reached 99.95% accuracy by learning complex visual differences. Adaptive Neuro-Fuzzy Inference System led in grading quality of 98.6% by discerning grain features. Larger datasets improved accuracy indicating that the more training data has, it enhances model accuracy. The review demonstrates AI’s significant potential to automate essential aspects of rice production. Further research expanding standardized algorithm evaluations is recommended to strengthen the evidence-base and support integration of AI for intelligent, sustainable rice agriculture.

YOLO-CRD: A Lightweight Model for the Detection of Rice Diseases in Natural Environments

YOLO-CRD: A Lightweight Model for the Detection of Rice Diseases in Natural Environments

Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis

Early rice disease detection is vital in preventing damage to agricultural product output and quantity in the agricultural field. Manual observations of rice diseases are tedious, costly, and time-consuming, especially when classifying disease patterns and color while dealing with non-native diseases. Hence, image processing and Machine Learning (ML) techniques are used to detect rice disease early and within a relatively brief time period. This article aims to demonstrate the performance of different ML algorithms in rice disease detection through image processing. We critically examined different techniques, and the outcomes of previous research have been reviewed to compare the performance of rice disease classifications. Performance has been evaluated based on the criteria of feature extraction, clustering, segmentation, noise reduction, and level of accuracy of disease detection techniques. This paper also showcases various algorithms for different datasets in terms of training methods, image preprocessing with clustering and filtering criteria, and testing with reliable outcomes. Through this review, we provide valuable insights into the current state of ML-based approaches for the early detection of rice diseases, and assist future research and improvement. In addition, we discuss several challenges that must be overcome in order to achieve effective identification of rice diseases.

Research on rice disease recognition based on improved SPPFCSPC-G YOLOv5 network

Spatial Pyramid Pooling (SPP) is important in capturing remote contextual information for pixel-level prediction tasks in scene-resolved detection of rice diseases. In this paper, the detection objects of the rice disease dataset used in this paper have almost the same target size and do not need to be passed through different filters to obtain different receptive fields of view. Therefore, this paper proposed a new pooling structure, SPPFCSPC-G, which split the feature vector into 2 channels for processing. One channel was processed using grouped 1×1 Conv, while the other channel mainly used multiple filters with the same parallel structure (5×5 MaxPool). Additionally, multiple 1×1 and 3×3 grouped convolutions were concatenated in series in that branch (Group-Conv) to extract more complex features in rice. Finally, the 2 parts were connected (Concat) together, with each convolutional layer Conv divided into 4 groups as a way to reduce the amount of computation in the model. The project team incorporated SPPFCSPC-G into the Backbone of YOLOv5 and trained it on NVIDIA Tesla T4 (GPU). The experimental results showed that the performance of the method used in this paper improved, including Precision, Recall, mAP, and training speed, while reducing the size of computational parameters (Parameters), computational volume (GFLOPs), and model size (Param.). The project team carried out the trained YOLOv5 model on Intel Core i5 (CPU) for inference detection of rice leaves in real scenarios, and the experiments showed that both pre-inference and actual inference were faster. Moreover, the consumption of computational resources was almost minimized, and the model effectively identified rice diseases.

An Interpretable High-Accuracy Method for Rice Disease Detection Based on Multisource Data and Transfer Learning.

With the evolution of modern agriculture and precision farming, the efficient and accurate detection of crop diseases has emerged as a pivotal research focus. In this study, an interpretative high-precision rice disease detection method, integrating multisource data and transfer learning, is introduced. This approach harnesses diverse data types, including imagery, climatic conditions, and soil attributes, facilitating enriched information extraction and enhanced detection accuracy. The incorporation of transfer learning bestows the model with robust generalization capabilities, enabling rapid adaptation to varying agricultural environments. Moreover, the interpretability of the model ensures transparency in its decision-making processes, garnering trust for real-world applications. Experimental outcomes demonstrate superior performance of the proposed method on multiple datasets when juxtaposed against advanced deep learning models and traditional machine learning techniques. Collectively, this research offers a novel perspective and toolkit for agricultural disease detection, laying a solid foundation for the future advancement of agriculture.

Classification and Detection of Rice Diseases Using a 3-Stage CNN Architecture with Transfer Learning Approach

Rice is a vital crop for global food security, but its production is vulnerable to various diseases. Early detection and treatment of rice diseases are crucial to minimise yield losses. Convolutional neural networks (CNNs) have shown great potential for disease detection in plant leaves, but training CNNs requires large datasets of labelled images, which can be expensive and time-consuming. Here, we have experimented a 3-Stage CNN architecture with a transfer learning approach that utilises a pre-trained CNN model fine-tuned on a small dataset of rice disease images. The proposed approach significantly reduces the required training data while achieving high accuracy. We also incorporated deep learning techniques such as progressive re-sizing and parametric rectified linear unit (PReLU) to enhance rice disease detection. Progressive re-sizing improves feature learning by gradually increasing image size during training, while PReLU reduces overfitting and enhances model performance. The proposed approach was evaluated on a dataset of 8883 and 1200 images of disease and healthy rice leaves, respectively, achieving an accuracy of 94% when subjected to the 10-fold cross-validation process, significantly higher than other methods. These simulation results for disease detection in rice prove the feasibility and efficiency and offer a cost-effective, accessible solution for the early detection of rice diseases, particularly useful in developing countries with limited resources that can significantly contribute toward sustainable food production.

Comparison of CNN-based deep learning architectures for rice diseases classification

Although convolutional neural network (CNN) paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures, few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice diseases. Moreover, most CNN-based rice disease detection studies only considered a small number of diseases in their experiments. Both these shortcomings were addressed in this study. In this study, a rice disease classification comparison of six CNN-based deep-learning architectures (DenseNet121, Inceptionv3, MobileNetV2, resNext101, Resnet152V, and Seresnext101) was conducted using a database of nine of the most epidemic rice diseases in Bangladesh. In addition, we applied a transfer learning approach to DenseNet121, MobileNetV2, Resnet152V, Seresnext101, and an ensemble model called DEX (Densenet121, EfficientNetB7, and Xception) to compare the six individual CNN networks, transfer learning, and ensemble techniques. The results suggest that the ensemble framework provides the best accuracy of 98%, and transfer learning can increase the accuracy by 17% from the results obtained by Seresnext101 in detecting and localizing rice leaf diseases. The high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural systems. This research is significant for farmers in rice-growing countries, as like many other plant diseases, rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases

Rice is a staple food for over half of the global population, but it faces significant yield losses: up to 52% due to leaf blast disease and brown spot diseases, respectively. This study aimed at proposing a hybrid architecture, namely ResViT-Rice, by taking advantage of both CNN and transformer for accurate detection of leaf blast and brown spot diseases. We employed ResNet as the backbone network to establish a detection model and introduced the encoder component from the transformer architecture. The convolutional block attention module was also integrated to ResViT-Rice to further enhance the feature-extraction ability. We processed 1648 training and 104 testing images for two diseases and the healthy class. To verify the effectiveness of the proposed ResViT-Rice, we conducted comparative evaluation with popular deep learning models. The experimental result suggested that ResViT-Rice achieved promising results in the rice disease-detection task, with the highest accuracy reaching 0.9904. The corresponding precision, recall, and F1-score were all over 0.96, with an AUC of up to 0.9987, and the corresponding loss rate was 0.0042. In conclusion, the proposed ResViT-Rice can better extract features of different rice diseases, thereby providing a more accurate and robust classification output.

Approach and Analysis of Yolov4 Algorithm for Rice Diseases Detection at Different Drone Image Acquisition Distances

Rice production plays an important role in people's lives globally because rice is the most widely consumed staple food for over half of the world's human population. Unfortunately, the rice plants are prone to pests and diseases which may result in a decrease in the rice production. Thus, early and accurate detection of rice diseases is needed. This paper discusses a method for detecting rice diseases called You Only Look Once (YOLO) object detection algorithm version 4. A drone camera was used to acquire the images from four different distances, namely 2 meters, 5 meters, 10 meters and 20 meters. This approach aims to detect the presence of pests to be faster, more accurate and more precise. The precision results at each image capture from the different distances were 46.8%, 48%, 65% and 77.3% with the average loss value of 6.52, 0.54, 1.16 and 2.73

Enhancing Rice Crop Health through Computational Intelligence-Based Disease Detection

Rice is one of the most important staple crops worldwide, and rice plant diseases are a significant threat to global food security. Early detection and accurate classification of these diseases are crucial for effective disease management and prevention of crop losses. In this paper, we propose a novel computational intelligence-based technique for rice disease detection and classification. Our proposed method is composed of a residual network-based feature extractor followed by a Light Gradient Boosting Machine (LGBM) classifier. We use a publicly available rice leaf dataset to evaluate the performance of our proposed method. The results demonstrate that our proposed method achieves high accuracy, sensitivity, and specificity in identifying diseased rice plants, outperforming existing state-of-the-art methods. We also compare our proposed method against other methods using different performance metrics, showing its superior performance. The proposed method provides a promising approach to enhance rice crop health management and can be adapted and customized for other crops and agricultural settings. The proposed computational intelligence-based technique for rice disease detection and classification has significant implications for improving crop productivity and ensuring food security.

A Copy Paste and Semantic Segmentation-Based Approach for the Classification and Assessment of Significant Rice Diseases.

The accurate segmentation of significant rice diseases and assessment of the degree of disease damage are the keys to their early diagnosis and intelligent monitoring and are the core of accurate pest control and information management. Deep learning applied to rice disease detection and segmentation can significantly improve the accuracy of disease detection and identification but requires a large number of training samples to determine the optimal parameters of the model. This study proposed a lightweight network based on copy paste and semantic segmentation for accurate disease region segmentation and severity assessment. First, a dataset for rice significant disease segmentation was selected and collated based on 3 open-source datasets, containing 450 sample images belonging to 3 categories of rice leaf bacterial blight, blast and brown spot. Then, to increase the diversity of samples, a data augmentation method, rice leaf disease copy paste (RLDCP), was proposed that expanded the collected disease samples with the concept of copy and paste. The new RSegformer model was then trained by replacing the new backbone network with the lightweight semantic segmentation network Segformer, combining the attention mechanism and changing the upsampling operator, so that the model could better balance local and global information, speed up the training process and reduce the degree of overfitting of the network. The results show that RLDCP could effectively improve the accuracy and generalisation performance of the semantic segmentation model compared with traditional data augmentation methods and could improve the MIoU of the semantic segmentation model by about 5% with a dataset only twice the size. RSegformer can achieve an 85.38% MIoU at a model size of 14.36 M. The method proposed in this paper can quickly, easily and accurately identify disease occurrence areas, their species and the degree of disease damage, providing a reference for timely and effective rice disease control.

Early Detection of Bacterial Blight in Hyperspectral Images Based on Random Forest and Adaptive Coherence Estimator

Rice disease detection is of great significance to rice disease management. It is difficult to identify the rice leaves with different colors in different disease periods by RGB image and without aided eyes. Traditional equipment and methods are relatively inefficient in meeting the needs of current disease detection. The accurate and efficient detection the infected areas from hyperspectral images has become a primary concern in current research. However, current spectral target detection research pays less attention to the time and computing resources consumed by detection. A disease detection method based on random forest (RF) and adaptive coherence estimator (ACE) is proposed here. Firstly, based on the spectral differences between diseased and healthy leaves, 18 characteristic spectral wavelengths with the highest importance were selected by an RF algorithm, and the spectral images of those characteristic wavelengths were synthesized. Then, the ACE model was established for the disease recognition of full wavelength spectral images, characteristic wavelength spectral images, and RGB images. At the same time, three other familiar target detection methods were selected as the control experiments. The detection results showed a similarity between the detection performance of the four detection methods for full wavelength spectral image and characteristic wavelength spectral image. This detection performance was higher than that of the RGB image, indicating that characteristic wavelength spectral image can replace full wavelength spectral image for disease detection. The detection performance of the ACE algorithm was better than other algorithms. The detection accuracy of 18 characteristic wavelengths was 97.41%. Compared with the hyperspectral full wavelength image detection results, the accuracy decreased by 1.12%, and the detection time decreased by 2/3, which greatly reduced the detection time. Based on these results, the target detection method combining the RF algorithm and the ACE algorithm can effectively and accurately detect rice bacterial blight disease, which provides a new method for automatic detection of plant disease in the field.

A Lightweight Real-Time Rice Blast Disease Segmentation Method Based on DFFANet

The evaluation of rice disease severity is a quantitative indicator for precise disease control, which is of great significance for ensuring rice yield. In the past, it was usually done manually, and the judgment of rice blast severity can be subjective and time-consuming. To address the above problems, this paper proposes a real-time rice blast disease segmentation method based on a feature fusion and attention mechanism: Deep Feature Fusion and Attention Network (abbreviated to DFFANet). To realize the extraction of the shallow and deep features of rice blast disease as complete as possible, a feature extraction (DCABlock) module and a feature fusion (FFM) module are designed; then, a lightweight attention module is further designed to guide the features learning, effectively fusing the extracted features at different scales, and use the above modules to build a DFFANet lightweight network model. This model is applied to rice blast spot segmentation and compared with other existing methods in this field. The experimental results show that the method proposed in this study has better anti-interference ability, achieving 96.15% MioU, a speed of 188 FPS, and the number of parameters is only 1.4 M, which can achieve a high detection speed with a small number of model parameters, and achieves an effective balance between segmentation accuracy and speed, thereby reducing the requirements for hardware equipment and realizing low-cost embedded development. It provides technical support for real-time rapid detection of rice diseases.