Low-cost hyper-spectral imaging system using a linear variable bandpass filter for agritech applications.

Abstract

Hyperspectral imaging for agricultural applications provides a solution for non-destructive, large-area crop monitoring. However, current products are bulky and expensive due to complicated optics and electronics. A linear variable filter was developed for implementation into a prototype hyperspectral imaging camera that demonstrates good spectral performance between 450 and 900 nm. Equipped with a feature extraction and classification algorithm, the proposed system can be used to determine potato plant health with ∼88 % accuracy. This algorithm was also capable of species identification and is demonstrated as being capable of differentiating between rocket, lettuce, and spinach. Results are promising for an entry-level, low-cost hyperspectral imaging solution for agriculture applications.

Plant phenotyping relevance

低コストのハイパースペクトル撮像カメラと特徴抽出・分類アルゴリズムを開発し、ジャガイモ植物の健全性を推定する方法を評価しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper describes hyperspectral plant datasets (salad leaves, potato late blight) and an SVM/PCA analysis pipeline, but no block contains any public data or code deposit, availability statement, or author-provided URL. No qualifying paper-specific public assets exist.

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