Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms.

Abstract

Mildew of maize seeds may affect their germination rates and reduce crop quality. It is crucial to classify maize seeds efficiently and without destroying their original structure. This study aimed to establish hyperspectral datasets using hyperspectral imaging (HSI) of maize seeds with different degrees of mildew and then classify them using spectral characteristics and machine learning algorithms. Initially, the images were processed with Otus and morphological operations. Each seed's spectral features were extracted based on its coding, its edge, region of interest (ROI), and original pixel coding. Random forest (RF) models were optimized using the sparrow search algorithm (SSA), which is incapable of escaping the local optimum; hence, it was optimized using a modified reverse sparrow search algorithm (JYSSA) strategy. This reverse strategy selects the top 10% as the elite group, allowing us to escape from local optima while simultaneously expanding the range of the sparrow search algorithm's optimal solution. Finally, the JYSSA-RF algorithm was applied to the validation set, with 96% classification accuracy, 100% precision, and a 93% recall rate. This study provides novel ideas for future nondestructive detection of seeds and moldy seed selection by combining hyperspectral imaging and JYSSA algorithms based on optimized RF.

Plant phenotyping relevance

トウモロコシ種子のカビ状態をハイパースペクトル画像から非破壊的に分類する画像取得・特徴抽出・機械学習ワークフローが中心で、植物の病害状態を推定する方法研究である。

Code and data availability

The paper describes hyperspectral imaging of moldy maize seeds and JYSSA-optimized RF classification, but the Data Availability Statement says 'Not applicable.' No public dataset, image repository, code deposit, or model checkpoint is mentioned; no author-provided URL exists. The only allowed URL is the CC BY license,

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