A Light Weight Deep Learning Model for Real World Plant Identification

Abstract

Automatic identification and classification of different plant leaf species have become a common trend among researchers and scientists. To obtain a result with better precision, they use various methods and techniques of deep learning to build a model. Convolutional neural networks are becoming the most common method used by scientists to classify plant leaves. However, the classification of plant leaves can be challenging with more rare species and complicated backgrounds, for which researchers build several models to achieve high-level accuracy. In the present study for the classification of leaves, we have created a model for plant leaf classification based on a dataset we collected. We've used the Resnet-50 model, a well-known CNN architecture, which provided an efficient method to organize and analyze a deep classification to reduce the complexity so that there will be fewer parameters for training and low time consumption as well. Using Resnet-50, we intended to develop a significant result in our classification model. The convolutional neural network is famous for its influential abilities in feature extraction and classification. And Resnet-50 being a residual network enabled us to train deep networks in our model. The average training accuracy reached 98.3%, while the average testing accuracy reached 92.5%. The key contribution of this study is effective accuracy as well as we have trained the model on our own prepared dataset that we have prepared from real world environment. Data Availability: https://drive.google.com/file/d/1bD7B257l-6wqUCQHBWhle95xyrotUbwO/view?usp=sharing