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Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network

INTRODUCTION: Estimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial re...

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Autores principales: Tang, Minmeng, Sadowski, Dennis Lee, Peng, Chen, Vougioukas, Stavros G., Klever, Brandon, Khalsa, Sat Darshan S., Brown, Patrick H., Jin, Yufang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975588/
https://www.ncbi.nlm.nih.gov/pubmed/36875622
http://dx.doi.org/10.3389/fpls.2023.1070699
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author Tang, Minmeng
Sadowski, Dennis Lee
Peng, Chen
Vougioukas, Stavros G.
Klever, Brandon
Khalsa, Sat Darshan S.
Brown, Patrick H.
Jin, Yufang
author_facet Tang, Minmeng
Sadowski, Dennis Lee
Peng, Chen
Vougioukas, Stavros G.
Klever, Brandon
Khalsa, Sat Darshan S.
Brown, Patrick H.
Jin, Yufang
author_sort Tang, Minmeng
collection PubMed
description INTRODUCTION: Estimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level. METHODS: This study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the ‘Independence’ cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level. RESULTS: The deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation. DISCUSSION: This study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability.
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spelling pubmed-99755882023-03-02 Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network Tang, Minmeng Sadowski, Dennis Lee Peng, Chen Vougioukas, Stavros G. Klever, Brandon Khalsa, Sat Darshan S. Brown, Patrick H. Jin, Yufang Front Plant Sci Plant Science INTRODUCTION: Estimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level. METHODS: This study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the ‘Independence’ cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level. RESULTS: The deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation. DISCUSSION: This study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability. Frontiers Media S.A. 2023-02-15 /pmc/articles/PMC9975588/ /pubmed/36875622 http://dx.doi.org/10.3389/fpls.2023.1070699 Text en Copyright © 2023 Tang, Sadowski, Peng, Vougioukas, Klever, Khalsa, Brown and Jin https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Tang, Minmeng
Sadowski, Dennis Lee
Peng, Chen
Vougioukas, Stavros G.
Klever, Brandon
Khalsa, Sat Darshan S.
Brown, Patrick H.
Jin, Yufang
Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network
title Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network
title_full Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network
title_fullStr Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network
title_full_unstemmed Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network
title_short Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network
title_sort tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975588/
https://www.ncbi.nlm.nih.gov/pubmed/36875622
http://dx.doi.org/10.3389/fpls.2023.1070699
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