Reducing catastrophic forgetting in CNNs for plant stress classification using continual learning.

Abstract

Plants face a range of biotic and abiotic stresses that reduce yield, and in agricultural countries like Bangladesh, manual detection of these stresses remains slow and error-prone. Convolutional Neural Networks (CNNs) classify plant leaves accurately, but they suffer from catastrophic forgetting when trained on tasks sequentially. This is a major obstacle in real agricultural settings, where new crops and stress conditions arrive over time. Existing continual learning (CL) studies in this domain rely on relatively heavy backbones, leaving open the question of whether lightweight CL pipelines can retain prior-task knowledge under realistic resource constraints. We investigate this question by integrating two established CL methods, Elastic Weight Consolidation (EWC) and Learning without Forgetting (LwF), into EfficientNet-B0, a backbone with only 5.3M parameters. The setup is evaluated on the Nutrispace cucurbit nutritional deficiency dataset, where three plant species (ash gourd, bitter gourd, and snake gourd) are treated as three sequential tasks, each with the same three classes: healthy, nitrogen deficiency, and potassium deficiency. Without continual learning, accuracy on the earliest task collapses to 30% by the end of training. EWC preserves over 61% accuracy on prior tasks while reaching 98% on the final task, and LwF reaches 98% on the final task with slightly lower retention on earlier ones. Pairwise Welch's t-tests confirm that both methods significantly outperform the baseline ([Formula: see text]) and that EWC retains prior-task knowledge significantly better than LwF ([Formula: see text]). These results show that lightweight CNNs paired with established CL techniques offer a workable path for plant stress classification in resource-constrained agricultural AI.

Plant phenotyping relevance

植物葉画像から栄養欠乏・健全状態を分類するCNNに、継続学習手法を組み込んで性能保持を評価しており、植物ストレス状態の取得・推定手法が研究の中心である。

Code and data availability

The paper uses the publicly available Nutrispace cucurbit nutritional deficiency dataset (Mendeley Data, DOI 10.17632/t2k7z4wsj4.2, reference 27), which is a paper-specific public phenotype image asset. However, no authors' public URL for this dataset (or for any analysis code, trained models, or supplements) appears;

No evidence-backed public reproduction asset is currently recorded.