PlantaeK: A leaf database of native plants of Jammu and Kashmir

Published: 5 September 2019| Version 1 | DOI: 10.17632/t6j2h22jpx.1


Computer vision can predominantly be focused to design the strategies for the conservation of the plants. Previous decade’s trends and the current prevailing incidents with respect to global warming, forest fires, and famines act as potential indicators of how much nature is destroyed by human activities. Plants are vitally used in foodstuff, medicine, industry and as well for environmental protection. However, due to lack of resources and knowledge, it is difficult to recognize different plant species, plant diseases, etc. Nowadays modern equipment’s are being designed to address these issues. So considering the challenges, demands, we have constructed a database of different plants. The plants taken for study are the native plants of the Kashmir region of India. The climate of Kashmir remains chilling for a few months and pleasant for the rest of the year. Eight different plants namely Apple, Apricot, Cherry, Cranberry, Grapes, Peach, Pear, and Walnut are selected for the study based on their commercial and medicinal usage. The leaf is the primary object of reference taken for making the database, as they grow much earlier than fruits as well as the other plant parts. For each plant two types of leaves are selected, one healthy and the other diseased. Considering the natural conditions under which the farmers or the agriculturists have to work, the images are captured in broad daylight under the auto mode with the Nikon D-SLR digital camera with an ISO Speed = 100, Aperture = F/5.6, Flash = Not Fired, Shutter Speed = 1/640. All the images are captured by an 18-55 mm lens and are in .JPG format. The leaves are divided into two major classes A and B respectively. The two major classes were then divided into 16 sub classes i.e., eight healthy and eight diseased. The symbol “h” e.g., plant-name_h001 in the images represent healthy images and “d” i.e., plant-name_d001 represents the diseased images. The images are labeled, resized and classified into different classes. The class of healthy images comprises of a total of 1223 images and the diseased images constitute of a total of 934 images. Thus a total of 2157 images were selected from the captured images to sew up this database. Every little step towards a positive perspective marks the beginning of the era of growth with kindness.



Shri Mata Vaishno Devi University


Image Processing, Machine Learning, Agricultural Plant, Precision Agriculture