Rice Leaf Diseases Research Articles

An Enhanced CNN-based ELM Classification for Disease Prediction in the Rice Crop

To meet the demands of a constantly expanding population, intensive farming is becoming more popular in the modern day. This strategy, meanwhile, increases the possibility of a wider range of plant illnesses. By reducing crop productivity in terms of both quantity and quality, these infections represent a threat to food production and ultimately result in a fall in the economy. Fortunately, new opportunities for early diagnosis of such epidemics have emerged because of technological improvements, which are advantageous for society as a whole. The difficulties created by technology and bio-mutations create a potential for additional breakthroughs, notwithstanding the significant contributions made by researchers in the field of agricultural disease diagnosis. The suggested framework comprises three key phases: preprocessing, feature extraction, and the classification of leaf diseases. To optimize computational resources and memory utilization, the input image undergoes pre-processing as a preliminary step. Afterward, a Convolutional Neural Network (CNN) is utilized on an extensive dataset of labeled images to capture pertinent features for the diagnosis of rice leaf diseases. The suggested model utilizes an Efficient Selective Pruning of Hidden Nodes (ELM) classifier based on the RBF kernel to classify the input data.

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.

Image recognition of rice leaf diseases using atrous convolutional neural network and improved transfer learning algorithm

Image recognition of rice leaf diseases using atrous convolutional neural network and improved transfer learning algorithm

Radial basis function Neural Network optimized with Salp Swarm algorithm espoused paddy leaf disease classification

Diseases in rice often cause yield losses of 20–40% in crop production and are closely linked globally. Disease detection is difficult to quickly plan treatments and decrease crop loss. Diagnosis of rice diseases till done manually. Attain AI-assisted detection of disease, here proposed radial function Neural Network optimized with Salp-Swarm approach (PLDC-RBFNN-SSA) for rice leaf disease segmentation. First, we extract rice leaf images from a dataset containing rice leaf disease image samples. Then the preprocessing augmentation process is performed using a random transformation method such as: Simple image rotation as well as mirror functions implemented to every images. B. Rotate Right 90°, Rotate Left 90°, Flip Vertically, Flip Horizontally, Rotate 180°. These preprocessed output images are then passed to Black Widow's k-means clustering method to segment regions of interest (ROI) for rice leaf disease. The segmented output image is then sent to an adaptive grayscale co-occurrence matrix windowing algorithm (GLCMWAA) to extract radiation features. Extracted features then input into a radial function Neural Network optimized with Salp-Swarm algorithm to classify rice leaf images into bacterial blight, blast, brown spot and tungro. The proposed method is implemented in MATLAB and the performance of the proposed PLDC-RBFNN-SSA approach achieves 15.25%, 18.98%, 20.5% and 24.85% higher accuracy. Computation time is reduced by 50.2%, 48.2%, 38.26%, 20.2% compared with existing methods. Finally, the simulation results demonstrate that the proposed PLDC-RBFNN-SSA method is more efficient and accurate to obtain the optimal global solution for detecting and classifying leaf diseases in rice breeding is shown.

Stacking-based and improved convolutional neural network: a new approach in rice leaf disease identification.

Rice leaf diseases are important causes of poor rice yields, and accurately identifying diseases and taking corresponding measures are important ways to improve yields. However, rice leaf diseases are diverse and varied; to address the low efficiency and high cost of manual identification, this study proposes a stacking-based integrated learning model for the efficient and accurate identification of rice leaf diseases. The stacking-based integrated learning model with four convolutional neural networks (namely, an improved AlexNet, an improved GoogLeNet, ResNet50 and MobileNetV3) as the base learners and a support vector machine (SVM) as the sublearner was constructed, and the recognition rate achieved on a rice dataset reached 99.69%. Different improvement methods have different effects on the learning and training processes for different classification tasks. To investigate the effects of different improvement methods on the accuracy of rice leaf disease diagnosis, experiments such as comparison experiments between single models and different stacking-based ensemble model combinations and comparison experiments with different datasets were executed. The model proposed in this study was shown to be more effective than single models and achieved good results on a plant dataset, providing a better method for plant disease identification.

An Accurate Classification of Rice Diseases Based on ICAI-V4.

Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network's ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network's feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method's strong performance and feasibility for rice disease classification in real-life scenarios.

Automatic Disease Detection in the Rice Leaves Employing a Support Vector Machine

Rice is a major agricultural crop around the world. Crop diseases, on the other hand, have the potential to reduce yield and quality greatly, posing a major danger to global food supplies. As a result, disease control is essential for rice production. Accurate and prompt disease diagnosis is critical to disease control success, which allows pesticide control measures to be implemented. The most common method for diagnosing rice leaf diseases is a manual decision-making based on disease appearance. There aren't enough skilled workers in the area, for such tasks to be completed on time. As a result, a more effective and convenient way of identifying rice leaf diseases is required. Therefore, this research creates an automatic diagnosis approach for rice leaf disease detection using deep learning. The proposed solution is built with deep learning techniques and a huge dataset containing 2,000 images of three types of rice diseases such as leaf blast, sheath blight, and brown spot, and healthy leaf. The proposed model's robustness is improved by using its real-world rice leaf datasets as well as publicly available online datasets. With an accuracy of 96.0%, the proposed deep-learning-based strategy proved successful in automatically diagnosing the three discriminative diseases of rice leaves. Furthermore, 99.25% of the time, the algorithm accurately detected a healthy rice leaf. The results demonstrate that the suggested deep learning model gives a highly effective technique for identifying rice leaf infections, and is capable of quickly and reliably identifying the most common rice diseases.

Deep learning model for detection of brown spot rice leaf disease with smart agriculture

Given that it provides nourishment for more than half of humanity, rice is regarded as one of the most significant plants in the world in agriculture. The quantity and quality of the product may be impacted by diseases that can damage rice plants which can occasionally cause crop losses ranging from 30 to 60%. This manuscript proposed a Convolutional Neural Network (CNN) and Visual Geometry Group (VGG)19 i.e. CNN-VGG19 model with a transfer learning-based method for the precise identification and classification of rice leaf diseases. This scheme employs a transfer learning technique based on the VGG19 which can identify the brown spot class. The accuracy is 93.0% in the deployment of the dataset of rice leaf disease. The other parameters are sensitivity, specificity, precision and F1-score with 89.9%, 94.7%, 92.4% and 90.5% respectively. The developed technique obtained better results as compared to the existing baseline models.

Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model

To ensure higher quality, capacity, and production of rice, it is vital to diagnose rice leaf disease in its early stage in order to decrease the usage of pesticides in agriculture which in turn avoids environmental damage. Hence, this article presents a Multi-scale YOLO v5 detection network to detect and classify the rice crop disease in its early stage. The experiment is initially started by pre-processing the rice leaf images obtained from the RLD dataset, after which data set labels are created, which are then divided into train and test sets. DenseNet-201 is used as the backbone network and depth-aware instance segmentation is used to segment the different regions of rice leaf. Moreover, the proposed Bidirectional Feature Attention Pyramid Network (Bi-FAPN) is used for extracting the features from the segmented image and also enhances the detection of diseases with different scales. Furthermore, the feature maps are identified in the detection head, where the anchor boxes are then applied to the output feature maps to produce the final output vectors by the YOLO v5 network. The subset of channels or filters is pruned from different layers of deep neural network models through the principled pruning approach without affecting the full framework performance. The experiments are conducted with RLD dataset with different existing networks to verify the generalization ability of the proposed model. The effectiveness of the network is evaluated based on various parameters in terms of average precision, accuracy, average recall, IoU, inference time, and F1 score, which are achieved at 82.8, 94.87, 75.81, 0.71, 0.017, and 92.45 respectively.

Non-Destructive Classification of Paddy Rice Leaf Disease Infected by Bacterial and Fungal Species Using Vision-Based Deep Learning

Rice is a plant with rounded hollow articulated culms, flat, well-attached leaf blades, and terminal spikes. Its cultivation and consumption shape the culture, diet, and economy of different groups, especially in Asia. However, farmers suffer great financial losses each year due to rice disease. Therefore, the identification and classification of rice diseases are very important. Prompt, timely, and accurate disease diagnosis prevents product loss and improves crop quality. This study focuses on the classification of whether rice paddy leaf is normal or has a disease (one of the following: bacterial leaf blight (BLB), bacterial leaf streaks (BLS), bacterial panicle blight (BPB): heart, downy mildew, hispa, and rice tungro disease (RTD)) using deep learning-based algorithms such as EfficientNet-b0, MobileNet-v2, and Places365-GoogLeNet. The best model for this simulation was found to be EfficientNet-b0 with an average accuracy of 97.74%.

Optimal deep generative adversarial network and convolutional neural network for rice leaf disease prediction

Optimal deep generative adversarial network and convolutional neural network for rice leaf disease prediction

Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification

Rice is a staple food for roughly half of the world’s population. Some farmers prefer rice cultivation to other crops because rice can thrive in a wide range of environments. Several studies have found that about 70% of India’s population relies on agriculture in some way and that agribusiness accounts for about 17% of India’s GDP. In India, rice is one of the most important crops, but it is vulnerable to a number of diseases throughout the growing process. Farmers’ manual identification of these diseases is highly inaccurate due to their lack of medical expertise. Recent advances in deep learning models show that automatic image recognition systems can be extremely useful in such situations. In this paper, we propose a suitable and effective system for predicting diseases in rice leaves using a number of different deep learning techniques. Images of rice leaf diseases were gathered and processed to fulfil the algorithmic requirements. Initially, features were extracted by using 32 pre-trained models, and then we classified the images of rice leaf diseases such as bacterial blight, blast, and brown spot with numerous machine learning and ensemble learning classifiers and compared the results. The proposed procedure works better than other methods that are currently used. It achieves 90–91% identification accuracy and other performance parameters such as precision, Recall Rate, F1-score, Matthews Coefficient, and Kappa Statistics on a normal data set. Even after the segmentation process, the value reaches 93–94% for model EfficientNetV2B3 with ET and HGB classifiers. The proposed model efficiently recognises rice leaf diseases with an accuracy of 94%. The experimental results show that the proposed procedure is valid and effective for identifying rice diseases.

RiceDRA-Net: Precise Identification of Rice Leaf Diseases with Complex Backgrounds Using a Res-Attention Mechanism

In this study, computer vision applicable to traditional agriculture was used to achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and used it to identify four different rice leaf diseases. The rice leaf disease test set with a complex background was named the CBG-Dataset, and a new single background rice leaf disease test set was constructed, the SBG-Dataset, based on the original dataset. The Res-Attention module used 3 × 3 convolutional kernels and denser connections compared with other attention mechanisms to reduce information loss. The experimental results showed that RiceDRA-Net achieved a recognition accuracy of 99.71% for the SBG-Dataset test set and possessed a recognition accuracy of 97.86% on the CBG-Dataset test set. In comparison with other classical models used in the experiments, the test accuracy of RiceDRA-Net on the CBG-Dataset decreased by only 1.85% compared with that on the SBG-Dataset. This fully illustrated that RiceDRA-Net is able to accurately recognize rice leaf diseases with complex backgrounds. RiceDRA-Net was very effective in some categories and was even capable of reaching 100% precision, indicating that the proposed model is accurate and efficient in identifying rice field diseases. The evaluation results also showed that RiceDRA-Net had a good recall ability, F1 score, and confusion matrix in both cases, demonstrating its strong robustness and stability.

SE_SPnet: Rice leaf disease prediction using stacked parallel convolutional neural network with squeeze‐and‐excitation

AbstractRice is one of the significant crops, and the early identification and prevention of its diseases are essential to ensure adequate and healthy availability to the world's growing population. The use of image processing is an encouraging method for automatic rice leaf disease identification and detection. In particular, the recent advancements indicate the effectiveness of convolutional neural network (CNN) based deep learning approaches. In this direction, the present work proposes a novel stacked parallel convolution layers‐based network (SPnet) with the squeeze‐and‐excitation (SE) architecture, named (SE_SPnet), for classifying diseased rice leaf images. The stacked parallel network block comprises four parallel convolution layers with different kernel sizes for abstractions of the global and local features. The SE block extracts feature information automatically while removing invalid ones. We compare the SE_SPnet model with state‐of‐the‐art CNN models such as VGG16, DenseNet121, and InceptionV3 based on computational effort, accuracy, sensitivity, specificity, precision, recall, and F1‐score. The experimental results show that the SE_SPnet outperforms standard CNN models for the considered rice leaf disease image datasets. In particular, the SE_SPnet achieves the highest accuracy (99.2%), sensitivity (98.2%), specificity (98.5%), precision (98.4%), recall (98.2%), and F1‐score (98.5%) while using stochastic gradient descent (with momentum) optimizer with a 0.01 learning rate. Furthermore, the SE_SPnet also exhibits to outperform when compared with some of the most recent and relevant existing works.

AI based rice leaf disease identification enhanced by Dynamic Mode Decomposition

AI based rice leaf disease identification enhanced by Dynamic Mode Decomposition

Automatic Recognition of Rice Leaf Diseases Using Transfer Learning

Rice, the world’s most extensively cultivated cereal crop, serves as a staple food and energy source for over half of the global population. A variety of abiotic and biotic factors such as weather conditions, soil quality, temperature, insects, pathogens, and viruses can greatly impact the quantity and quality of rice grains. Studies have established that plant infections have a significant impact on rice crops, resulting in substantial financial losses in agriculture. To accurately diagnose and manage the diseases affecting rice plants, plant pathologists are seeking efficient and reliable methods. Traditional disease detection techniques, employed by farmers, involve time-consuming visual inspections and result in inadequate farming practices. With advancements in agricultural technology, the identification of pathogenic organisms in rice plants has become significantly more manageable through techniques such as machine learning and deep learning, which are receiving significant attention in crop disease research. In this paper, we used the transfer learning approach on 15 pre-trained CNN models for the automatic identification of Rice leave diseases. Results showed that the InceptionV3 model is outperforming with an average accuracy of 99.64% with Precision, Recall, F1-Score, and Specificity as 98.23, 98.21, 98.20, and 99.80, and the AlexNet model resulted in poor performance with average accuracy of 97.35% among others.

Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods

Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods

Rice leaf disease identification by residual-distilled transformer

As the worldwide planting crop, rice feeds nearly half of the world’s population. However, the continuous spread of diseases is threatening rice production. It is of great practical value to identify rice diseases precisely. Recent studies suggest that the computational approaches provide an opportunity for rice leaf disease prediction and achieve a series of achievements. However, the existing works for rice leaf disease identification are still unsatisfactory either in identification accuracy or model interpretability. To address these limitations, a residual-distilled transformer architecture is proposed in this study. Inspired by the early success of transformers in computer vision, the distillation strategy is introduced to distill weights and parameters from the pre-trained vision transformer models. The residual concatenation between vision transformer and the distilled transformer are as residual blocks for features extraction, and then fed them into multi-layer perceptron (MLP) for prediction. Experimental results demonstrate that the presented method achieves 0.89 F1-score and 0.92 top-1 accuracy, outperforms the existing state-of-the-art models on the rice leaf disease dataset which collected in paddy fields. In addition, the proposed architecture provides model interpretability to grasp the key features that are significant for positive prediction results.

Implementation of Pretrained VGG16 Model for Rice Leaf Disease Classification using Image Segmentation

Rice is an agricultural sector that produces rice which is one of the staple foods for the majority of the population in Indonesia. In the cultivation of rice plants there are also factors that affect rice production and are not realized by farmers causing that they are late in handling and diagnosing symptoms and making rice production decline. Therefore, it is necessary to have an early diagnosis of rice plants to identify them correctly, quickly and accurately. Machine learning is one of the classification techniques to detect various plant diseases such as rice plants. There are several studies on machine learning using the Convolutional Neural Network with the VGG16 model to classify rice leaf diseases and using Image Segmentation techniques on rice leaf datasets for make the image becomes a form that is not too complicated to analyze. The data used in this research is Rice Leaf Disease which consists of 3 classes including Bacterial leaf blight, Brown spot, and Leaf smut. Then segmentation is carried out using two techniques, namely threshold and k means. Then data augmentation for make dataset used has a large and varied number and training using VGG16 model with hyperparameter tuning and obtained 91.66% accuracy results for scenarios with the k-means dataset.

Detection and Prediction of Rice Leaf Disease Using a Hybrid CNN-SVM Model

Detection and Prediction of Rice Leaf Disease Using a Hybrid CNN-SVM Model