Smart and accurate: A new tool to identify stressed soybean seeds based on multispectral images and machine learning models

Author(s)

Petronilio, Ana Carolina Picinini ; Mastrangelo, Clissia Barboza ; Batista, Thiago Barbosa ; de Oliveira, Gustavo Roberto Fonseca ; dos Santos, Isabela Lopes ; da Silva, Edvaldo Aparecido Amaral

• Document type: Journal article
• Source: SMART AGRICULTURAL TECHNOLOGY; v. 12, p. 10-pg., 2025-12-01.

Abstract

Extreme environmental conditions have been recurrent during the last few years and have impacted crop seed quality worldwide, mainly but not limited to, soybeans (Glycine max (L) Merrill). To overcome this, seed companies often demand innovative tools to address seed quality factors. Machine learning models based on multispectral imaging are a novel seed quality analysis approach. Thus, we hypothesize that segmenting stressed (those produced under conditions that are not favorable to the mother-plant) and non-stressed (produced under conditions favorable to the mother-plant) soybean seeds would be possible with this technology, opening a new opportunity for seed quality management and elucidating quality factors. Soybean seeds (cultivar BR/MG 46Conquista) were produced under water deficit and heat during maturation (from R5.5 onwards). Multispectral images were acquired from stressed and non-stressed seeds, and the reflectance, autofluorescence, physical properties, and chlorophyll parameters were extracted from the images. In parallel, we determined seed vigor. We designed machine learning models using multispectral imaging data based on three algorithms: neural network, support vector machine, and random forest. Our results demonstrated that the stressed seeds have spectral markers that enable their recognition. Concomitantly, these markers had a direct relationship with seed vigor. The machine learning models developed based on neural network algorithm showed the highest performance in segmenting stressed seeds (>= 90 % of accuracy, precision, recall, specificity and F1 score) in contrast to random forest- and support vector machine algorithm (>= 88 % of accuracy, precision, recall, specificity and F1 score). Here, we report a new approach for multispectral imaging with the potential to identify soybean seeds of lower vigor as a result of unfavorable environmental conditions during seed maturation. (AU)

• FAPESP's process: 17/50211-9 - Genetic and molecular basis of chlorophyll in seeds: a step forward to improve soybean adaptability to climate change
• Grantee: Edvaldo Aparecido Amaral da Silva
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/03802-4 - Multi-user equipment approved in grant 2017/15220-7: imaging system VideoMeterLab
• Grantee: Clíssia Barboza Mastrangelo
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 20/14050-3 - Peanut seed maturity disuniformity: physiological, chemical and transcriptomic approach
• Grantee: Gustavo Roberto Fonseca de Oliveira
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 19/06160-6 - Influence of the environment on the occurrence of green seeds, production of oil and protein in a soybean genotype with accumulation of ß-Carotene
• Grantee: Thiago Barbosa Batista
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 17/15220-7 - Non-destructive image analysis methods for seed quality evaluation
• Grantee: Clíssia Barboza Mastrangelo
• Support Opportunities: Research Grants - Young Investigators Grants
• FAPESP's process: 18/03793-5 - Multi-user equipment approved in grant 2017/15220-7: imaging system SeedReporter camera spectral & colour
• Grantee: Clíssia Barboza Mastrangelo
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 21/03209-4 - How are the transcription factors ABI3 and ABI5 associated with chlorophyll retention in soybean seeds?
• Grantee: Isabela Lopes dos Santos
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 18/01774-3 - Non-destructive image analysis methods for seed quality evaluation
• Grantee: Clíssia Barboza Mastrangelo
• Support Opportunities: Scholarships in Brazil - Young Researchers
• FAPESP's process: 23/00435-9 - Anthocyanin: a new biomarker for physiological quality of peanut seeds?
• Grantee: Gustavo Roberto Fonseca de Oliveira
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate