research-article

Convolutional neural networks in predicting cotton yield from images of commercial fields

Authors:

Danilo Tedesco-Oliveira,

Rouverson Pereira da Silva,

Walter Maldonado, Jr.,

Cristiano ZerbatoAuthors Info & Claims

Volume 171, Issue C

https://doi.org/10.1016/j.compag.2020.105307

Published: 01 April 2020 Publication History

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Highlights

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Images of the cotton plants in commercial fields were acquired by a mobile device.

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We present an approach robust exploring environmental condition.

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The best result for count cotton bolls obtained an accuracy of 8.84%

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Predicting cotton yield showed a high precision (R2 = 0.93).

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Deep Learning networks were efficient and viable to be used in harvesting machines.

Abstract

One way to improve the quality of mechanized cotton harvesting is to change harvester settings and adjustments throughout the process, according to information obtained during the operation. We believe that yield predictions are important for managing the quality of operation, aiming at increasing efficiency and reducing losses. Therefore, this study aimed to develop an automated system for cotton yield prediction from color images acquired by a simple mobile device. We propose a robust approach to environmental conditions, training detection algorithms with images acquired at different times throughout the day, and evaluating three different scenarios (low-, average-, and high-demand computational resources). The experimental results for the average demand computational scenario, which are suitable for real-time deployment on low-cost devices such as smartphones and other ARM-processed devices, indicated the possibility of counting bolls using images acquired at different times throughout the day, with mean errors of 8.84% (∼5 bolls). Furthermore, we observed a 17.86% error when predicting yield using 205 images from the testing dataset, which is equivalent to about 19.14 g.

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Cited By

Verma PPaul AMachavaram RBhattacharya M(2024)Cotton Growth Stages Detection Using Fine-Tuned YOLOv8 Deep Learning ModelProceedings of the 2024 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence10.1145/3665065.3665069(20-25)Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1145/3665065.3665069
Castellano GDe Marinis PVessio G(2023)Weed mapping in multispectral drone imagery using lightweight vision transformersNeurocomputing10.1016/j.neucom.2023.126914562:COnline publication date: 28-Dec-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126914
Liu QZhang YYang G(2023)Small unopened cotton boll counting by detection with MRF-YOLO in the wildComputers and Electronics in Agriculture10.1016/j.compag.2022.107576204:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.compag.2022.107576
Show More Cited By

Index Terms

Convolutional neural networks in predicting cotton yield from images of commercial fields
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Computers and Electronics in Agriculture

Computers and Electronics in Agriculture Volume 171, Issue C

Apr 2020

289 pages

ISSN:0168-1699

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 April 2020

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Cited By

Verma PPaul AMachavaram RBhattacharya M(2024)Cotton Growth Stages Detection Using Fine-Tuned YOLOv8 Deep Learning ModelProceedings of the 2024 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence10.1145/3665065.3665069(20-25)Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1145/3665065.3665069
Castellano GDe Marinis PVessio G(2023)Weed mapping in multispectral drone imagery using lightweight vision transformersNeurocomputing10.1016/j.neucom.2023.126914562:COnline publication date: 28-Dec-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126914
Liu QZhang YYang G(2023)Small unopened cotton boll counting by detection with MRF-YOLO in the wildComputers and Electronics in Agriculture10.1016/j.compag.2022.107576204:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.compag.2022.107576
Zhang YYang GLiu YWang CYin Y(2022)An improved YOLO network for unopened cotton boll detection in the fieldJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21151442:3(2193-2206)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-211514

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