Jul 2026· International Journal of Data Informatics and Intelligent Computing· Vol 5, pp. 1-20· 0 citations· 25 references
TL;DR
The present work provides an integrated, optimized deep learning detector with a user interface beneficial for precision farming that can help in the early identification of diseases on eggplants, resulting in increased yield.
Abstract
Early disease Classification will help reduce crop loss as well as increase agricultural productivity. A rapid and accurate deep learning-based framework to identify different diseases in eggplant on the marketplace level has been proposed in the research. Implementation and Training of an End-to-End Object Classification Based on YOLOv8. To train a custom multi-class data set, six target classes: Healthy Leaf, White Mold Disease, Leaf Spot Disease, Wilt Disease, Mosaic Virus Disease, and Insect Pest Disease. Initial work was done in developing the object detectors. We used standard metrics like accuracy, precision, recall, and F1-score to evaluate the performance of the model. When it came to finding Plant Leaf Disease (PLD), traceable configurations were revealed from configuration tuning among the combinations of hyperparameters, which converged at equal measurement intervals on a curve between Classification accuracy and computation efficiency from screened candidate architectures along ranges determined by performance metrics defined for detecting plant diseases using only above-ground debris as input sources. With stable convergence during the training phase, the model YOLOv8m had an accuracy of 96.84%, a precision of 96.85%, a recall of 96.84%, and an F1 score of 96.84%. The model that has been trained was deployed with the help of a web application named Streamlit, so that it could be used in practical procedures where disease can be detected if we upload an image. That means the system is robust and operates effectively in the wild as opposed to ideal test conditions, which lends itself well to agricultural usage. The present work provides an integrated, optimized deep learning detector with a user interface beneficial for precision farming that can help in the early identification of diseases on eggplants, resulting in increased yield.
Background: Legumes, such as beans, are important in worldwide agriculture because of their nutritional value and soil-enriching qualities. However, bean crops are susceptible to diseases such as angular leaf spot and rust, which may reduce production and quality. Disease identification that is both effective and timely is essential to crop health and output. Traditional diagnostic approaches are often labor-intensive and susceptible to inaccuracy. Recent advances in deep learning (DL) provide interesting possibilities for automating disease categorization, possibly improving accuracy and efficiency. Methods: This study evaluates and compares the performance of two deep learning architectures, ResNet50 and VGG19, for the classification of bean leaf diseases. The dataset, sourced from Kaggle, comprises 1295 images categorized into three classes: Angular Leaf Spot, Rust and Healthy. Both systems relied on pre-trained ImageNet weights, with adjustments customized to the classification objective. The models were trained for 25 epochs and their performance was assessed based on overall accuracy. Result: The performance of the models is evaluated in terms of the confusion matrix, classification report and ROC(AUC) curves. The ResNet50 model achieved an overall accuracy of 93.75%, while the VGG19 model attained an accuracy of 91.41%. The findings indicate that ResNet50 performs better than VGG19 in terms of classification accuracy. This work demonstrates ResNet50’s performance for bean leaf disease classification tasks, providing important insights for future research and practical applications in agricultural disease control.
Yu-Yan Xu, Hui-Qing Chen, Qing-Mei Lin· Legume Research An Internati...· 0 citations
Agricultural productivity and food security are heavily impacted by plant diseases, and thus there is a high demand for accurate and automated plant disease detection that can be achieved by applying deep learning techniques. This research proposes a Multi-Model Ensemble Method Based on Deep Learning for multi-plant disease detection using ResNet50 to improve classification performance across multiple crop varieties. The proposed framework takes five important categories of plants into consideration including tomato, potato, grape, apple and maize, and 10 classes of healthy and diseased plants are generated from the PlantSeg dataset. The Anaconda platform was used along with Python to create a development environment that allows data preprocessing, augmentation, training and testing to be implemented efficiently. The proposed ensemble framework combines the feature extraction power of ResNet50 with several deep learning classifiers so as to obtain a good identification performance at different resolutions and environments. The proposed model performance is tested with the following metrics Accuracy, Precision, Inference Time, and Resolution quality and compared with MobileNetV2, YOLOv8 and the baseline CNN models. Experimental results show that the proposed ensemble ResNet50 framework achieves an accuracy of 98.7% and precision of 98.3%, which is about 6.4%, 4.8%, and 9.2% higher than that of MobileNetV2, YOLOv8, and CNN respectively. Moreover, the proposed method achieves high resolution disease localization capability with an inference time improvement of almost 18% compared with YOLOv8. The proposed system greatly improves the detection accuracy of the early stage and the calculation speed of the system, which is very suitable for smart agriculture applications and real-time monitoring of the health status of crops.
A modified AlexNet architecture for classifying field-captured tomato leaf images into seven disease categories was developed and generally focused on symptom-bearing leaf regions, whereas target spot was the most difficult category to classify.
Debabrat Bharali, Kanak C. Bora, Rashel Sarkar et al.· Journal of Scientific Resear...· 0 citations
This study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny, a modern convolutional architecture with a Global Response Normalization mechanism, combined with an ensemble Stratified K-Fold Cross Validation strategy (K=6) to improve generalization on real-world field data.
This study presents a highly optimized, end-to-end deep learning pipeline leveraging transfer learning via the EfficientNet-B0 architecture for multi-class mango leaf disease classification, establishing a robust and computationally efficient baseline for automated precision pathology.
Jodell R. Bulaclac, Joseph R. Del Carmen· International Journal of Inn...· 0 citations
Two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture are presented, showing that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
