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

Tavseef Mairaj Shah, Durga Prasad Babu Nasika and Ralf Otterpohl
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Tavseef Mairaj Shah: Rural Revival and Restoration Egineering (RUVIVAL), Institute of Wastewater Management and Water Protection, Hamburg University of Technology, Eissendorfer Strasse 42, 21073 Hamburg, Germany
Durga Prasad Babu Nasika: Rural Revival and Restoration Egineering (RUVIVAL), Institute of Wastewater Management and Water Protection, Hamburg University of Technology, Eissendorfer Strasse 42, 21073 Hamburg, Germany
Ralf Otterpohl: Rural Revival and Restoration Egineering (RUVIVAL), Institute of Wastewater Management and Water Protection, Hamburg University of Technology, Eissendorfer Strasse 42, 21073 Hamburg, Germany

Agriculture, 2021, vol. 11, issue 3, 1-31

Abstract: 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.

Keywords: deep learning; artificial neural networks; image identification; agroecology; weeds; yield gap; environment; health (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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