Classification of plant seedlings using deep convolutional neural network architectures

Author:

Makanapura Namratha,Sujatha C,Patil Prakash R,Desai Padmashree

Abstract

Abstract Weed management has a vital role in applications of agriculture domain. One of the key tasks is to identify the weeds after few days of plant germination which helps the farmers to perform early-stage weed management to reduce the contrary impacts on crop growth. Thus, we aim to classify the seedlings of crop and weed species. In this work, we propose a plant seedlings classification using the benchmark plant seedlings dataset. The dataset contains the images of 12 different species where three belongs to plant species and the other nine belongs to weed species. We implement the classification framework using three different deep convolutional neural network architectures, namely ResNet50V2, MobileNetV2 and EfficientNetB0. We train the models using transfer learning and compare the performance of each model on a test dataset of 833 images. We compare the three models and demonstrate that the EfficientNetB0 performs better with an average F1-Score of 96.26% and an accuracy of 96.52%.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference18 articles.

1. Precise weed and maize classification through convolutional neuronal networks;Andrea,2017

2. DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning;Olsen;Sci Rep,2019

3. Philippine Indigenous Plant Seedlings Classification Using Deep Learning;Villaruz,2018

4. Crop Weed Identification System Based on Convolutional Neural Network;Miao,2019

5. Crop and Weeds Classification for Precision Agriculture Using Context-Independent Pixel-Wise Segmentation;Fawakherji,2019

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