A neural network approach employed to classify soybean plants using multi-sensor images - de Souza, Flávia Luize Pereira [UNESP]; Shiratsuchi, Luciano Shozo; Dias, Maurício Acconcia; Barbosa Júnior, Marcelo Rodrigues; Setiyono, Tri Deri; Campos, Sérgio [UNESP]; Tao, Haiying (Springer Nature, 2025)
Counting soybean plants is a crucial strategy for assessing sowing quality and supporting high production. Despite its importance, the laborious nature of traditional assessment methods makes them unreliable and not scalable. Additionally, innovative image-based solutions have demonstrated limitations in detecting dense crops such as soybeans. Therefore, in this study, we developed neural network models to analyze a set of RGB and multispectral images and perform plant classification in a comprehensive dataset, which included data collected at three vegetative stages of soybean (VC, V1, and V2). Our results demonstrated high accuracy in classifying plants using either RGB (98%) or multispectral images (92%). A significant strength of this study is the ability to classify highly dense plants, without a trend for misclassification. Clearly, our findings provide stakeholders with a timely and effective approach to counting soybean plants, reducing labor and time, while increasing reliability.
Keywords: Dense crop; Multilayer perceptron; Multispectral images; Plant classification; Stand count