Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search

Abstract

Efficient crop disease management shows great promise in optimizing the agricultural industry. Accurate identification of infection levels is crucial for implementing effective and efficient disease treatments. However, accurately identifying and locating crop diseases in complex, unstructured field environments remain challenging. This necessitates the utilization of large volumes of annotated data. This research paper comprehensively evaluates deep transfer learning techniques for identifying the degree of rust disease infection in Vicia faba L. production systems. We curate a vast dataset comprising images captured under natural lighting conditions and at different growth stages of the crop under study. We propose a deep learning model based on Neural Architecture Search (NAS) specifically designed for early detection and accurate classification of disease levels in crops. We compare the performance of our proposed model with nine other deep learning models using transfer learning. Remarkably, transfer learning based on the NAS method achieves high classification accuracy, consistently exceeding 90.84% F1 scores. Moreover, all models exhibit short training times, requiring less than 3 hours. Among the evaluated models, our NAS-based model emerges as the top performer, highlighting the importance and effectiveness of this method in developing state-of-the-art models. It achieves a mean average precision of 94.10% and an impressive overall recall of 96.96%. These results significantly contribute to developing robust and accurate disease management strategies, paving the way for improved agricultural practices and increased crop yields. Our approach enables early disease detection and precise classification, leveraging deep transfer learning and facilitating timely interventions and optimized treatments. With the help of this study, we can now better utilize cuttingedge agricultural technology, paving the way for sustainable crop production in the future.

Plant phenotyping relevance

画像からソラマメのさび病感染レベルを推定・分類する深層学習モデルを開発し、複数モデルと比較検証しており、植物病害状態の表現型取得が中心です。

Code and data availability

The paper describes a curated dataset of 3,296 annotated Vicia faba rust disease images and a YOLO-NAS model, but provides no public deposit, repository, or availability statement for the dataset, annotations, code, or trained model checkpoints. No paper-specific public asset is actionable.

No evidence-backed public reproduction asset is currently recorded.