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.

Global climate change has intensified prolonged droughts, making the development of sustainable agricultural technologies essential. Although potassium humate (KH) and Bacillus aryabhattai (BA) are recognized individually as effective biostimulants, their joint application represents an innovative, unexplored complementary strategy to mitigate drought stress in cowpea (Vigna unguiculata L. Walp.). This study aimed to evaluate the effects of these bio-inputs on mitigating water stress in the cowpea (Vigna unguiculata L. Walp.) cultivar ‘BRS Verdejante’. The experiment was conducted under greenhouse conditions using a completely randomized design in a 2 × 8 factorial arrangement, with two water regimes (100% and 50% crop evapotranspiration—ETc) and eight biostimulant combinations of KH and BA. While BA alone (T4) intensified osmotic adjustment via proline accumulation and APX activation, the combined treatment T5 (10 mg kg⁻¹ soil KH + 4 mL kg⁻¹ seed BA containing 1 × 10⁸ CFU mL⁻¹) achieved the best overall performance under drought (50% ETc). T5 effectively enhanced superoxide dismutase activity, preserved leaf relative water content, and reduced cell membrane electrolyte leakage by 42.6% compared to untreated stressed plants. These physiological adaptations protected reproductive organs, resulting in a 51.6% increase in pod number per plant under drought. The strategic co-application of low-dose potassium humate (10 mg kg⁻¹ soil) and B. aryabhattai (4 mL kg⁻¹ seed) effectively regulates osmotic and oxidative homeostasis, maintaining pod formation (+51.6%) and securing cowpea grain yield per plant under drought stress, thus serving as a promising strategy to mitigate drought stress in cowpea under controlled greenhouse conditions.

Cowpea is a crop of great importance worldwide. Consuming pods and green beans provides vitamins, minerals, and functional components for people with limited access to vegetables. The objective of this study was to use machine learning and artificial intelligence techniques to classify and distinguish cowpea lines based on images of their pods and green beans. Digital images of four heirloom landraces of the cowpea genotypes (Sempre Verde, Rabo de Tatu, Corujinha, and Paulistinha) and nine cultivars (BRS Novaera, 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 (Multi-Layer Perceptron) and SVM models performed best, particularly when integrated with the InceptionV3 embedder. In terms of classification efficiency, the evaluated algorithms achieved exceptional accuracy, reaching a Classification Accuracy (CA) of 0.999 for pod discrimination and 1.000 for green beans. For pod classification, these models achieved high performance metrics, including an Area Under the Curve (AUC) of 1.000. For green beans, the MLP maintained a high Area Under the Curve (AUC = 1.000) and better probabilistic calibration (lower Log-Loss) than the SVM. Digital image-based identification using machine learning is an efficient, non-destructive approach for morphological characterization and discrimination of cowpea genotypes, supporting high-throughput phenotyping applications.