Entropy-Guided Feature Fusion Deep Learning Framework for Orange Fruit Disease Detection

Enhanced tomato leaf disease detection using multi-modal deep-handcrafted feature fusion with optimization-driven learning techniques

Mango Fruit Disease Classification Using Transformer-Based Feature Learning and Optimized Ensemble Modeling

A Deep Learning Model for Chili Pepper Fruit Shape Classification Using DenseNet-121 and CBAM

Traditional manual grading of fresh chili peppers suffers from inconsistent quality control and low efficiency. To meet the demand for accurate fruit shape recognition during the post-harvest stage, this study proposes an intelligent recognition method based on an improved DenseNet-121 network. This approach facilitates the application of machine vision in agricultural sorting equipment. DenseNet-121 serves as the backbone network. The Convolutional Block Attention Module (CBAM) is introduced to enhance feature focus on fruit shapes. A regularization strategy (Dropout = 0.3, weight decay = 1 × 10−4) and a cross-entropy loss function with label smoothing (LS = 0.1) are integrated to optimize decision boundaries. These configurations prevent the model from overfitting to hard training labels and yield a robust classification architecture. Experimental results demonstrate that the proposed model achieves a precision of 90.09%, a recall of 89.60%, an F1-score (the harmonic mean of precision and recall) of 89.53%, and an overall accuracy of 89.74%. The model contains 7.09 M parameters and requires a single-frame inference time of 7.35 ms. Comprehensive evaluations indicate that the proposed model achieves an optimal balance among environmental noise robustness, prediction accuracy, and computational efficiency. Consequently, by maintaining high fine-grained classification accuracy alongside a low memory footprint and rapid inference speed, the model demonstrates strong potential for real-time deployment on resource-constrained edge devices within actual agricultural optical sorting equipment.

Hybrid Feature Fusion and Bayesian-Optimized Ensemble Learning for Robust Citrus Disease Detection

The combination of hybrid feature fusion, NCA-based feature optimization, and Bayesian-optimized ensemble classification leads to enhanced discriminative power, greater robustness, and better generalization performance for the system in citrus disease identification in a real-world agricultural setting, as demonstrated by the results.

Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model

The identification of plant diseases plays a crucial role in sustaining agricultural productivity and minimizing economic losses. Traditional approaches, which often depend on visual assessment and the farmer’s experience, are typically inadequate for the timely recognition of infections, allowing diseases to progress and cause substantial damage. In overcoming these challenges, deep learning methods offer greater capability in solving complex classification problems than traditional machine learning algorithms. In this study, we propose a hybrid transformer-driven framework for high-precision disease detection on mango leaves. This approach combines mango leaf vein segmentation with transformer-based feature extraction. MaxViT and Swin models derive 512 and 768 features from each image, which are then combined to form a 1280-dimensional feature vector. The feature attention mechanism highlights the most informative components of the features, while the improved grey wolf optimizer reduces the increased dimensionality. 200 discriminative features were selected from the feature vector, and the decreasing features were classified using six machine learning classifiers. Experiments were performed on the MangoLeafBD dataset, which contains eight classes: seven diseases and a healthy class. The proposed MaxViT-Swin–IGWO hybrid framework achieved remarkable results, achieving 100% accuracy for the Linear Discriminant classifier and 99.98% accuracy for the Neural Network classifier. Performance analysis was accomplished using precision, recall, F1-score, dice, and ROC criteria. Furthermore, an ablation test was conducted to evaluate the impact of individual model variations on the preprocessing pipeline. The findings revealed that the proposed MaxViT Swin–IGWO hybrid framework detects mango leaf diseases with superior performance, outperforming both conventional and contemporary alternatives.

Automated Detection and Classification of Vegetable Leaf Disease using Machine Learning Techniques

The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.