Black Gram Leaf Image Dataset for Disease Detection in Field Conditions

In countries of Southeast Asia, black gram (Vigna mungo) is a popular lentil for its taste, nutrient components, and health benefits. It has different types of uses, which increase its economic significance in the agricultural sector of most South Asian countries. The production of this lentil is troublesome due to its proneness to different kinds of diseases, and these diseases decrease the quality of lentils significantly, which hinders the economic profit of black gram cultivation. This lentil, produced from black gram seed, is also exported to international markets. For this reason, ensuring its sound quality is a crucial task. But leaf diseases are a great threat to its quality and cultivation. To prevent and control leaf diseases, an accurate diagnosis tool is highly important for black gram cultivation, which requires advanced datasets of leaf images.

This dataset is developed for advanced disease detection of black gram, which contains 587 field images of black gram. The size of all leaf images is 1024x1024 pixels, which supports almost all computer vision techniques. These images were captured from cultivation fields of black gram under the direct supervision of a plant pathology expert. Using the Make Sense (https://www.makesense.ai/) software, this dataset was developed by utilizing image annotation. A total of 4771 bounding boxes (rectangular) are present in this dataset, which were manually marked utilizing the Make Sense software. Among 4771 bounding boxes, 976 boxes were for LA (representative of healthy leaf), 2359 boxes for LB (representative of flea beetle damage), and 1436 for LC (representative of yellow mosaic disease) label. Besides disease diagnosis, analysis of leaf diseases can also be performed using this comprehensive dataset, as it contains high-quality images of black gram leaves.

Most of the existing image datasets are developed for the disease classification of black gram leaves. This dataset can be used for disease detection of black gram leaf, where multiple types of diseases can be identified from a single image. This method of disease diagnosis is more efficient than the traditional image classification approach. This dataset can play a crucial role in the advanced diagnosis of black gram leaf, which is highly important for sustainable progress in black gram cultivation.

Dataset Information:
# Size - 70.2 MB (Compressed)
# Size - 71.2 MB (Original Folder)
# Number of images - 587
# Annotation Type - Rectangular bounding boxes
# Total Bounding Boxes - 4771
# Labels - 3
1. LA
2. LB
3. LC
# Annotations Format
1. Single CSV file
2. VOC XML format
3. YOLO format

东南亚各国中，黑绿豆（black gram, Vigna mungo）凭借其风味、营养成分与健康益处，成为广受欢迎的小扁豆类作物。其多样的应用场景提升了它在多数南亚国家农业领域的经济价值。但该作物易受多种病害侵扰，导致籽粒品质大幅下降，进而制约了黑绿豆种植的经济效益。由黑绿豆种子产出的该豆类还会出口至国际市场，因此保障其品质是一项关键任务。而叶部病害是其品质与种植的重大威胁，因此开发精准的诊断工具对黑绿豆种植至关重要，而这类工具需要高质量的叶片图像数据集作为支撑。

本数据集专为黑绿豆病害精准检测而构建，共包含587张黑绿豆田间图像。所有叶片图像的分辨率均为1024×1024像素，可适配绝大多数计算机视觉技术。这些图像由植物病理学专家全程监督，在黑绿豆种植田间拍摄完成。本数据集通过图像标注流程构建，使用Make Sense（https://www.makesense.ai/）软件完成标注工作，共计生成4771个矩形边界框，全部通过该软件手动标注完成。在4771个边界框中，976个对应LA标签（代表健康叶片），2359个对应LB标签（代表跳甲虫害），1436个对应LC标签（代表黄花叶病）。除病害诊断外，本数据集还可用于叶部病害分析，因其包含高质量的黑绿豆叶片图像。

现有多数图像数据集均针对黑绿豆叶片的病害分类任务构建，而本数据集可用于黑绿豆叶片的病害检测，能够从单张图像中识别多种病害类型，这种病害诊断方式相较于传统图像分类方法效率更高。本数据集对黑绿豆叶片的精准诊断具有重要支撑作用，对推动黑绿豆种植的可持续发展意义重大。

数据集信息：
# 压缩后大小 - 70.2 MB
# 原始文件夹大小 - 71.2 MB
# 图像总数 - 587
# 标注类型 - 矩形边界框
# 总边界框数 - 4771
# 标签类别
1. LA
2. LB
3. LC
# 标注格式
1. 单一CSV文件格式
2. VOC XML格式
3. YOLO格式