Hierarchical Feature Fusion With Inception V3 for Multiclass Plant Disease Classification

Abstract

Artificial Intelligence (AI) and Deep Learning (DL) are revolutionizing plant disease detection, which is crucial for mitigating crop loss, improving food security, and enhancing yield. Traditional manual methods, such as field visits for disease diagnosis, are labor-intensive, time-consuming, and costly, highlighting the demand for automated, real-time applications. However, plant disease classification faces challenges such as class variations, cluttered backgrounds, lesion scale variations, and the need for robust models. Several Convolutional Neural Network (CNN)-based frameworks have been proposed to tackle these challenges, but they often suffer from limitations such as fixed-size kernels and inefficient feature utilization across deeper layers. These limitations lead to feature loss during the initial stages of feature extraction, reducing the model's overall effectiveness in multi-class plant disease classification. To address these issues, we introduce HFFIncep-Net, a novel Deep Learning framework that fuses hierarchical features with the power of the Inception V3 architecture. The Hierarchical Feature Fusion (HFF) stem extracts multiscale features, which are fused at multiple levels within the network. This enhances feature representation and mitigates information loss in the initial layers. Incorporating two Inception V3 blocks, the model captures a diverse set of features at different scales, further improving classification performance. Additionally, the inclusion of a Global Average Pooling (GAP) layer reduces computational complexity while maintaining high accuracy. To enhance training stability and performance, we employ the Swish activation function, which facilitates smoother gradient propagation during backpropagation. When evaluated on the PlantVillage and Cassava Leaf Disease (CLD) datasets, HFFIncep-Net achieves 94.61% precision, 94.31% recall, 94.31% F1 score, and 97.80% accuracy, outperforming existing CNN-based methods. These results demonstrate that HFFIncep-Net is an effective solution for early-stage, multi-class plant disease classification, with significant potential for practical deployment in agricultural applications.

Plant phenotyping relevance

植物画像から病害状態を分類する深層学習手法の開発とデータセット上での性能評価が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper uses the public PlantVillage and Cassava Leaf Disease (CLD) datasets, but these are pre-existing community benchmarks, not author-deposited paper-specific assets. No author code, trained models, or data deposit with a public URL is mentioned anywhere in the supplied blocks.

No evidence-backed public reproduction asset is currently recorded.