Paper record

Phenotyping of genotypes and diagnosis of water status in cowpea using thermographic images and machine learning

Semako Ibrahim Bonou · Rosana Araujo Martins Lucena · Agda Malany Forte de Oliveira · Igor Eneas Cavalcante · Tulio William da Silva Gonçalves · Priscylla Marques de Oliveira Viana · Guilherme Felix Dias · Rener Luciano de Souza Ferraz · André Allison Rodrigues da Silva · José Dantas Neto · Carlos Alberto Vieira de Azevedo · Alberto Soares de Melo

Research Square · 15 Jul 2026 · 10.21203/rs.3.rs-9609259/v1

Abstract

Abstract Purpose The variability in tolerance to water stress among cowpea genotypes requires fast and accurate phenotyping methods. The integration of infrared thermography with artificial intelligence is emerging as a robust solution for large-scale, non-invasive monitoring. Thus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images. Methods Ten genotypes (five varieties: Corujinha – G1, Paulistinha – G2, Sempre Verde – G3, Pintado – G4, and Rabo de Tatu – G5) and the cultivars BRS Novaera – G6, BRS Pajeú – G7, IPA 206 – G8, BRS Tapaihum – G9, and BRS Miranda – G10) were subjected to four water regimes (25%, 50%, 75%, and 100% of ETc). Thermographic images were collected at the V3 and R2 stages and processed using Deep Learning architectures (InceptionV3, SqueezeNet, VGG16, and VGG19) to extract features (vectorization). The k-NN, Decision Tree, Random Forest, SVM, Neural Network, and AdaBoost algorithms were trained to classify stress levels and genotypes. Results The vegetative stage (V3) proved more effective for diagnosis than the reproductive stage (R2), exhibiting more stable thermal signatures. The SVM algorithm, combined with the VGG16 vectorizer, achieved the best performance, achieving an accuracy greater than 0.910 in classifying water regimes. The landrace varieties exhibited thermal patterns distinct from those of the improved cultivars, enabling high-precision genotypic identification. Conclusions The proposed approach demonstrates that infrared thermography, combined with machine learning models, is an effective tool for high-throughput digital phenotyping, optimizing the selection of drought-tolerant materials and irrigation management in precision agriculture.

Code and data availability

The supplied blocks describe thermographic image collection and Orange Data Mining machine learning analysis of cowpea genotypes, but contain no data availability statement, public dataset deposit, author code repository, or trained model release. The only URLs present are the preprint DOI and a cited reference DOI (Dí

No evidence-backed public reproduction asset is currently recorded.