Please use this identifier to cite or link to this item:
Publisher DOI: 10.3390/agriculture11030222
Title: Plant and weed identifier robot as an agroecological tool using artificial neural networks for image identification
Language: English
Authors: Shah, Tavseef Mairaj  
Nasika, Durga Prasad Babu 
Otterpohl, Ralf 
Keywords: deep learning;artificial neural networks;image identification;agroecology;weeds;yield gap;environment;health
Issue Date: 8-Mar-2021
Publisher: Multidisciplinary Digital Publishing Institute
Source: Agriculture 11 (3): 222 (2021)
Journal or Series Name: Agriculture 
Abstract (english): 
Farming systems form the backbone of the world food system. The food system, in turn, is a critical component in sustainable development, with direct linkages to the social, economic, and ecological systems. Weeds are one of the major factors responsible for the crop yield gap in the different regions of the world. In this work, a plant and weed identifier tool was conceptualized, developed, and trained based on artificial deep neural networks to be used for the purpose of weeding the inter-row space in crop fields. A high-level design of the weeding robot is conceptualized and proposed as a solution to the problem of weed infestation in farming systems. The implementation process includes data collection, data pre-processing, training and optimizing a neural network model. A selective pre-trained neural network model was considered for implementing the task of plant and weed identification. The faster R-CNN (Region based Convolution Neural Network) method achieved an overall mean Average Precision (mAP) of around 31% while considering the learning rate hyperparameter of 0.0002. In the plant and weed prediction tests, prediction values in the range of 88–98% were observed in comparison to the ground truth. While as on a completely unknown dataset of plants and weeds, predictions were observed in the range of 67–95% for plants, and 84% to 99% in the case of weeds. In addition to that, a simple yet unique stem estimation technique for the identified weeds based on bounding box localization of the object inside the image frame is proposed.
DOI: 10.15480/882.3385
ISSN: 2077-0472
Other Identifiers: doi: 10.3390/agriculture11030222
Institute: Abwasserwirtschaft und Gewässerschutz B-2 
Document Type: Article
More Funding information: We acknowledge support for the Open Access fees by Hamburg University of Technology (TUHH) in the funding programme Open Access Publishing. We acknowledge support of Hamburg Open Online University (HOOU) for the grant to develop the prototype of this robot. We acknowledge the support the Institute of Reliability Engineering, TUHH for their logistical support in this research.
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
Appears in Collections:Publications with fulltext

Files in This Item:
File Description SizeFormat
agriculture-11-00222-v2.pdf18,13 MBAdobe PDFView/Open
Show full item record

Page view(s)

Last Week
Last month
checked on Apr 21, 2021


checked on Apr 21, 2021

Google ScholarTM


Note about this record

Cite this record


This item is licensed under a Creative Commons License Creative Commons