The research, conducted by Saloua Lagnaoui and Khalid Haddouch of the Laboratory of Applied Sciences and Emerging Technologies at ENSA, Sidi Mohamed Ben Abdellah University in Fez, Morocco, together with Zakariae En-naimani of the Laboratory Computer Science, Artificial Intelligence and Cyber Security at ENSET, Hassan II University of Casablanca, introduces a fundamentally different way of thinking about redundancy. Rather than waiting until training is complete and then surgically removing filters, or randomly resetting them in the hope that diversity will emerge, the proposed method models the relationships between filters dynamically during training itself. It continuously measures how similar the responses of different filters are and applies similarity-aware weighting that dampens redundant feature responses before they can dominate the network’s internal representations.

To understand why this matters, it helps to consider what filters actually do inside a convolutional neural network. Each filter is a small pattern detector that slides across an image, responding strongly to particular visual features such as edges, textures, color gradients, or the characteristic lesions and discolorations that signal disease on a leaf. In an ideal network, every filter would specialize in a distinct feature, collectively forming a rich and diverse vocabulary of visual descriptors. In practice, however, gradient-based training frequently drives multiple filters toward nearly identical behavior. When that happens, the network wastes computational capacity re-learning the same feature, its effective representational power shrinks, and its convergence during training can become unstable as redundant filters compete for the same gradient signals.

Previous approaches to this problem have generally fallen into two camps. Pruning techniques, such as those that rank filters by importance and remove the least useful ones after or during training, treat redundancy as something to be excised. Stochastic resetting methods, which randomly reinitialize filters mid-training, gamble that randomness will restore diversity. Both strategies have drawbacks: pruning can be brittle and often requires careful tuning and retraining, while stochastic approaches introduce nondeterminism that makes results harder to reproduce and optimize. The CNN-SA-RFR framework, by contrast, is deterministic and adaptive. By embedding a cosine similarity measure into a self-attention mechanism, it quantifies the angular alignment between filter responses and uses that information to reweight the network’s feature maps continuously, suppressing responses that duplicate what other filters already capture.

The choice of cosine similarity is technically significant. Unlike Euclidean distance, which is sensitive to the magnitude of activation vectors, cosine similarity measures the orientation between vectors, making it a natural gauge of whether two filters are responding to the same underlying pattern regardless of response strength. Combined with self-attention, a mechanism popularized by the transformer architecture that allows a model to weigh the relationships among different elements of its input, this creates a layer that is acutely aware of its own internal redundancy. The attention weights effectively act as a soft, learned form of redundancy control: filters whose responses are highly similar to others receive diminished influence, while distinctive, informative features are amplified and allowed to propagate through the network.

The authors evaluated their framework on two publicly available benchmark datasets: Plant Pathology 2020, released for the FGVC7 Kaggle competition, and the Plant Disease Recognition dataset, also hosted on Kaggle. These datasets present genuinely difficult classification challenges, requiring models to distinguish healthy leaves from those affected by multiple diseases under varying lighting conditions, viewing angles, and stages of disease progression. The results were striking. The proposed method achieved up to 78 percent accuracy on the Plant Pathology 2020 dataset and 94 percent on the Plant Disease Recognition dataset, outperforming baseline convolutional neural network models on both benchmarks.

Beyond raw accuracy, the study reports improvements in three additional dimensions that matter greatly for practical deployment. Feature diversity increased, indicating that the network’s filters genuinely specialized rather than duplicating one another, which suggests the similarity-aware weighting is doing exactly what it was designed to do. Convergence stability also improved, meaning training runs were less prone to the oscillations and plateaus that plague redundant networks, a benefit that translates directly into reduced engineering time and compute cost. Together, these gains position the method as a practical optimization strategy rather than a laboratory curiosity, particularly for agricultural applications where models may need to run on edge devices with limited processing power.

The work builds on a growing body of research at the intersection of attention mechanisms and efficient convolutional networks. Self-attention has already proven its value in plant disease recognition, with prior studies showing that attention-enhanced convolutional models can better localize disease symptoms and ignore background clutter. At the same time, research into kernel and filter redundancy reduction has explored low-rank expansions, knowledge distillation, and differentiable pruning masks. What distinguishes CNN-SA-RFR is the fusion of these two threads: it uses attention not to improve the network’s view of the input image, but to improve the network’s view of itself, continuously auditing its own filters for redundancy and correcting it in real time.

The implications extend well beyond plant pathology. Filter redundancy is a general problem in convolutional architecture design, affecting applications from medical imaging to autonomous driving, and any deterministic, training-time method that mitigates it without sacrificing accuracy is of broad interest to the machine learning community. For agriculture specifically, the stakes are high. Plant diseases cause substantial global crop losses each year, and smartphone-based diagnostic tools powered by efficient CNNs are increasingly seen as a frontline defense for farmers who lack access to expert agronomists. Leaner, more diverse, and more stable networks make such tools cheaper to train, faster to run, and more reliable in the field.

The Moroccan team’s findings also carry a methodological lesson for the wider deep learning community: architecture and training procedure need not be designed independently. By making redundancy awareness an intrinsic property of the network layer rather than an external post-processing step, CNN-SA-RFR suggests a future in which models are not merely trained and then optimized, but are self-optimizing from the first forward pass. The datasets used in the study are publicly available, and the authors note that the code can be obtained from the corresponding author, opening the door for other researchers to replicate, refine, and extend the approach across new domains and larger architectures.

Subject of Research: A cosine similarity-based self-attention framework for reducing filter redundancy in convolutional neural networks applied to plant disease classification

Article Title: CNN-SA-RFR: A cosine similarity-based self-attention approach for reducing filter redundancy in CNNs for plant disease classification

Article References: Lagnaoui, S., En-naimani, Z., & Haddouch, K. (2026). CNN-SA-RFR: A cosine similarity-based self-attention approach for reducing filter redundancy in CNNs for plant disease classification. Multimedia Tools and Applications, 85(10), Article 764. https://doi.org/10.1007/s11042-026-21921-3

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21921-3

Keywords: convolutional neural networks, self-attention, filter redundancy, cosine similarity, plant disease classification, deep learning, computer vision, agricultural AI, model efficiency, feature diversity, Plant Pathology 2020, neural network pruning