Image Classification of Rice Leaf Diseases Using Random Forest Algorithm

Abstract. Hartoni, Siregar VP, Wouthuyzen S, Agus SB. 2021. Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu. Biodiversitas 23: 514-520. Benthic habitats have very high complexity and are home to many types of aquatic organisms. Benthic habitats have various functions, including habitat for flora and fauna, sediment traps, nursery areas, and foraging areas for aquatic fauna that are susceptible to damage due to human activities or natural factors. Therefore, more accurate spatial information is needed. The purpose of this study was to examine the ability of object-based classification techniques for mapping shallow waters benthic habitats using Sentinel 2A imagery. The two classification algorithms used are support vector machine (SVM) and random forest (RF). The input image layer (IIL) used for classification is the natural color band (Band 432). The results showed that the SVM and RF classification algorithms could classify eight classes of benthic habitats. The overall accuracy (OA) of the SVM algorithm is 65%, while the RF accuracy is 67%, with kappa values of 0.59 and 0.60, respectively. The significant test applied to Sentinel 2 images with SVM and RF algorithms for benthic habitats has a Z test value of-0.41. These results indicate that the classification results between the SVM and RF algorithms are not significantly different.

Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. The initial algorithmic phase involved image pre-processing of the crop images, using a bilateral filter to improve image quality. The effectiveness of this step was measured by using metrics like the Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), MobileNetV2, and Convolutional Neural Network (CNN). The proposed CNN model stood out, since it demonstrated exceptional performance in identifying rice leaf diseases, with test Accuracy of 98% and high Precision, Recall, and F1 scores. This result highlights that the proposed model is particularly well suited for rice leaf disease classification. The robustness of the proposed model was validated through k-fold cross-validation, confirming its generalizability and minimizing the risk of overfitting. This study not only focused on classifying rice leaf diseases but also has the potential to benefit farmers and the agricultural community greatly. This work highlights the advantages of custom CNN models for efficient and accurate rice leaf disease classification, paving the way for technology-driven advancements in farming practices.

Rice is one of the global most critical harvests, and a great many people eat it as a staple eating routine. Different rice plant diseases harm, spread, and drastically reduce crop yields. In extreme situations, they may result in no grain harvest at all, posing a severe threat to food security. In this paper, to amplify the recognition ability for rice leaf disease (RLD) classification, we proposed hierarchical transfer learning (HTL) methods incorporating ensemble models containing two-step. In the first step, an ensemble combining MobileNet and DenseNet was addressed to tackle the diseased leaf problem. Consequently, DenseNet and XceptionNet were fused to identify three RLDs. Here, we compare our models with state-of-the-art deep learning models such as ResNet, DenseNet, InceptionNet, Xception, MobileNet, and EfficientNet. Our framework at top-notch with 89 % and 91 % for accuracy. In future works, RLD segmentation is suggested to pinpoint the illness and quantify the afflicted region.

Our society is becoming increasingly reliant on technology every day. Agriculture, on the other hand, is critical to human survival. Rice is one of the most important grains of food. It feeds nearly half of the world’s population and supports a large number of jobs. Crop diseases cause significant yield losses around the world, particularly in Sub Saharan Africa. To implement effective disease management measures, pathogen detection and understanding spatiotemporal dynamics are critical, and this necessitates the use of molecular detection methods, particularly to discriminate between infections that cause similar symptoms. As a result, adequate disease mitigation for rice plants is critical. We used to our proposes a model for detecting three rice leaf diseases: brown spot, leaf smut, and bacterial leaf blight. This paper presents a unique model for the classification of rice leaf diseases by multi-class SVM, K—means clustering, and PSO. The disease was classified using an SVM classifier and the classifier accuracy is optimized using PSO. The exploratory results show that the proposed approach performed well in terms of disease detection accuracy, with a score of 98.89%.

Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. Multiple network attacks by an intruder are unavoidable. Our research targets the critical issue of class imbalance in intrusion detection, a reflection of the real-world scenario where legitimate network activities significantly out number malicious ones. This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. The primary goal is to create models that are highly sensitive to intrusions while minimizing false alarms, an essential aspect of effective IDS. This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. Our research, by addressing these significant challenges, is positioned to make substantial contributions to cybersecurity, providing valuable insights and applicable solutions in the fight against digital threats and ensuring robustness and relevance in IDS development. An intrusion detection system (IDS) checks network traffic for security, availability, and being non-shared. Despite the efforts of many researchers, contemporary IDSs still need to further improve detection accuracy, reduce false alarms, and detect new intrusions. The mean convolutional layer (MCL), feature-weighted attention (FWA) learning, a bidirectional long short-term memory (BILSTM) network, and the random forest algorithm are all parts of our unique hybrid model called MCL-FWA-BILSTM. The CNN-MCL layer for feature extraction receives data after preprocessing. After convolution, pooling, and flattening phases, feature vectors are obtained. The BI-LSTM and self-attention feature weights are used in the suggested method to mitigate the effects of class imbalance. The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. The suggested model's accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. The model's binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. The model proficiently identifies diverse network intrusions on computer networks and accomplishes its intended purpose. The suggested model will be helpful in a variety of network security research fields and applications.

Rice is a staple food feeding more than half of the world’s population. Rice disease is one of the major problems affecting rice production. Machine Vision Technology has been used to help develop agricultural production, both in terms of quality and quantity. In this study, convolutional neural network (CNN) was applied to detect and identify diseases in images. We studied 6 varieties of major rice diseases, including blast, bacterial leaf blight, brown spot, narrow brown spot, bacterial leaf streak and rice ragged stunt virus disease. Our studied used well known pre-trained models namely Faster R-CNN, RetinaNet, YOLOv3 and Mask RCNN, and compared their detection performance. The database of rice diseases used in our study contained photographs of rice leaves taken from fields of planting areas. The images were taken under natural uncontrolled environment. We conducted experiments to train and test each model using a total of 6,330 images. The experimental results showed that YOLOv3 provided the best performance in term of mean average precision (mAP) at 79.19% in the detection and classification of rice leaf diseases. The precision obtained from Mask R-CNN, Faster R-CNN, and RetinaNet was at 75.92%, 70.96%, and 36.11%, respectively.

Rice (Oryza sativa L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features.

Food security continues to be a significant challenge the world over, with crop production becoming increasingly threatened by crop diseases and pest infestations. In the case of chili production, farmers often suffer significant yield loss and economic insecurity due to the unpredictable nature of both of these problems. Current pest control options (agrochemical and organic methods alike) have not reliably been enough for timely and effective control and demonstrate the importance of early and effective pest and disease identification processes. To solve this problem, the present work describes a deep learning implementation that leverages advanced YOLO-based architectures for the detection and classification of leaf diseases and pest on chili plant leaves. The present work trains and evaluates three models (YOLOv5, YOLOv7, and YOLOv8) on a newly created and balanced dataset of over 28,800 images combining 20 total classes of pest and leaf diseases. The dataset was supplemented by preprocessing the images and conducting an augmentation process to create a total of 32,000 images for training to generate reliable models. The results from the experiments found that YOLOv8 provided the best baseline performance of 95.1% mean Average Precision (mAP), while YOLOv5 had an mAP of 86.1%, and YOLOv7 had an mAP of 67.5%. An additional enhancement in the construction of a Modified YOLOv8 hybrid model—reflecting all advantages of YOLOv5, YOLOv7, and YOLOv8—achieved a highest mAP of 99.5% to be the most effective model in this study, the results suggest that the newly proposed Modified YOLOv8 framework, is highly accurate and reliable for the early detection of pests and diseases in chili, and is helpful to improve sustainable agricultural practices, mitigate crop losses, and increase global food security.

Rice is amongst the majorly cultivated crops in India and its leaf diseases can have a substantial impact on output and quality. The most important component is identifying rice leaf diseases, which have a direct impact on the economy and food security. Brown spot, Leaf Blast, Hispa are the most frequently occurring rice leaf diseases. To resolve this issue, we have studied various machine learning and deep learning approaches for detecting the diseases on their leaves by calculating their accuracy, recall, and precision to measure the performance. This study helps the farmers by detecting the diseases in rice leaves in order to get a healthy crop yield. The deep learning models perform well when compared with the machine learning methods. After analyzing all of the deep learning models, we found that the 5-layer convolution model had the best accuracy of 78.2 %, while others, such as VGG16, had a lower accuracy of 58.4%.

Due to the complexity of data characteristics, multi-label learning in data mining has been proposed by scholars to solve the problem of information knowledge in the era of big data. In the era of big data, the complexity of the data structures makes it impossible for traditional single-label learning methods to meet the needs of technological development. Moreover, the importance of multi-label learning is gradually becoming evident. The random forest (RF) algorithm is regarded as one of the best classification algorithms. In this study, the traditional decision tree algorithm was improved, and the traditional RF method was converted into an adaptive RF (ARF) method for multi-label classification. By experiments, the effectiveness of the proposed method was verified. The RF method may not be able to classify massive data in a short time, but Hadoop, which was by Apache, is suitable for data-intensive tasks. On this basis, we modified the MapReduce programming mode to make it suitable for the proposed ARF method. This method was implemented on the cloud platform, and the time effectiveness of the parallel model was verified by experiments.

Potholes are defects on the surface of roads, streets, or pavements brought on by depressions or holes, which are hazardous for vehicles and pedestrians, whether small divots or huge craters. Various methods have been explored to improve the accuracy of potholes detection. Existing approaches have advantages and disadvantages. This paper presents the proposed method of pothole detection utilising Doppler Radar signal's Power Spectrum Density (PSD) together with the Decision Tree classification algorithm. While continuous waveform (CW) radar is able to identify moving targets, it cannot localise the exact depth of the reflector, which is the prominent characteristic of potholes. In addition, the target's reflected signal is likely to be masked by nearby harmonics. Since the radar is moving despite the target of interest, mounting it on a moving vehicle offers a different perspective. This paper explores the potential of Doppler radar's signal for pothole detection while comparing two Machine Learning (ML) techniques. A commercially over-the-shelf (COTS) K-LC2 Doppler radar was employed to acquire pothole and non-pothole raw datasets. Doppler signal was hardly distinguished between pothole and non-pothole, either in the time or frequency domain. Hence, Doppler signals were converted to power spectral density (PSD), and PSD's features were extracted. Extracted features were applied with the coarse Decision Tree (DT) and K-Nearest Neighbours (KNN) classification algorithms. The result exhibits a better accuracy of 91.2% for 80:20 distribution by using the Decision Tree.

In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.

Programmed cell death (PCD) plays critical roles in plant immunity but must be regulated to prevent excessive damage. In this study, a novel spotted leaf (spl11-1) mutant was identified from an ethyl methane sulfonate (EMS) population. The SPL11-1 gene was genetically mapped to chromosome 12 between the Indel12-37 and Indel12-39 molecular markers, which harbor a genomic region of 27 kb. Annotation of the SPL11-1 genomic region revealed the presence of two candidate genes. Through gene prediction and cDNA sequencing, it was confirmed that the target gene in the spl11-1 mutant is allelic to the rice SPOTTED LEAF (SPL11), hereafter referred to as spl11-1. Sequence analysis of SPL11 revealed a single bp deletion (T) between the spl11-1 mutant and the ‘Shuangkang77009’ wild type. Moreover, protein structure analysis showed that the structural differences between the SPL11-1 and SPL11 proteins might lead to a change in the function of the SPL11 protein. Compared to the ‘Shuangkang77009’ wild type, the spl11-1 mutant showed more disease resistance. The agronomical evaluation showed that the spl11-1 mutant showed more adverse traits. Through further mutagenesis treatment, we obtained the spl11-2 mutant allelic to spl11-1, which has excellent agronomic traits and more improvement and may have certain breeding prospects in future breeding for disease resistance.

ABSTRACTXanthomonas oryzae pv. oryzae (Xoo) is the causal agent of bacterial leaf blight (BLB) disease in rice, exerting a detrimental impact on the yield of various rice cultivars in Vietnam, notably the Bacthom 7 (BT7) variety. Xoo possesses transcription activator‐like effectors (TALEs) which modulate the expression of host genes by specifically binding to the effector‐binding element (EBE) sequence in target promoter regions, thereby facilitating bacterial proliferation and pathogenesis on rice plants. Notably, OsSWEET13, a member of the Sugar Will Eventually be Exported Transporter (SWEET) gene family encoding sugar transporter proteins, has been identified as a prominent susceptibility (S) gene for BLB. This investigation focuses on elucidating the role of OsSWEET13 in the susceptibility of BT7 to BLB disease in Vietnam. Vietnamese Xoo (VXO) strains were observed to elicit characteristic BLB symptoms on BT7 and trigger the expression of OsSWEET13 during the course of infection. To further probe this, a clustered regularly interspaced palindromic repeats/CRISPR‐associated protein 9 (CRISPR/Cas9) construct targeting the EBE PthXo2A site on the OsSWEET13 promoter was designed and introduced into BT7 rice plants through Agrobacterium tumefaciens, resulting in the creation of rice lines with OsSWEET13 promoter modifications. Upon conducting detailed genotype and phenotype analyses of the gene‐edited rice plants, it was ascertained that the mutation exhibited no adverse effects on the principal agronomic traits under scrutiny. Notably, a transgene‐free homozygous 3‐nucleotide deletion modified rice line displayed diminished OsSWEET13 gene expression and a significant reduction in BLB development upon inoculation with VXO strains. This study unequivocally establishes OsSWEET13 as an S gene for VXO, underscoring its significance in breeding programs aimed at enhancing BLB resistance in the BT7 variety through the utilization of CRISPR/Cas9 technology.

Artificial intelligence (AI) has shown potential in analyzing electromyography (EMG) signals, but clinical applicability remains limited by studies based on small, curated datasets, and variable accuracy. This study evaluated AI performance in classifying needle electromyography (EMG) signals as muscle activity versus background/noise/artifact, and then in distinguishing three clinical categories: amyotrophic lateral sclerosis (ALS), myopathy, and non-disease controls. Data from the Cleveland Clinic Foundation EMG Database (CCFDB), a large clinically acquired EMG dataset was utilized for this study. A two-step classification approach was used: a convolutional neural network (CNN) to separate muscle activity from background/noise/artifact, followed by a random forest algorithm and CNNs for clinical category classification. Feature extraction techniques included Short-Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), and Wavelet Packet Decomposition (WPD). EMG data from 608 participants (266 ALS, 89 myopathy, 253 non-disease controls), totaling 11,456 muscle recordings, and 15,613 segments of muscle activity were included. The muscle activity detection model achieved 85.4% accuracy. For clinical category classification, CWT with a two-layer CNN performed best on the CCFDB (62% accuracy). Deeper CNN architectures did not consistently improve performance. On the publicly available curated EMGlab dataset, the best accuracy using the same AI models was higher (91%). AI can assist in EMG analysis, but the performance gap between curated and clinically-acquired datasets underscores the need for robust models capable of handling signal variability and complexity in authentic clinical contexts. Future efforts should focus on clinically-oriented AI development to improve translational applicability.