Identification of Cowpea Genotypes by Machine Learning Using Digital Images of Pods and Green Beans

Abstract

Cowpea is a crop of great importance worldwide, which is why many heirloom varieties and improved cultivars are explored. Consuming pods and green beans provides vitamins, minerals, and functional components for people with limited access to vegetables. The pods and green beans of these materials have intrinsic characteristics that distinguish them. Therefore, the objective was to adjust machine learning models to identify cowpea from digital images of pods and green beans using artificial intelligence techniques. Digital images of four heirloom Creole of the cowpea genotypes (Sempre Verde, Rabú de tatu, Corujinha, and Paulistinha) and nine cultivars (BRS No-vaera, BRS Olhonegro, BRS Verdejante, BRS Exuberante, BRS Pajeú, BRS Miranda, IPA 206, BRS Tapaihum, and BRS Pingo de Ouro) were processed using four deep learning architectures for feature extraction (vectorization): InceptionV3, SqueezeNet, VGG16, and VGG19. Six machine learning algorithms were evaluated: K-Nearest Neighbors (KNN), Decision Tree, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The MLP (Artificial Neural Network) and SVM models, particularly when integrated with the InceptionV3 embedder, demonstrated superior performance. For pod classification, these models achieved near-perfect performance, with Area Under the Curve (AUC) and Classification Accuracy (CA) of 1.000. For green beans, the MLP maintained high accuracy (CA = 0.977) and better probabilistic calibration (lower Log-Loss) than the SVM. Digital image-based identification associated with machine learning is an efficient, non-destructive approach for the morphological characterization and discrimination of cowpea genotypes, supporting high-throughput phenotyping (HTP) applications.

Plant phenotyping relevance

デジタル画像と機械学習による莢・サヤインゲンの形態的特徴抽出と遺伝子型識別が研究の中心であり、ハイスループット植物表現型解析への応用を明示している。

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