Work

Castor Beans Identification in Sugarcane Plantations Using Active Learning

Year: 2025

Type: article

Abstract: Castor beans have multiple applications but can become weeds in several crops. Manual identification is unfeasible in large plantations. Machine learning models can make identification efficient but require a lot of labeled data. Many images of a plantation would be similar and wouldn't improve the model's performance. Active learning (AL) allows labeling only relevant data, often surpassing the performance of models trained on entire datasets. In this work, we detect castor beans, with an AL method that uses self-supervised pretext tasks to separate data for labeling. Models based on pretext tasks presented a decrease in recall relative to the model trained in the whole dataset, which has 93%. We also trained a pseudo-task that separates data with a reasonable concentration of castor bean images. The pseudo-task classifier obtained a 92% recall, being trained in less than 1% of the dataset.

Source: Revista de Informática Teórica e Aplicada

DOI: 10.22456/2175-2745.143495

Authors: Juan Carlos Conceiç˜ao de Lima Sales, Mário De Araújo Carvalho, José Marcato Junior, Wesley Nunes Goncalves

Institution: Universidade Federal de Mato Grosso do Sul

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: diamond

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul, Universidade Federal de Mato Grosso do Sul