Mignoni Maria Eloisa, Honorato Aislan, Kunst Rafael, Righi Rodrigo, Massuquetti Angélica
Universidade do Estado de Mato Grosso Carlos Alberto Reyes Maldonado, Avenida das Garças, 1192, Centro, Nova Mutum, MT 78450-000, Brazil.
Centro Universitário Univag, Av. Dom Orlando Chaves, 2655, Bairro Cristo Rei, Várzea Grande, MT 78118-900, Brazil.
Data Brief. 2021 Dec 31;40:107756. doi: 10.1016/j.dib.2021.107756. eCollection 2022 Feb.
This article presents a dataset of insect-damaged soybean leaves. The capture of images was carried out on several soy farms, under realistic weather conditions, using two cell phones and a UAV. The dataset consists of 3 (three) folders with a total of 6,410 images. The dataset is divided into three categories: (I) healthy plants, (II) plants affected by caterpillars, and (III) images of plants damaged by . This dataset allows training and validation of machine learning models to diagnose, recognize, and classify soybeans affected by caterpillars or . The images can be processed according to the user's need since only the size was standardized during the pre-processing phase.
本文展示了一个昆虫损伤大豆叶片的数据集。图像采集是在几个大豆农场进行的，在真实天气条件下，使用两部手机和一架无人机。该数据集由3个文件夹组成，共有6410张图像。该数据集分为三类：（I）健康植株，（II）受毛虫影响的植株，以及（III）受[此处原文缺失内容]损伤的植株图像。这个数据集可用于训练和验证机器学习模型，以诊断、识别和分类受毛虫影响或[此处原文缺失内容]的大豆。由于在预处理阶段仅对尺寸进行了标准化，因此图像可根据用户需求进行处理。
