Search Results (11)

Apple is an important fruit worldwide, but it is quite susceptible to various diseases. In particular, apple scab disease (Venturia Inaequalis) is a common fungal infection that causes serious yield losses in apple production. This disease causes spots on both leaves and fruits, negatively affecting product quality and marketability. Early diagnosis and management of apple diseases are critical to increase productivity in apple production. Traditional methods are usually time-consuming and costly; therefore, image processing and artificial intelligence technologies have become important tools in disease detection. In this study, a new approach is developed for the classification of healthy and scab apples by combining image processing, deep learning and optimization methods. First, the dataset is enriched using data augmentation techniques such as rotation, mirroring, zooming, shifting, brightness adjustment, and noise addition. Then, the images are analyzed with Shearlet Transform (ST), and frequency and spatial features are extracted in detail. The features obtained from the ST are reconstructed with the inverse transformation, and the original images are given as inputs to deep learning architectures, specifically AlexNet, VGG-16 and ResNet-18. In each model, deep features are extracted to classify healthy and scab apple images, and a feature pool is created by combining these features. The selection process of features that will increase performance in the classification process is carried out with the Red Deer Optimization (RDO) algorithm. This algorithm, inspired by the natural life cycle of male deer, includes the steps of determining the leader deer, creating a harem, mating and selecting the next generations. By selecting the best male leaders and optimizing the mating process, the algorithm ensures that the most effective feature combinations are chosen to enhance classification performance. As a result, this hybrid method presents an innovative approach to accurately classifying healthy and scab apple images, contributing to more efficient and reliable disease detection in apple production. Full article

One of the world’s most important economic crops, apples face numerous disease threats during their production process, posing significant challenges to orchard management and yield quality. To address the impact of complex disease characteristics and diverse environmental factors on detection accuracy, this study proposes a multimodal parallel transformer-based approach for apple disease detection and classification. By integrating multimodal data fusion and lightweight optimization techniques, the proposed method significantly enhances detection accuracy and robustness. Experimental results demonstrate that the method achieves an accuracy of 96%, precision of 97%, and recall of 94% in disease classification tasks. In severity classification, the model achieves a maximum accuracy of 94% for apple scab classification. Furthermore, the continuous frame diffusion generation module enhances the global representation of disease regions through high-dimensional feature modeling, with generated feature distributions closely aligning with real distributions. Additionally, by employing lightweight optimization techniques, the model is successfully deployed on mobile devices, achieving a frame rate of 46 FPS for efficient real-time detection. This research provides an efficient and accurate solution for orchard disease monitoring and lays a foundation for the advancement of intelligent agricultural technologies. Full article

The present study seeks to improve the accuracy and reliability of disease identification in apple fruits and leaves through the use of state-of-the-art deep learning techniques. The research investigates several state-of-the-art architectures, such as Xception, InceptionV3, InceptionResNetV2, EfficientNetV2M, MobileNetV3Large, ResNet152V2, DenseNet201, and NASNetLarge. Among the models evaluated, ResNet152V2 performed best in the classification of apple fruit diseases, with a rate of 92%, whereas Xception proved most effective in the classification of apple leaf diseases, with 99% accuracy. The models were able to correctly recognize familiar apple diseases like blotch, scab, rot, and other leaf infections, showing their applicability in agriculture diagnosis. An important by-product of this research is the creation of a web application, easily accessible using Gradio, to conduct real-time disease detection through the upload of apple fruit and leaf images by users. The app gives predicted disease labels along with confidence values and elaborate information on symptoms and management. The system also includes a visualization tool for the inner workings of the neural network, thereby enabling higher transparency and trust in the diagnostic process. Future research will aim to widen the scope of the system to other crop species, with larger disease databases, and to improve explainability further to facilitate real-world agricultural application. Full article

Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits. Full article

Plant disease identification is a crucial issue in agriculture, and with the advancement of deep learning techniques, early and accurate identification of plant diseases has become increasingly critical. In recent years, the rise of vision transformers has attracted significant attention from researchers in various vision-based application areas. We designed a model with an encoder–decoder architecture to efficiently classify plant diseases using a transfer learning approach, which effectively recognizes a large number of plant diseases in multiple crops. The model was tested on the “PlantVillage”, “FGVC8”, and “EMBRAPA” datasets, which contain leaf information from crops such as apples, soybeans, tomatoes, and potatoes. These datasets cover diseases caused by fungi, including rust, spot, and scab, as well as viral diseases such as leaf curl. The model’s performance was rigorously evaluated on datasets, and the results demonstrated its high accuracy. The model achieved 99.9% accuracy on the “PlantVillage” dataset, 97.4% on the “EMBRAPA” dataset, and 91.5% on the “FGVC8” dataset, showcasing its competitiveness with other state-of-the-art models. This study provides a robust and reliable solution for plant disease classification and contributes to the advancement of precision agriculture. Full article

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8. Full article

Plant and fruit diseases significantly impact agricultural economies by diminishing crop quality and yield. Developing precise, automated detection techniques is crucial to minimize losses and drive economic growth. We introduce YOLO-AppleScab, integrating Content-Aware ReAssembly of FEature ($\mathbb{C}\mathbb{A}\mathbb{R}\mathbb{A}\mathbb{F}\mathbb{E}$) architecture into YOLOv7 for enhanced apple fruit detection and disease classification. The model achieves impressive metrics: F1, recall, and precision of 89.75%, 85.20%, and 94.80%, and a mean average precision of 89.30% at $IoU$ 0.5. With 64% efficiency, this model’s integration with YOLOv7 improves detection, promising economic benefits by accurately detecting apple scab disease and reducing agricultural damage. Full article

Plant diseases, if misidentified or ignored, can drastically reduce production levels and harvest quality. Technology in the form of artificial intelligence applications has the potential to facilitate and improve the disease identification process, which in turn will empower prompt control. More specifically, the work in this paper addressed the identification of three common apple leaf diseases—rust, scab, and black rot. Twelve deep transfer learning artificial intelligence models were customized, trained, and tested with the goal of categorizing leaf images into one of the aforementioned three diseases or a healthy state. A dataset of 3171 leaf images (621 black rot, 275 rust, 630 scab, and 1645 healthy) was used. Extensive performance evaluation revealed the excellent ability of the transfer learning models to achieve high values (i.e., >99%) for F1 score, precision, recall, specificity, and accuracy. Hence, it is possible to design smartphone applications that enable farmers with poor knowledge or limited access to professional care to easily identify suspected infected plants. Full article

The intelligent identification and classification of plant diseases is an important research objective in agriculture. In this study, in order to realize the rapid and accurate identification of apple leaf disease, a new lightweight convolutional neural network RegNet was proposed. A series of comparative experiments had been conducted based on 2141 images of 5 apple leaf diseases (rust, scab, ring rot, panonychus ulmi, and healthy leaves) in the field environment. To assess the effectiveness of the RegNet model, a series of comparison experiments were conducted with state-of-the-art convolutional neural networks (CNN) such as ShuffleNet, EfficientNet-B0, MobileNetV3, and Vision Transformer. The results show that RegNet-Adam with a learning rate of 0.0001 obtained an average accuracy of 99.8% on the validation set and an overall accuracy of 99.23% on the test set, outperforming all other pre-trained models. In other words, the proposed method based on transfer learning established in this research can realize the rapid and accurate identification of apple leaf disease. Full article

Diseases in apple orchards (rot, scab, and blotch) worldwide cause a substantial loss in the agricultural industry. Traditional hand picking methods are subjective to human efforts. Conventional machine learning methods for apple disease classification depend on hand-crafted features that are not robust and are complex. Advanced artificial methods such as Convolutional Neural Networks (CNN’s) have become a promising way for achieving higher accuracy although they need a high volume of samples. This work investigates different Deep CNN (DCNN) applications to apple disease classification using deep generative images to obtain higher accuracy. In order to achieve this, our work progressively modifies a baseline model by using an end-to-end trained DCNN model that has fewer parameters, better recognition accuracy than existing models (i.e., ResNet, SqeezeNet, and MiniVGGNet). We have performed a comparative study with state-of-the-art CNN as well as conventional methods proposed in the literature, and comparative results confirm the superiority of our proposed model. Full article

Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed. Full article