Prediction of Leaf Species & Disease using AI for Various Plants

Abstract

This paper presents a method for predicting leaf species and disease in various plants using artificial intelligence. The approach involves training a machine learning model on a dataset of images of plant leaves and corresponding labels of species and disease. The trained model is then evaluated on a test set to determine its accuracy in classifying new images. Our Study show that the model will achieves high accuracy in predicting both leaf species and disease. This method provides a useful tool for identifying plants and detecting disease in agriculture, forestry, and other relevant fields. However, present techniques require laboratory diagnosis which takes time and resources. To help improve plant disease detection, the PlantDoc dataset was created. The original dataset contained 2,598 data images with 13 plant species and 17 classes of diseases. Data was provided as images in JPG, and annotations in both the VOC XML format and CSV format.