A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology

The relationship between the plants and the environment is multitudinous and complex. They help in nourishing the atmosphere with diverse elements. Plants are also a substantial element in regulating carbon emission and climate change. But in the past, we have destroyed them without hesitation. For the reason that not only we have lost a number of species located in them, but also a severe result has also been encountered in the form of climate change. However, if we choose to give them time and space, plants have an astonishing ability to recover and re-cloth the earth with varied plant and species that we have, so recently, stormed. Therefore, a contribution has been made in this work towards the study of plant leaf for their identification, detection, disease diagnosis, etc. Twelve economically and environmentally beneficial plants named as Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar have been selected for this purpose. Leaf images of these plants in healthy and diseased condition have been acquired and alienated among two separate modules.

Principally, the complete set of images have been classified among two classes i.e. healthy and diseased. First, the acquired images are classified and labeled conferring to the plants. The plants were named ranging from P0 to P11. Then the entire dataset has been divided among 22 subject categories ranging from 0000 to 0022. The classes labeled with 0000 to 0011 were marked as a healthy class and ranging from 0012 to 0022 were labeled diseased class. We have collected about 4503 images of which contains 2278 images of healthy leaf and 2225 images of the diseased leaf. All the leaf images were collected from the Shri Mata Vaishno Devi University, Katra. This process has been carried out form the month of March to May in the year 2019. The images are captured in a closed environment. This acquisition process was completely wi-fi enabled. All the images are captured using a Nikon D5300 camera inbuilt with performance timing for shooting JPEG in single shot mode (seconds/frame, max resolution) = 0.58 and for RAW+JPEG = 0.63. The images were in .jpg format captured with 18-55mm lens with sRGB color representation, 24-bit depth, 2 resolution unit, 1000-ISO, and no flash.

Further, we hope that this study can be beneficial for researchers and academicians in developing methods for plant identification, plant classification, plant growth monitoring, leave disease diagnosis, etc. Finally, the anticipated impression is towards a better understanding of the plants to be planted and their suitable management.

植物与环境之间的关系错综复杂，它们在滋养大气中各种元素方面发挥着重要作用。植物亦是调节碳排放和气候变化的重要力量。然而，在过去，我们对它们的破坏毫不犹豫。这不仅导致了其中许多物种的灭绝，还遭遇了气候变化这一严重后果。然而，若我们给予植物时间和空间，它们将展现出惊人的恢复能力，重新装扮地球，使其重现生机。因此，本研究致力于植物叶片的识别、检测、病害诊断等方面的研究。为此，选取了十二种经济和环境效益显著的植物，包括芒果、阿琼、阿斯特罗尼亚、石榴、白果、黑莓、巴豆、马缨丹、印度楝、罗勒、石榴、柠檬和银杏。收集了这些植物健康和病害状态下的叶片图像，并分配至两个独立的模块中。主要而言，整个图像集被划分为两大类，即健康和病害。首先，根据植物对获取的图像进行分类和标记，植物名称从P0至P11。随后，整个数据集被划分为22个主题类别，从0000至0022。类别0000至0011被标记为健康类别，而0012至0022被标记为病害类别。共收集了约4503张图像，其中健康叶片图像有2278张，病害叶片图像有2225张。所有叶片图像均来自克里特拉的斯里玛塔瓦伊什诺德维大学。该过程于2019年3月至5月进行，图像在封闭环境中捕获。整个捕获过程完全由Wi-Fi驱动。所有图像均使用内置性能计时拍摄JPEG的单次拍摄模式（秒/帧，最大分辨率）为0.58和RAW+JPEG为0.63。图像采用18-55mm镜头，以sRGB颜色表示，24位深度，2个分辨率单位，1000-ISO，无闪光灯。此外，我们希望这项研究能够对研究人员和学者在开发植物识别、植物分类、植物生长监测、叶片病害诊断等方法方面提供益处。最终，我们期待的是对拟种植植物及其适宜管理的更深入理解。

搜集汇总

背景与挑战

背景概述

该数据集是一个专注于植物叶片图像的数据集，包含12种经济或环境有益植物（如芒果、榕树、石榴等）的健康和患病叶片图像，总计4503张（健康类2278张，患病类2225张），图像采集于2019年在受控环境中使用专业相机拍摄，旨在支持植物识别、分类和疾病诊断等研究。数据集具有结构化的分类（分为22个主题类别，涵盖健康和患病两类）和详细的图像技术规格（如.jpg格式、24位深度），适用于计算机视觉和植物保护领域的应用。