The forest is considered as a significant source of woody biomass production. Sustainable production of wood, lower emittance of CO2 from burning, and lower amount of sulfur and heavy metals are the advantages of woods rather than fossil fuels. The utilization of biomass, as an energy resource, is required four main steps of production, pretreatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the production of the woody biomass raw material in forests because investigating numerous related works concluded that there is a considerable reviewing gap in analyzing and collecting the applications of Machine Learning in the woody biomass. To fill this gap in the current work, the origin of woods is explained and the application of Machine Learning in this section is scrutinized. Then, the multidisciplinary enhancement approaches in the production of plants as well as the role of Machine Learning in each of them are reviewed. Meanwhile, the role of natural and planted forests in the production of woody biomass is explained and the application of Machine Learning in these areas is surveyed. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning is widely utilized in the production of woody biomass to enhance the wood production quantity and quality, improve the predictions, enhance the harvesting techniques, and diminish the losses.
REFERENCES
1.
Abioye
,
E. A.
,
Hensel
,
O.
,
Esau
,
T. J.
,
Elijah
,
O.
,
Abidin
,
M. S. Z.
,
Ayobami
,
A. S.
,
Yerima
,
O.
, and
Nasirahmadi
,
A.
, “
Precision irrigation management using machine learning and digital farming solutions
,” AgriEngineering
4
(1
), 70
–103
(2022
).2.
Ajjolli Nagaraja
,
A.
,
Charton
,
P.
,
Cadet
,
X. F.
,
Fontaine
,
N.
,
Delsaut
,
M.
,
Wiltschi
,
B.
,
Voit
,
A.
,
Offmann
,
B.
,
Damour
,
C.
,
Grondin-Perez
,
B.
, and
Cadet
,
F.
, “
A machine learning approach for efficient selection of enzyme concentrations and its application for flux optimization
,” Catalysts
10
(3
), 291
(2020
).3.
Alley
,
E. C.
,
Khimulya
,
G.
,
Biswas
,
S.
et al, “
Unified rational protein engineering with sequence-based deep representation learning
,” Nat. Methods
16
, 1315
–1322
(2019
).4.
Amani
,
M. A.
and
Marinello
,
F.
, “
A deep learning-based model to reduce costs and increase productivity in the case of small datasets: A case study in cotton cultivation
,” Agriculture
12
(2
), 267
(2022
).5.
Anjum
,
M.
and
Umar
,
M. S.
, “
Garbage localization based on weakly supervised learning in deep convolutional neural network
,” in Proceedings of the International Conference on Advances in Computing, Communication Control and Networking (ICACCCN 2018)
(IEEE, 2018
), pp. 1108
–1113
.6.
Buxbaum
,
N.
,
Lieth
,
J. H.
, and
Earles
,
M.
, “
Non-destructive plant biomass monitoring with high spatio-temporal resolution via proximal RGB-D imagery and end-to-end deep learning
,” Front. Plant Sci.
13
, 758818
(2022
).7.
Chen
,
X.
,
Avtar
,
R.
,
Umarhadi
,
D. A.
,
Louw
,
A. S.
,
Shrivastava
,
S.
,
Yunus
,
A. P.
,
Khedher
,
K. M.
,
Takemi
,
T.
, and
Shibata
,
H.
, “
Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
,” Weather Clim. Extremes
38
, 100494
(2022a
).8.
Chen
,
X.
,
Huang
,
Y.
,
Nie
,
C.
et al, “
A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms
,” Sci. Data
9
, 427
(2022b
).9.
Chiang
,
C.
,
Barnes
,
C.
,
Angelov
,
P. P.
, and
Jiang
,
R. M.
, “
Deep learning based automated forest health diagnosis from aerial images
,” IEEE Access
8
, 144064
–144076
(2020
).10.
Ching
,
P. M. L.
,
Mayol
,
A. P.
,
Juan
,
J. L. G. S.
et al, “
Early prediction of Spirulina platensis biomass yield for biofuel production using machine learning
,” Clean Technol. Environ. Policy
24
, 2283
–2293
(2022
).11.
Concu
,
R.
,
Cordeiro
,
M.
,
Natália
,
D. S.
,
Munteanu
,
C. R.
, and
González-Díaz
,
H.
, “
PTML model of enzyme subclasses for mining the proteome of biofuel producing microorganisms
,” J. Proteome Res.
18
(7
), 2735
–2746
(2019
).12.
Coopersmith
,
E. J.
,
Minsker
,
B. S.
,
Wenzel
,
C. E.
, and
Gilmore
,
B. J.
, “
Machine learning assessments of soil drying for agricultural planning
,” Comput. Electron. Agric.
104
, 93
–104
(2014
).13.
Coşgun
,
A.
,
Günay
,
M. E.
, and
Yıldırım
,
R.
, “
Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning
,” Renewable Energy
163
, 1299
–1317
(2021
).14.
Dalaka
,
A.
,
Kompare
,
B.
,
Robnik-Šikonja
,
M.
, and
Sgardelis
,
S. P.
, “
Modelling the effects of environmental conditions on apparent photosynthesis of Stipa bromoides by machine learning tools
,” Ecol. Modell.
129
(2–3
), 245
–257
(2000
).15.
Dantas
,
D.
,
de Castro Nunes Santos Terra
,
M.
,
Schorr
,
L. P. B.
, and
Calegário
,
N.
, “
Machine learning for carbon stock prediction in a tropical forest in Southeastern Brazil
,” Bosque
42
, 131
–140
(2021
).16.
Doshi
,
Z.
,
Nadkarni
,
S.
,
Agrawal
,
R.
, and
Shah
,
N.
, “
AgroConsultant: Intelligent crop recommendation system using machine learning algorithms
,” in Proceedings of the Fourth International Conference on Computing Communication Control and Automation (ICCUBEA, 2018)
(
IEEE
,
Piscataway, NJ
, 2018
), pp. 1
–6
.17.
Dutta
,
R.
,
Li
,
C.
,
Smith
,
D.
,
Das
,
A.
, and
Aryal
,
J.
, “
Big data architecture for environmental analytics
,” in International Symposium on Environmental Software Systems
(
Springer
,
Berlin/Heidelberg, Germany
, 2015
), pp. 578
–588
.18.
Fang
,
F.
,
Ni
,
B.
,
Li
,
W.
,
Sheng
,
G.
, and
Yu
,
H.
, “
A simulation-based integrated approach to optimize the biological nutrient removal process in a full-scale wastewater treatment plant
,” Chem. Eng. J.
174
, 635
–643
(2011
).19.
Feehan
,
R.
,
Franklin
,
M. W.
, and
Slusky
,
J. S. G.
, “
Machine learning differentiates enzymatic and non-enzymatic metals in proteins
,” Nat. Commun.
12
, 3712
(2021
).20.
Fu
,
P.
,
Meacham-Hensold
,
K.
,
Guan
,
K.
, and
Bernacchi
,
C. J.
, “
hyperspectral leaf reflectance as proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms
,” Front. Plant Sci.
10
, 730
(2019
).21.
Furuya
,
D. E. G.
,
Aguiar
,
J. A. F.
,
Estrabis
,
N. V.
,
Pinheiro
,
M. M. F.
,
Furuya
,
M. T. G.
,
Pereira
,
D. R.
,
Gonçalves
,
W. N.
,
Liesenberg
,
V.
,
Li
,
J.
,
Marcato
, Jr.,
J.
,
Prado Osco
,
L.
, and
Ramos
,
A. P. M.
, “
A machine learning approach for mapping forest vegetation in riparian zones in an Atlantic Biome environment using Sentinel-2 imagery
,” Remote Sens.
12
(24
), 4086
(2020
).22.
Gamble
,
C.
,
Bryant
,
D.
,
Carrieri
,
D.
,
Bixby
,
E.
,
Dang
,
J.
,
Marshall
,
J.
,
Doughty
,
D.
,
Colwell
,
L.
,
Berndl
,
M.
,
Roberts
,
J.
, and
Frumkin
,
M.
, “
Machine learning optimization of photosynthetic microbe cultivation and recombinant protein production
,” bioRxiv 2021.08.06.453272 (2021
).23.
Gautam
,
S.
,
Mishra
,
U.
,
Scown
,
C. D.
, and
Zhang
,
Y.
, “
Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions
,” GCB Bioenergy
12
, 878
(2020
).24.
Grinblat
,
G. L.
,
Uzal
,
L. C.
,
Larese
,
M. G.
, and
Granitto
,
P. M.
, “
Deep learning for plant identification using vein morphological patterns
,” Comput. Electron. Agric.
127
, 418
–424
(2016
).25.
Han
,
X.
,
Ning
,
W.
,
Ma
,
X.
,
Wang
,
X.
, and
Zhou
,
K.
, “
Improving protein solubility and activity by introducing small peptide tags designed with machine learning models
,” Metab. Eng. Commun.
11
, e00138
(2020
).26.
Hart
,
E.
,
Sim
,
K.
,
Kamimura
,
K.
,
Meredieu
,
C.
,
Guyon
,
D.
, and
Gardiner
,
B.
, “
Use of machine learning techniques to model wind damage to forests
,” Agric. For. Meteorol.
265
, 16
–29
(2019
).27.
Häse
,
F.
,
Roch
,
L. M.
,
Friederich
,
P
et al, “
Designing and understanding light-harvesting devices with machine learning
,” Nat. Commun.
11
, 4587
(2020
).28.
He
,
T.
,
Marco
,
J.
, and
Soares
,
R.
et al, “
Machine learning models with quantitative wood anatomy data can discriminate between Swietenia macrophylla and Swietenia mahagoni
,” Forests
11
, 36
(2020
).29.
Heckmann
,
D.
,
Schlüter
,
U.
, and
Weber
,
A. P. M.
, “
Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra
,” Mol. Plant
10
(6
), 878
–890
(2017
).30.
Huang
,
Z. Q.
,
Chen
,
Y. C.
, and
Wen
,
C. Y.
, “
Real-time weather monitoring and prediction using city buses and machine learning
,” Sensors
20
(18
), 5173
(2020a
).31.
Huang
,
P.
,
Chu
,
S. K. S.
,
Frizzo
,
H. N.
,
Connolly
,
M. P.
,
Caster
,
R. W.
, and
Siegel
,
J. B.
, “
Evaluating protein engineering thermostability prediction tools using an independently generated dataset
,” ACS Omega
5
(12
), 6487
–6493
(2020b
).32.
Huntington
,
H.
,
Cui
,
X.
,
Mishra
,
U.
, and
Scown
,
C. D.
, Machine Learning to Predict Biomass Sorghum Yields under Future Climate Scenarios
(
Wiley
, 2020
).33.
Johann
,
A. L.
,
de Araújo
,
A. G.
,
Delalibera
,
H. C.
, and
Hirakawa
,
A. R.
, “
Soil moisture modeling based on stochastic behavior of forces on a no-till chisel opener
,” Comput. Electron. Agric.
121
, 420
–428
(2016
).34.
Kaneko
,
T.
et al, “
A canopy photosynthesis model based on a highly generalizable artificial neural network incorporated with a mechanistic understanding of single-leaf photosynthesis
,” Agric. For. Meteorol.
323
, 109036
(2022
).35.
Khruschev
,
S. S.
,
Plyusnina
,
T. Y.
,
Antal
,
T. K.
et al, “
Machine learning methods for assessing photosynthetic activity: Environmental monitoring applications
,” Biophys. Rev.
14
, 821
–842
(2022
).36.
Kramer
,
D.
,
Chen
,
J.
,
Ostendorf
,
E.
,
Lei
,
X.
,
Cruz
,
J.
, and
Brodersen
,
J.
, Methods for Estimating Photosynthetic Characteristics in Plant Canopies and Systems and Apparatus Related Thereto
(
Board of trustees of Michigan State University
, 2019
).37.
Kumar
,
A.
,
Rajendra
,
O.
, and
Chakraborty
,
S.
, see http://203.201.63.46:8080/jspui/bitstream/123456789/6116/1/PR3111.pdf for “
Intelligent Crop Recommendation System using ML
” (2019
).38.
Kumar
,
V.
,
Sharma
,
A.
,
Kumar
,
R.
,
Bhardwaj
,
R.
,
Kumar Thukral
,
A.
, and
Rodrigo-Comino
,
J.
, “
Assessment of heavy-metal pollution in three different Indian water bodies by combination of multivariate analysis and water pollution indices
,” Hum. Ecol. Risk Assess.
26
, 1
–16
(2018
).39.
Kundu
,
N.
,
Rani
,
G.
, and
Singh Dhaka
,
V.
, “
Seeds classification and quality testing using deep learning and YOLO v5
,” in Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence (DSMLAI '21)
(
Association for Computing Machinery
,
New York
, 2022
), pp. 153
–160
.40.
Li
,
D.
, “
Application of artificial intelligence and machine learning based on big data analysis in sustainable agriculture
,” Acta Agric. Scand.
71
, 956
–969
(2021
).41.
Lee
,
J.
,
Im
,
J.
,
Kim
,
K.
, and
Quackenbush
,
L. J.
, “
Machine learning approaches for estimating forest stand height using plot-based observations and airborne LiDAR data
,” Forests
9
(5
), 268
(2018
).42.
Lee
,
K. J.
,
Yun
,
S. T.
,
Yu
,
S.
,
Kim
,
K. H.
,
Lee
,
J. H.
, and
Lee
,
S. H.
, “
The combined use of self-organizing map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea
,” J. Hydrol.
569
, 685
–697
(2019
).43.
Liu
,
B.
,
Sträuber
,
H.
,
Saraiva
,
J.
et al, “
Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture
,” Microbiome
10
, 48
(2022
).44.
Liu
,
P.
,
Wang
,
J.
,
Sangaiah
,
A.
,
Xie
,
Y.
, and
Yin
,
X.
, “
Analysis and prediction of water quality using LSTM deep neural networks in IoT environment
,” Sustainability
11
, 072058
(2019
).45.
Long
,
B.
,
Fischer
,
B.
,
Zeng
,
Y.
et al, “
Machine learning-informed and synthetic biology enabled semi-continuous algal cultivation to unleash renewable fuel productivity
,” Nat. Commun.
13
, 541
(2022
).46.
Ly
,
Q. V.
,
Nguyen
,
X. C.
,
Le
,
N. C.
,
Truong
,
T. D.
,
Hoang
,
T. T.
,
Park
,
T. J.
,
Maqbool
,
T.
,
Pyo
,
J.
,
Cho
,
K. H.
,
Lee
,
K. S
et al, “
Application of machine learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea
,” Sci. Total Environ.
797
, 149040
(2021
).47.
Maione
,
C.
,
Batista
,
B. L.
,
Campiglia
,
A. D.
,
Barbosa
,
F.
, and
Barbosa
,
R. M.
, “
Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry
,” Comput. Electron. Agric.
121
, 101
–107
(2016
).48.
Mohammadi
,
K.
,
Shamshirband
,
S.
,
Motamedi
,
S.
,
Petković
,
D.
,
Hashim
,
R.
, and
Gocic
,
M.
, “
Extreme learning machine based prediction of daily dew point temperature
,” Comput. Electron. Agric.
117
, 214
–225
(2015
).49.
Mohammed
,
M.
,
Kolapalli
,
R.
,
Golla
,
N.
, and
Maturi
,
S. S.
, “
Prediction of rainfall using machine learning techniques
,” Int. J. Sci. Technol. Res.
9
(1
), 3236
–3240
(2020
).50.
Moore
,
B. M.
,
Wang
,
P.
,
Fan
,
P.
,
Leong
,
B.
,
Schenck
,
C. A.
, and
Lloyd
,
P.
, “
Robust predictions of specialized metabolism genes through machine learning
,” Proc. Natl. Acad. Sci. U. S. A.
116
, 2344
–2353
(2019
).51.
Morellos
,
A.
,
Pantazi
,
X.-E.
,
Moshou
,
D.
,
Alexandridis
,
T.
,
Whetton
,
R.
,
Tziotzios
,
G.
,
Wiebensohn
,
J.
,
Bill
,
R.
, and
Mouazen
,
A. M.
, “
Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy
,” Biosyst. Eng.
152
, 104
–116
(2016
).52.
Moshou
,
D.
,
Pantazi
,
X.-E.
,
Kateris
,
D.
, and
Gravalos
,
I.
, “
Water stress detection based on optical multisensory fusion with a least squares support vector machine classifier
,” Biosyst. Eng.
117
, 15
–22
(2014
).53.
Munis
,
R. A.
,
Almeida
,
R. O.
,
Camargo
,
D. A.
,
da Silva
,
R. B. G.
,
Wojciechowski
,
J.
, and
Simões
,
D.
, “
Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil
,” Forests
13
(7
), 1068
(2022
).54.
Musa
,
E.
,
Doherty
,
F.
, and
Goldsmith
,
B. R.
, “
Accelerating the structure search of catalysts with machine learning
,” Curr. Opin. Chem. Eng.
35
, 100771
(2022
).55.
Nag
,
A.
,
Gerritsen
,
A.
,
Doeppke
,
C.
, and
Harman-Ware
,
A. E.
, “
Machine learning-based classification of lignocellulosic biomass from pyrolysis-molecular beam mass spectrometry data
,” Int. J. Mol. Sci.
22
(8
), 4107
(2021
).56.
Nahvi
,
B.
,
Habibi
,
J.
,
Mohammadi
,
K.
,
Shamshirband
,
S.
, and
Al Razgan
,
O. S.
, “
Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature
,” Comput. Electron. Agric.
124
, 150
–160
(2016
).57.
Panda
,
B. S.
,
Lasyasri
,
P. V.
,
Maneesha
,
D.
,
Goutham
,
P.
,
Suresh
,
K.
, and
Pranavsankar
,
C.
, “
A novel approach for weather forecasting using machine learning techniques
,” J. Comput. Sci. Commun. 11, 25
–30
(2020).58.
Pantazi
,
X.-E.
,
Moshou
,
D.
,
Alexandridis
,
T. K.
,
Whetton
,
R. L.
, and
Mouazen
,
A. M.
, “
Wheat yield prediction using machine learning and advanced sensing techniques
,” Comput. Electron. Agric.
121
, 57
–65
(2016a
).59.
Pantazi
,
X.-E.
,
Moshou
,
D.
, and
Bravo
,
C.
, “
Active learning system for weed species recognition based on hyperspectral sensing
,” Biosyst. Eng.
146
, 193
–202
(2016b
).60.
Parashar
,
A.
, “
IoT based automated weather report generation and prediction using machine learning
,” in 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) (ICCT 2019)
(IEEE, 2019
), pp. 339
–344
.61.
Patil
,
A.
and
Deka
,
P.
, “
An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs
,” Comput. Electron. Agric.
121
, 385
–392
(2016
).62.
Périé
,
C.
and
de Blois
,
S.
, “
Dominant forest tree species are potentially vulnerable to climate change over large portions of their range even at high latitudes
,” PeerJ
4
, e2218
(2016
).63.
Paudel
,
D.
,
Boogaard
,
H.
,
de Wit
,
A.
,
Janssen
,
S.
,
Osinga
,
S.
,
Pylianidis
,
C.
, and
Athanasiadis
,
I. N.
, “
Machine learning for large-scale crop yield forecasting
,” Agric. Syst.
187
, 103016
(2021
).64.
Qu
,
Y.
,
Clausen
,
A.
, and
Jørgensen
,
B. N.
, “
Net photosynthesis prediction by deep learning for commercial greenhouse production
,” in Proceedings of the IEEE 25th International Conference on Intelligent Engineering Systems (INES, 2021)
(IEEE, 2021
), p. 000139
.65.
Rana
,
P.
and
Miller
,
D.
, “
Machine learning to analyze the social-ecological impacts of natural resource policy: Insights from community forest management in the Indian Himalaya
,” Environ. Res. Lett.
14
, 024008
(2019
).66.
Rodríguez-Rángel
,
H.
,
Arias
,
D. M.
,
Morales-Rosales
,
L. A.
,
Gonzalez-Huitron
,
V.
,
Valenzuela Partida
,
M.
, and
García
,
J.
, “
Machine learning methods modeling carbohydrate-enriched cyanobacteria biomass production in wastewater treatment systems
,” Energies
15
(7
), 2500
(2022
).67.
Roy Choudhury
,
M.
,
Das
,
S.
,
Christopher
,
J.
,
Apan
,
A.
,
Chapman
,
S.
,
Menzies
,
N. W.
, and
Dang
,
Y. P.
, “
Improving biomass and grain yield prediction of wheat genotypes on sodic soil using integrated high-resolution multispectral, hyperspectral, 3D point cloud, and machine learning techniques
,” Remote Sens.
13
(17
), 3482
(2021
).68.
Robinson
,
S. L.
,
Smith
,
M. D.
,
Richman
,
J. E.
,
Aukema
,
K. G.
, and
Wackett
,
L. P.
, “
Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily
,” Synth. Biol.
5
(1
), ysaa004
(2020
).69.
Saha
,
S.
,
Bhattacharjee
,
S.
,
Shit
,
P. K.
,
Sengupta
,
N.
, and
Bera
,
B.
, “
Deforestation probability assessment using integrated machine learning algorithms of Eastern Himalayan foothills (India)
,” Resour., Conserv. Recycl. Adv.
14
, 200077
(2022
).70.
Saini
,
D. K.
,
Rai
,
A.
,
Devi
,
A.
,
Pabbi
,
S.
,
Chhabra
,
D.
,
Chang
,
J.-S.
, and
Shukla
,
P.
, “
A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive phycobiliproteins production in Nostoc sp. CCC-403
,” Bioresour. Technol.
329
, 124908
(2021
).71.
Salman
,
A. G.
,
Kanigoro
,
B.
, and
Heryadi
,
Y.
, “
Weather forecasting using deep learning techniques
,” in Proceedings of the International Conference on Advanced Computer Science and Information Systems (ICACSIS, 2015)
, 2016
.72.
Saunders
,
A.
,
Drew
,
D. M.
, and
Brink
,
W.
, “
Machine learning models perform better than traditional empirical models for stomatal conductance when applied to multiple tree species across different forest biomes
,” Trees, Forests and People
6
, 100139
(2021
).73.
Silva
,
J. P. M.
,
da Silva
,
M. L. M.
,
de Mendonça
,
A. R.
,
da Silva
,
G. F.
,
de Barros Junior
,
A. A.
,
da Silva
,
E. F.
,
Aguiar
,
M. O.
,
Santos
,
J. S.
, and
Rodrigues
,
N. M. M.
, “
Prognosis of forest production using machine learning techniques
,” Inf. Process. Agric.
10
(1
), 71
–84
(2023
).74.
Suh
,
D.
,
Lee
,
J. W.
,
Choi
,
S.
, and
Lee
,
Y.
, “
Recent applications of deep learning methods on evolution- and contact-based protein structure prediction
,” Int. J. Mol. Sci.
22
(11
), 6032
(2021
).75.
Suresha
,
A. M.
, “
Machine Learning for mining weather patterns and weather forecasting
” (unpublished) (2020
).76.
Thimm
,
O.
,
Bläsing
,
O.
,
Gibon
,
Y.
,
Nagel
,
A.
,
Meyer
,
S.
,
Krüger
,
P.
et al, “
MAPMAN: A user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes
,” Plant J.
37
(6
), 914
–939
(2004
).77.
Tian
,
H.
,
Zhu
,
J.
,
He
,
X.
,
Chen
,
X.
,
Jian
,
Z.
,
Li
,
C.
,
Ou
,
Q.
,
Li
,
Q.
,
Huang
,
G.
,
Liu
,
C.
, and
Xiao
,
W.
, “
Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China
,” For. Ecosyst.
9
, 100037
(2022
).78.
Toklu
,
E.
, “
Biomass energy potential and utilization in Turkey
,” Renewable Energy
107
, 235
–244
(2017
).79.
Uzal
, L. C.
, Grinblat
, G. L.
, Namias
, R.
, Larese
, M. G.
, Bianchi
, J. S.
et al, “
Seed-per-pod estimation for plant breeding using deep learning
,” Comput. Electron. Agric.
150
, 196
–204
(2018
).80.
Wan
,
L.
,
Li
,
S.
,
Chen
,
Y.
,
He
,
Z.
, and
Shi
,
Y.
, “
Application of deep learning in land use classification for soil erosion using remote sensing
,” Front. Earth Sci.
10
, 849531
(2022
).81.
Wang
,
J.
, “
Protein sequence design by deep learning
,” Nat. Comput. Sci.
2
, 416
–417
(2022
).82.
Wang
,
J.
,
Bretz
,
M.
,
Ali Akber Dewan
,
M.
, and
Delavar
,
M. A.
, “
Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
,” Sci. Total Environ.
822
, 153559
(2022a
).83.
Wang
,
Y.
,
Dai
,
X.
,
Fu
,
D.
et al, “
PGD: A machine learning-based photosynthetic-related gene detection approach
,” BMC Bioinf.
23
, 183
(2022b
).84.
Wilhelm
,
C.
,
Weinberg
,
J.
, and
Kaltschmitt
,
M.
, “
Conversion steps in bioenergy production analysis of the energy flow from photon to biofuel
,” Biofuels
5
, 385
–404
(2014
).85.
Windrim
,
L.
,
Bryson
,
M.
,
McLean
,
M.
,
Randle
,
J.
, and
Stone
,
C.
, “
Automated mapping of woody debris over harvested forest plantations using UAVs, high-resolution imagery, and machine learning
,” Remote Sens.
11
(6
), 733
(2019
).86.
Wu
,
Z.
,
Jennifer Kan
,
S. B.
,
Lewis
,
R. D.
,
Wittmann
,
B. J.
, and
Arnold
,
F. H.
, “
Machine learning-assisted directed protein evolution with combinatorial libraries
,” Proc. Natl. Acad. Sci. U. S. A.
116
, 8852
–8858
(2019
).87.
Wu
,
Y.
,
Zhang
,
X.
,
Xiao
,
Y.
, and
Feng
,
J.
, “
Attention neural network for water image classification under IoT environment
,” Appl. Sci.
10
, 030909
(2020
).88.
Yang
,
Z.
,
Tian
,
J.
,
Wang
,
Z.
, and
Feng
,
K.
, “
Monitoring the photosynthetic performance of grape leaves using a hyperspectral-based machine learning model
,” Eur. J. Agron.
140
, 126589
(2022
).89.
Yang
,
P.
,
Zhao
,
Q.
, and
Cai
,
X.
, “
Machine learning based estimation of land productivity in the contiguous US using biophysical predictors
,” Environ. Res. Lett.
15
(7
), 074013
(2020
).90.
Yousafzai
,
A.
,
Manzoor
,
W.
,
Raza
,
G.
,
Mahmood
,
T.
,
Rehman
,
F.
,
Hadi
,
R.
,
Shah
,
S.
,
Amin
,
M.
,
Akhtar
,
A.
,
Bashir
,
S.
,
Habiba
,
U.
, and
Hussain
,
M.
, “
Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
,” Braz. J. Biol.
84
, e253106
(2021
).91.
Zhang
,
Z.
,
Pope
,
M.
,
Shakoor
,
N.
,
Pless
,
R.
,
Mockler
,
T. C.
, and
Stylianou
,
A.
, “
Comparing deep learning approaches for understanding genotype × phenotype interactions in biomass sorghum
,” Front. Artif. Intell.
5
, 872858
(2022a
).92.
Zhang
,
F.
,
Tian
,
X.
,
Zhang
,
H.
, and
Jiang
,
M.
, “
Estimation of aboveground carbon density of forests using deep learning and multisource remote sensing
,” Remote Sens.
14
(13
), 3022
(2022b
).93.
Zhang
,
H.
,
Yin
,
A.
,
Yang
,
X.
,
Fan
,
M.
,
Shao
,
S.
,
Wu
,
J.
,
Wu
,
P.
,
Zhang
,
M.
, and
Gao
,
C.
, “
Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils
,” Ecol. Indic.
122
, 107233
(2021
).94.
Zheng
,
K.
,
Bo
,
Y.
,
Bao
,
Y.
,
Zhu
,
X.
,
Wang
,
J.
, and
Wang
,
Y.
, “
A machine learning model for photorespiration response to multi-factors
,” Horticulturae
7
, 207
(2021
).95.
Zhi
,
W.
,
Feng
,
D.
,
Tsai
,
W. P.
,
Sterle
,
G.
,
Harpold
A.
,
Shen
,
C.
, and
Li
,
L.
, “
From hydrometeorology to river water quality: Can a deep learning model predict dissolved oxygen at the continental scale?
,” Environ. Sci. Technol.
55
, 2357
–2368
(2021
).96.
Zhou
,
J.-J.
,
Zhang
,
Y.-H.
,
Han
,
Z.-M.
,
Liu
,
X.-Y.
,
Jian
,
Y.-F.
,
Hu
,
C.-G.
, and
Dian
,
Y.-Y.
, “
Hyperspectral sensing of photosynthesis, stomatal conductance, and transpiration for citrus tree under drought condition
,” bioRxiv (2021
).© 2023 Author(s). Published under an exclusive license by AIP Publishing.
2023
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