Jun 2026· Agriculture· Vol 16, pp. 1413· 0 citations· 36 references
TL;DR
EA-HFA, an innovative framework based on deep neural networks that synergistically integrates an Entropy Attention mechanism alongside a Hierarchical Feature Alignment component, holds practical potential as an alternative solution for automated plant disease monitoring in precision farming.
Abstract
Tomato, as a globally vital economic crop, relies heavily on accurate disease recognition to safeguard food security. However, tomato leaf disease identification constitutes a classic fine-grained visual classification task characterized by minimal inter-class variance, spatially sparse lesion features, and complex background interference. These challenges hinder conventional deep learning models from precisely localizing critical discriminative regions. In response to the aforementioned challenges, we introduce EA-HFA, an innovative framework based on deep neural networks that synergistically integrates an Entropy Attention mechanism alongside a Hierarchical Feature Alignment component. Specifically, the Entropy Attention module leverages information-theoretic entropy to quantify pixel-wise predictive uncertainty, adaptively selecting high-confidence pixels to automatically focus the network on sparse yet highly discriminative lesion features. Concurrently, the Hierarchical Feature Alignment module imposes KL-divergence constraints on the temperature-scaled probability distributions across adjacent network layers, enforcing cross-scale consistency in the localization of discriminative regions. Evaluations conducted on the PlantVillage and AI Challenger 2018 benchmarks reveal that EA-HFA achieves Top-1 accuracies of 99.29% and 97.82%, respectively, yielding performance comparable to established deep learning architectures while maintaining a reasonable computational footprint. Furthermore, qualitative analyses indicate that the model tends to attend to minute lesion-relevant areas, providing a certain level of interpretability for its decision-making process. Thus, EA-HFA holds practical potential as an alternative solution for automated plant disease monitoring in precision farming.
The proposed framework provides an accurate and explainable approach for rice leaf disease diagnosis in smart agriculture applications and effectively integrates global semantic information with local disease-specific features for improved classification performance.
Mohamed N. Rahaman, Abdullah Al Mamun, Md. Kamal Hossen et al.· Computers· 0 citations
To address the challenges in maize leaf disease detection, including large variation in lesion scales, weak texture of small targets, strong background interference, limited recall ability for blurred lesions, and computational redundancy of conventional detection heads, this paper proposes a lightweight detection algorithm based on an improved YOLOv11n. First, a multi-scale global context kernel attention module is designed, which employs GCKA-bottleneck with large-kernel attention and residual connections to enhance the deep semantic representation of multi-scale lesions. Second, a GSConv-enhanced coordinate multi-receptive attention module is constructed, which combines coordinate position awareness and multi-scale depthwise convolution. Finally, a regression-enhanced depthwise-separable decoupled detection head is proposed to decouple classification and regression tasks, and introduces depthwise separable convolution and a distributed bounding box regression. On a public dataset containing four classes, the improved model achieves an mAP@0.5 of 85.36%, a recall of 83.32%, and a precision of 86.91%, which are 3.46, 27 2.94, and 2.38 percentage points higher than those of the original YOLOv11n, respectively. Meanwhile, GFLOPs and parameter count are reduced by 27.0% and 12.4%, respectively. The proposed algorithm strikes a favorable balance between accuracy, real-time performance, and lightweight design, providing a feasible technical support for field deployment in intelligent agricultural disease monitoring systems.
Potato leaf disease poses a significant threat to global food security, causing substantial crop losses that jeopardise agricultural productivity and farmers’ livelihoods worldwide. Existing automated detection frameworks suffer from several persistent limitations, including over-reliance on controlled benchmark datasets, narrow disease class coverage, exclusive use of spatial feature representations, absence of feature selection, and dependence on single-architecture end-to-end pipelines. To address these limitations, this paper proposes ComPo-Net, a novel lightweight ensemble framework that integrates three efficient CNN architectures—ResNet18, ShuffleNet, and MobileNetV2—for nine-class potato leaf disease detection and classification. Deep features are extracted from three intermediate layers of each network, with the Discrete Wavelet Transform applied for dimensionality reduction and cross-network fusion of the higher-dimensional layer features, capturing spectral–spatial information that purely spatial approaches cannot provide, while the remaining layer features are directly concatenated across networks. One-way Analysis of Variance (ANOVA) feature selection is subsequently applied to retain the most statistically significant features from the combined multi-scale, multi-network representation, and seven machine learning classifiers are systematically evaluated to identify the optimal classification strategy. The framework is assessed on a merged dataset of three publicly available benchmarks spanning both controlled and uncontrolled imaging environments, constituting a nine-class evaluation setting not previously addressed at this scale in the literature. ComPo-Net achieves an accuracy of 96.32%, an F1-score of 93.87%, an MCC of 0.9350, and AUC values exceeding 0.993 across all nine classes with Cubic SVM as the best-performing classifier. When compared against methods evaluated on the seven-class uncontrolled-environment dataset—the closest available task setting to ComPo-Net’s nine-class merged benchmark—ComPo-Net surpasses the best-performing comparable method by a margin of 6.45 percentage points, demonstrating the effectiveness of multi-scale ensemble feature extraction combined with spectral–spatial representation and principled feature selection for robust potato leaf disease detection under diverse real-world conditions.
Findings confirm that the hybrid CNN–Transformer architecture effectively enhances classification performance, robustness, and generalization in coffee leaf disease classification, with potential applications in precision agriculture and data-driven crop management.
