An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology.

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.

The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT models by employing advanced machine learning and computer vision techniques. This approach commences with data augmentation to enhance the diversity and volume of resistivity data. Subsequently, a bilateral filter was applied to reduce noise while preserving edge details within the resistivity images. To further improve image contrast and highlight significant resistivity variations, contrast-limited adaptive histogram equalization (CLAHE) was employed. Finally, k-means clustering was utilized to segment the resistivity data into distinct groups based on resistivity values, enabling the identification of color features in different centroids. This facilitated the detection of regions with significant resistivity contrasts in the reservoir. From the clustered images, color masks were generated to visually differentiate the groups and calculate the area and proportion of each group within the pictures. Key features extracted from resistivity profiles were used to train unsupervised learning models capable of generalizing across different geological settings. The proposed methodology improves the accuracy of detecting zones with oil potential and offers scalable applicability to different datasets with minimal retraining, applicable to different subsurface environments. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. During initial analyses using only k-means, the resulting optimal value of the silhouette coefficient K was 2. After using bilateral filtering together with contrast-limited adaptive histogram equalization (CLAHE) and validation by an expert, the results were more representative, and six clusters were identified. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques.

Tomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and managing these diseases, we propose an advanced automatic system for detecting and identifying tomato leaf diseases using sophisticated image processing techniques. Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co‐occurrence matrix (GLCM) and scale‐invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. We curated an extensive dataset of 2700 tomato leaf images, with a minimum of 300 images for each of the nine distinct disease classes. This comprehensive dataset facilitated the training and testing of various machine learning and deep learning models. The experimental results highlight our proposed approach’s exceptional accuracy and reliability, significantly improving the detection and classification of tomato leaf diseases. A thorough comparative analysis with contemporary state‐of‐the‐art techniques further validates the superiority of our system. Our findings suggest that this framework can significantly benefit tomato cultivation by enabling timely and precise disease management. Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge.

Deep learning, a powerful subset of artificial intelligence, has emerged as a transformative tool in various domains, including agriculture, due to its ability to autonomously analyze complex data. This study focuses on the application of Convolutional Neural Networks (CNNs) for detecting and classifying diseases affecting tobacco leaves, a major concern in countries like India, one of the largest producers of tobacco. India produces an estimated 804 million kg of tobacco annually, with cultivation spanning half of its states. Traditional methods relying on manual inspection by farmers are time-consuming, subjective, and often inaccurate, leading to crop losses and reduced productivity. To overcome these challenges, this research developed a deep learning-based model leveraging real-time images of diseased tobacco leaves provided by farmers from the Prakasam district of Andhra Pradesh. A dataset comprising over 1000 annotated images depicting three distinct tobacco leaf diseases was curated for training and validation. The CNN model achieved an impressive accuracy of 95%, demonstrating its ability to accurately detect and classify diseases, enabling targeted interventions. The study not only underscores the efficiency of CNNs in revolutionizing disease detection but also highlights their role in reducing crop losses, improving yield, and supporting farmers with timely and unbiased disease diagnosis. By incorporating advanced image processing techniques and deep learning, this research provides a scalable, automated, and cost-effective solution to a pressing agricultural challenge, paving the way for improved crop management and sustainable farming practices.

The disease detection is vital to increase the productivity and quality of soybean cultivation and this detection is usually carried out in a laboratory, which is time consuming and costly. To overcome these issues, there is a growing demand for technologies that aim at a faster detection and classification of diseases. In this context, this work proposes the extraction of several patches from a leaf image and combining a convolutional neural network with a support vector machine, we present a complete model for the classification of soybean leaf diseases. In this approach, an image dataset with evidence of diseases commonly observed in soybean crops was analyzed and our experiments achieved precisions greater than 90%.

This study aims to classify rice leaf diseases using the Support Vector Machine (SVM) algorithm based on image processing and feature extraction. A total of 600 rice leaf images were collected, each representing one of five disease types: bacterial blight, leaf smut, leaf blast, brown spot, and hispa. The images underwent preprocessing, including resizing, background removal, and feature extraction using HSV and GLCM methods. Extracted features were then used to train and test an SVM classification model. The evaluation using confusion matrix showed an overall accuracy of 83%, with class-specific F1-scores ranging from 0.72 to 0.90. These results indicate that SVM is effective in classifying rice leaf diseases and can potentially assist farmers in early disease detection to reduce crop loss.

Application of machine learning techniques in rice leaf disease detection

The problem of rice diseases around the world make to damage and fall into a large number of rice. Caused by many of types, such as; fungi, Bakteri and Viruses. which are the main causes of rice disease affected to farmers. The classification of rice can be classified into several methods. In this research, image classification is used to classify the data set of rice leaf diseases, such as; Brown Spot Rice disease (BSR), Brown Spot Rice disease (BSR), Bacterial Leaf Blight disease (BLB), which is the rice leaf disease with severe outbreaks around Thailand. Moreover, image processing technology in the classification types of rice leaf disease, such as; Random forest classification algorithm, Decision tree classification algorithm, Gradient Boosting classification algorithm and Naïve-Baye classification algorithm, which is measured by the accuracy, precision and recall of each algorithms. The best result of performance in the image classification of rice leaf diseases is random forest algorithm equal to 69.44 percent.

In order to mitigate decreases in agricultural yield and production, the identification of diseases in plants assumes paramount importance. The agricultural sector has been employing various methodologies rooted in machine learning and image processing to address these challenges. This comprehensive analysis focuses specifically on the detection of diseases in rice plants by leveraging a diverse array of machine learning and image processing techniques with input images of infected rice plants. Furthermore, we delve into significant concepts pertaining to machine learning and image processing that aid in the identification and categorization of plant diseases. Various classification methods such as the k-Nearest Neighbor Classifier (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Genetic Algorithm (GA), and others find applications in agricultural research endeavors. The selection of an appropriate classification method assumes critical importance as the quality of the output is contingent on the input data. The classification of plant leaf diseases finds utility across multiple domains, including agriculture and biological research. This paper presents an extensive exploration of rice plant diseases, encompassing aspects such as image dataset size, preprocessing techniques, segmentation methods, and classifiers.

Plant leaves and crops play a crucial role as a primary food source globally, making significant contributions to dietary iron intake (9%) and energy consumption (23%) per capita in the Asian region. Bacterial, yeast, and other microbial diseases pose significant challenges to farmers as they detrimentally impact plant health and reduce crop productivity. The manual diagnosis of these diseases poses a considerable challenge, particularly in regions with a scarcity of professionals specializing in leaves and crop protection. Automating leaf disease detection and providing easily accessible decision-support resources are crucial for facilitating efficient leaf protection strategies and mitigating crop damage. Despite multiple classification methods for diagnosing leaf diseases, a secure and accurate approach that fulfills these requirements has not yet been identified. This paper presents an architectural framework called Lightweight Federated Transfer Learning (LFTL) that addresses the challenge of Leaf Disease Detection and Classification (LDDC) while ensuring data privacy limitations are upheld. A dataset consisting of leaf disease images has been compiled, characterized by an imbalance in the distribution of the diseases. The collection includes four conditions: bacterial decay, brown spot, blast, and tungro, corresponding image counts of 1695, 1551, 1711, and 1419, respectively. Following the preprocessing stage, the LFTL framework was tested using both Independent and Identically Distributed (IID) and non-IID datasets. The study commenced with an efficacy evaluation of the Convolutional Neural Network (CNN) and eight TL models in the LDDC. The framework’s performance was evaluated across different circumstances and compared to conventional and federated learning models. The study’s findings revealed that the LFTL framework outperformed traditional distributed deep-learning classifiers, thus demonstrating its efficacy in individual and multiple client scenarios.

This paper describes an experiment on Parkinsons disease classification using multiple classification algorithms for comparison. Parkinsons disease is a common neurological disorder, and early diagnosis and classification are important for the assessment of treatment and prognosis. Therefore, the research implications of this paper are clear. The classification algorithms used in the experiment include adaboost classification model, XGBoost classification model, logistic regression regression model, random forest plain Bayesian classification model, bp neural network and support vector machine. The experimental results show that adaboost classification model performs well when dealing with small sample data, XGBoost classification model performs well when dealing with large-scale datasets, and logistic regression regression model and random forest plain Bayesian classification model also have good performance. The bp neural network and support vector machine, on the other hand, perform poorly in terms of classification results and require a much larger dataset for support. These experimental results have important reference value for the classification and diagnosis of Parkinsons disease. Different classification algorithms are suitable for different dataset sizes and characteristics, so in practical applications, we can choose different classification algorithms according to the size and characteristics of the dataset to achieve the optimal classification effect. In conclusion, the results of this paper provide a reference for the classification and diagnosis of Parkinsons disease, as well as a guide for choosing appropriate classification algorithms. In the future, we can further expand the dataset size and use more classification algorithms for comparison to improve the accuracy and robustness of Parkinsons disease classification.

Efficient detection of foliar diseases in cassava (Manihot esculenta) is essential for sustaining crop productivity and ensuring food security, particularly in regions vulnerable to environmental stress. However, accurate identification remains a challenge due to the widespread occurrence of diseases such as Cassava Mosaic Disease (CMD), Cassava Bacterial Blight (CBB), and Cassava Brown Streak Disease (CBSD), which continue to threaten cassava yields. This study addresses two major obstacles in cassava disease classification—uneven image quality and imbalanced class distribution—by implementing Contrast Limited Adaptive Histogram Equalization (CLAHE) and the Synthetic Minority Over-sampling Technique (SMOTE). A publicly available dataset from the Cassava Leaf Disease Classification competition on Kaggle was used, and two pretrained convolutional neural networks, EfficientNetV2B2 and DenseNet169, were fine-tuned through transfer learning. The images were resized, enhanced using CLAHE, and augmented before being split into training, validation, and test sets. Both models were trained for 10 epochs using identical configurations. Results indicate that EfficientNetV2B2 achieved higher classification accuracy (88.1%) than DenseNet169 (86.4%), with CLAHE contributing a 2–3% improvement in accuracy. While these results are slightly lower than those reported in previous studies employing extended training durations and advanced techniques such as focal loss, the lightweight approach presented here proves effective under computational constraints. The findings demonstrate the feasibility of developing scalable and resource-efficient disease detection systems, especially for mobile or edge devices. Future research should focus on longer training schedules, advanced loss functions, and validation using field-acquired images to further improve model performance in real-world agricultural settings.

Plants are integral to human life, and so, plant health is important. Regularly monitoring of plant health and plant disease detections are important in property agriculture. In agriculture, the use of image processing techniques run by computers in solving agricultural problems is increasingly common, particularly in the classification and identification of crop disease. Such usage could preserve the technical and commercial well-being of agriculture. This study demonstrated the application of support vector machine and image processing-enabled approach to detect and classify Olive leaf disease. It comprises seven steps that begin with a presentation of a digital color picture of a sickly leaf, followed by the step of image denoising using mean function, image enhancing using CLAHE method, image segmentation using fuzzy C Means algorithm, image feature extraction using PCA, and disease detection and classification using PSO SVM, BPNN, and random forest algorithms. The results showed high accuracy of the proposed PSO SVM in Olive leaf disease classification and detection.

With the development of advanced image acquisition and processing techniques providing better biomarkers for the characterization of brain diseases, automatic classification of biomedical imaging becomes an important field in research. Since brain neural network is one of the most complex network, graph theory constitutes a promising approach to characterize its connectivity properties. In this work, we applied this technique to diffusion tensor imaging data acquired in multiple sclerosis (MS) patients in order to classify their clinical forms. Support Vector Machine (SVM) algorithm in combination with graph kernel were used to classify 65 MS patients in three different clinical forms. Results showed high classification performances using both weighted and unweighted connectivity graphs, the later being more stable, and less dependent to the pathological conditions.KeywordsMultiple sclerosisSVMGraph kernelClinical classificationStructural connectome

The classification of plant leaf diseases is a critical step in safeguarding global food security and ensuring sustainable agricultural productivity.Recent advances in artificial intelligence (AI) and deep learning have accelerated the development of automated disease detection systems, improving both efficiency and diagnostic accuracy.This study introduces a novel hybrid strategy that integrates Convolutional Neural Networks (CNN) with traditional machine learning classifiers, specifically Support Vector Machines (SVM) and XGBoost, to leverage the complementary strengths of both paradigms.Two hybrid variants, CNN+SVM and CNN+XGBoost, are proposed, alongside the use of Particle Swarm Optimization (PSO) for feature selection, enabling the extraction of the most informative features for robust classification.The dataset used for experimentation comprises 15,000 maize leaf images, covering four major diseases: Northern Corn Leaf Blight, Common Rust, Brown Spot, and Gray Leaf Spot, along with healthy samples.Experimental evaluations confirm that the hybrid models outperform standalone CNN classifiers, achieving superior accuracy and generalization.Specifically, CNN+XGBoost achieved 95.2% accuracy, 93% precision, 96.3% recall, and 94.3% F1-score, while CNN+SVM achieved 92.7% accuracy, 91.1% precision, 94% recall, and 92.2% F1-score.These results demonstrate the synergistic benefits of combining CNNs with classical classifiers, while PSO significantly enhanced classification outcomes by identifying highly discriminative features.This research contributes to the advancement of precision agriculture by presenting a scalable hybrid framework for maize disease detection, offering timely diagnostic insights for farmers and promoting sustainable crop management practices.