Plant and weed identifier robot as an agroecological tool using artificial neural networks for image identification

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.

Subjects

deep learning

artificial neural networks

image identification

agroecology

weeds

yield gap

environment

health

DDC Class

630: Landwirtschaft, Veterinärmedizin

Funding(s)

Publikationsfonds 2021

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.

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

agriculture-11-00222-v2.pdf

Size

17.7 MB

Format

Adobe PDF

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Plant and weed identifier robot as an agroecological tool using artificial neural networks for image identification