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Article

Plant and Weed Identifier Robot as an Agroecological Tool Using Artificial Neural Networks for Image Identification

Rural Revival and Restoration Egineering (RUVIVAL), Institute of Wastewater Management and Water Protection, Hamburg University of Technology, Eissendorfer Strasse 42, 21073 Hamburg, Germany
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Sebastian Kujawa, Gniewko Niedbała and Maciej Zaborowicz
Agriculture 2021, 11(3), 222; https://doi.org/10.3390/agriculture11030222
Received: 31 December 2020 / Revised: 10 February 2021 / Accepted: 4 March 2021 / Published: 8 March 2021
(This article belongs to the Special Issue Artificial Neural Networks in Agriculture)
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. View Full-Text
Keywords: deep learning; artificial neural networks; image identification; agroecology; weeds; yield gap; environment; health deep learning; artificial neural networks; image identification; agroecology; weeds; yield gap; environment; health
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MDPI and ACS Style

Shah, T.M.; Nasika, D.P.B.; Otterpohl, R. Plant and Weed Identifier Robot as an Agroecological Tool Using Artificial Neural Networks for Image Identification. Agriculture 2021, 11, 222. https://doi.org/10.3390/agriculture11030222

AMA Style

Shah TM, Nasika DPB, Otterpohl R. Plant and Weed Identifier Robot as an Agroecological Tool Using Artificial Neural Networks for Image Identification. Agriculture. 2021; 11(3):222. https://doi.org/10.3390/agriculture11030222

Chicago/Turabian Style

Shah, Tavseef Mairaj, Durga Prasad Babu Nasika, and Ralf Otterpohl. 2021. "Plant and Weed Identifier Robot as an Agroecological Tool Using Artificial Neural Networks for Image Identification" Agriculture 11, no. 3: 222. https://doi.org/10.3390/agriculture11030222

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