BRACOL - A Brazilian Arabica Coffee Leaf images dataset to identification and quantification of coffee diseases and pests

The dataset was developed with the purpose to evaluate deep learning algorithms for segmentation and classification. it contains images of arabica coffee leaves affected by the main biotic stresses that affect the coffee tree: leaf miner, leaf rust, brown leaf spot, and cercospora leaf spot. The images were obtained using different smartphones (ASUS Zenfone 2, Xiaomi Redmi 5A, Xiaomi S2, Galaxy S8, and iPhone 6S). The leaves were collected at different times of the year in Santa Maria of Marechal Floreano in the mountains regions of the state of Espirito Santo, Brazil. The photos were taken from the abaxial (lower) side of the leaves under partially controlled conditions and placed on a white background. The acquisition of the images was done without much criterion to make the dataset more heterogeneous. A total of 1747 images of arabica coffee leaves were collected, including healthy leaves and diseased leaves, affected by one or more types of biotic stresses. The process of biotic stresses recognition for dataset labeling was assisted by an expert and performed with the captured images. From the obtained photos were generated two datasets. A dataset with the original images of the entire leaves and a second one containing only symptoms images. Details of each dataset are described in the following. Leaf dataset: It consists of the original images of the entire leaves. The images were labeled in relation to the predominant biotic stress of each leaf and its severity. Stress severity was calculated using the symptom and leaf segmentation mask using automatic image processing methods presented in Manso et al. (2019). For certain severity ranges, labels were assigned as follows: healthy (< 0:1%), very low (0.1% - 5%), low (5% - 10%), high (10% - 15%) and very high (> 15%). Symptom dataset: This dataset was created by cropping the isolated symptoms from the original images in a way that only single stress was present in each image. A total of 2147 symptom images were cropped. Each dataset is divided in training, validation and test. GL Manso, H Knidel, RA Krohling, JA Ventura (2019), A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust. arXiv preprint arXiv:1904.00742

本数据集专为评估用于图像分割与分类任务的深度学习算法而构建。数据集包含受咖啡树主要生物胁迫侵害的阿拉比卡咖啡叶图像，胁迫类型包括咖啡潜叶蛾、咖啡锈病、褐斑病及尾孢叶斑病。
图像采集使用多款智能手机完成，分别为ASUS Zenfone 2、Xiaomi Redmi 5A、Xiaomi S2、Galaxy S8及iPhone 6S。叶片采集工作于巴西圣埃斯皮里图州山区的马雷夏尔弗洛雷阿诺市圣玛丽亚地区开展，采集时间覆盖全年不同时段。
所有照片均在半可控条件下拍摄叶片的背（下）面，且将叶片置于白色背景之上。为提升数据集的异质性，图像采集未设置严苛标准。本次共收集1747张阿拉比卡咖啡叶图像，涵盖健康叶片与受一种或多种生物胁迫侵害的染病叶片。
数据集标注所需的生物胁迫识别流程由领域专家协助完成，基于采集的图像进行标注。基于原始采集图像共生成两个子数据集：其一为完整叶片原始图像数据集，其二仅包含病害症状图像。下文将分别详述两个数据集的细节。

叶片数据集：该数据集包含完整叶片的原始图像，标注依据为每片叶片的主要生物胁迫类型及其严重程度。胁迫严重程度通过症状与叶片分割掩码计算，采用Manso等人（2019）提出的自动化图像处理方法完成。针对不同严重程度区间，标注规则如下：健康（<0.1%）、极轻（0.1% - 5%）、轻度（5% - 10%）、重度（10% - 15%）及极重（>15%）。

症状数据集：该数据集通过裁剪原始图像中的孤立病害症状生成，要求每张裁剪后的图像仅包含单一胁迫类型。本次共裁剪得到2147张症状图像。

两个数据集均被划分为训练集、验证集与测试集。

参考文献：GL Manso, H Knidel, RA Krohling, JA Ventura (2019), 一款用于咖啡潜叶蛾与咖啡锈病检测与分类的智能手机应用, arXiv预印本arXiv:1904.00742

搜集汇总

背景与挑战

背景概述

BRACOL数据集是一个包含1747张巴西阿拉比卡咖啡叶图像的集合，用于识别和量化咖啡病害和害虫。数据集包含健康叶片和受多种生物胁迫影响的叶片图像，适用于深度学习算法的评估，特别是在图像分割和分类任务中。