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纷繁复杂且多元交织。植物通过释放多种元素滋养大气，同时也是调控碳排放与气候变化的关键要素。然而过去人类曾毫无顾忌地破坏植被，不仅导致大量物种流失，更引发了气候变化等严重后果。但若给予植物充足的时间与生长空间，它们便具备惊人的恢复能力，能够重新以多样的植物类群覆盖大地——这也是近期相关研究的核心动因之一。本研究聚焦植物叶片的识别、检测与病害诊断等方向，为此遴选了12种兼具经济与生态价值的植物，分别为芒果（Mango）、阿江榄仁（Arjun）、糖胶树（Alstonia Scholaris）、番石榴（Guava）、木橘（Bael）、蒲桃（Jamun）、麻疯树（Jatropha）、水黄皮（Pongamia Pinnata）、罗勒（Basil）、石榴（Pomegranate）、柠檬（Lemon）以及悬铃木（Chinar）。研究团队采集了这些植物健康与染病状态下的叶片图像，并将其划分为两个独立数据集模块。 总体而言，所有图像被划分为健康与染病两个大类。首先，采集到的图像依据所属植物类别进行分类与标注，植物类别被编号为P0至P11。随后整个数据集被划分为22个主题类别，编号范围为0000至0022：其中0000至0011为健康叶片类别，0012至0022为染病叶片类别。本次研究共收集到4503张叶片图像，其中健康叶片图像2278张，染病叶片图像2225张。所有图像均采集自卡特拉市什里·玛塔·维什诺·德维大学（Shri Mata Vaishno Devi University, Katra），采集时间为2019年3月至5月。图像采集于封闭环境中，全程采用无线传输方案。所有图像均由尼康D5300（Nikon D5300）相机拍摄，该相机的单次拍摄性能参数如下：单JPEG模式下（秒/帧，最高分辨率）拍摄耗时为0.58，RAW+JPEG模式下为0.63。图像格式为.jpg，采用18-55mm镜头拍摄，色彩空间为sRGB，位深24位，分辨率单位为2，ISO感光度为1000，未使用闪光灯。 本研究希望可为从事植物识别、植物分类、植物生长监测以及叶片病害诊断等方向研究的科研人员与学者提供参考。最终目标是加深对目标种植植物的认知，从而实现更科学的种植管理。