Assessment of clustering techniques to support the analyses of soybean seed vigor.

Abstract

Soy is the main product of Brazilian agriculture and the fourth most cultivated bean globally. Since soy cultivation tends to increase and due to this large market, the guarantee of product quality is an indispensable factor for enterprises to stay competitive. Industries perform vigor tests to acquire information and evaluate the quality of soy planting. The tetrazolium test, for example, provides information about moisture damage, bedbugs, or mechanical damage. However, the verification of the damage reason and its severity are done by an analyst, one by one. Since this is massive and exhausting work, it is susceptible to mistakes. Proposals involving different supervised learning approaches, including active learning strategies, have already been used, and have brought significant results. Therefore, this paper analyzes the performance of non-supervised techniques for classifying soybeans. An extensive experimental evaluation was performed, considering (9) different clustering algorithms (partitional, hierarchical, and density-based) applied to 5 image datasets of soybean seeds submitted to the tetrazolium test, including different damages and/or their levels. To describe those images, we considered 18 extractors of traditional features. We also considered four metrics (accuracy, FOWLKES, DAVIES, and CALINSKI) and two-dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbor embedding) for validation. Results show that this paper presents essential contributions since it makes it possible to identify descriptors and clustering algorithms that shall be used as preprocessing in other learning processes, accelerating and improving the classification process of key agricultural problems.

Plant phenotyping relevance

大豆種子画像から損傷とその程度を分類するため、複数のクラスタリング手法・特徴量・評価指標を比較検証しており、種子状態の表現・抽出手法が中心である。

Code and data availability

The paper's Data Availability statement points to the authors' public GitHub repository containing the soybean seed image datasets and feature files used in the clustering experiments. JFeatureLib is a generic third-party library, not a paper-specific asset.