Deep Learning Techniques for Agronomy Applications
Open accessAbstract
This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ML tools for policymakers and agronomists could potentially encourage climate-positive action, e.g., remote sensing with UAVs and satellites could perform methane detection and carbon stock estimation, which could be used to incentivize farmers to sequester more carbon and reduce emissions.
“ML tools for policymakers and agronomists [126] could potentially encourage climate-positive action: for example, remote sensing with UAVs and satellites could perform methane detection and carbon stock estimation, which could be used to incentivize farmers to sequester more carbon and reduce emissions.”
From Tackling climate change with machine learning (2023) · cites this paper as [126]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Deep learning based multi-temporal crop classification
Zhong, L., Hu, L., & Zhou, H. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.032 · Cited by 1,042 · see citing works
Modern Trends in Hyperspectral Image Analysis: A Review
Khan, M. J., Khan, H. S., Yousaf, A., Khurshid, K., & Abbas, A. (2018) · IEEE Access · doi:10.1109/access.2018.2812999 · Cited by 873 · see citing works
Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images
Bah, M. D., Hafiane, A., & Canals, R. (2018) · Remote Sensing · doi:10.3390/rs10111690 · Cited by 313 · see citing works
A New Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of Things
Zhu, J., Song, Y., Jiang, D., & Song, H. H. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2759728 · Cited by 295 · see citing works
IoT based hydroponics system using Deep Neural Networks
Mehra, M., Saxena, S., Sankaranarayanan, S., et al. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.10.015 · Cited by 288 · see citing works
Kerkech, M., Hafiane, A., & Canals, R. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.10.006 · Cited by 276 · see citing works
Saggi, M. K., & Jain, S. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.11.031 · Cited by 240 · see citing works
Amin, S. U., Alsulaiman, M., Muhammad, G., Bencherif, M. A., & Hossain, M. S. (2019) · IEEE Access · doi:10.1109/access.2019.2895688 · Cited by 209 · see citing works
Learning Sparse Representation With Variational Auto-Encoder for Anomaly Detection
Sun, J., Wang, X., Xiong, N. N., & Shao, J. (2018) · IEEE Access · doi:10.1109/access.2018.2848210 · Cited by 184 · see citing works
Yang, Y., Zheng, K., Wu, C., et al. (2019) · Applied Sciences · doi:10.3390/app9020238 · Cited by 167 · see citing works
Yu, Z., Niu, Z., Tang, W., & Wu, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2895604 · Cited by 137 · see citing works
LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan
Haider, S. A., Naqvi, S. R., Akram, T., et al. (2019) · Agronomy · doi:10.3390/agronomy9020072 · Cited by 115 · see citing works
Deep Learning with Unsupervised Data Labeling for Weeds Detection on UAV ImagesPreprint
Bah, M. D., Hafiane, A., & Canals, R. (2018) · Preprints.org · doi:10.20944/preprints201809.0088.v1 · Cited by 100 · see citing works
An Arrival Time Prediction Method for Bus System
Chen, C. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2863555 · Cited by 88 · see citing works
Self-citationMulticolumn bidirectional long short-term memory for mobile devices-based human activity recognition
Tao, D., Wen, Y., & Hong, R. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2561962 · Cited by 83 · see citing works
α-Fraction First Strategy for Hierarchical Model in Wireless Sensor Networks
Pan, J., Kong, L., Sung, T., et al. (2018) · Swinburne figshare (Swinburne University of Technology) · doi:10.3966/160792642018111906009 · Cited by 83 · see citing works
Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks
Fang, L., Cheng, X., Wang, H., et al. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2832071 · Cited by 82 · see citing works
ResInNet: A Novel Deep Neural Network With Feature Reuse for Internet of Things
Sun, X., Gui, G., Li, Y., Liu, R. P., & An, Y. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2853663 · Cited by 81 · see citing works
Dong, W., Wu, T., Luo, J., Sun, Y., & Xia, L. (2019) · Geoderma · doi:10.1016/j.geoderma.2019.01.018 · Cited by 81 · see citing works
A Mobile Positioning Method Based on Deep Learning Techniques
Wu, L., Chen, C., & Qishan, Z. (2019) · Electronics · doi:10.3390/electronics8010059 · Cited by 80 · see citing works
Self-citationAn End-to-End Load Balancer Based on Deep Learning for Vehicular Network Traffic Control
Li, J., Luo, G., Cheng, N., Yuan, Q., Wu, Z., Gao, S., & Liu, Z. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2866435 · Cited by 80 · see citing works
Development of a Mushroom Growth Measurement System Applying Deep Learning for Image Recognition
Lu, C., Liaw, J., Wu, T.-C., et al. (2019) · Agronomy · doi:10.3390/agronomy9010032 · Cited by 74 · see citing works
Álvarez, J. R., Arroqui, M., Mangudo, P., Toloza, J. M., Jatip, D., Rodríguez, J. M., Teyseyre, A., Sanz, C., Zunino, A., Machado, C. F., et al. (2019) · Agronomy · doi:10.3390/agronomy9020090 · Cited by 69 · see citing works
Lin, H.-F., & Chen, C. (2017) · Symmetry · doi:10.3390/sym9070113 · Cited by 62 · see citing works
Self-citationWeb spam classification method based on deep belief networks
Li, Y., Nie, X., & Huang, R. H. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.12.016 · Cited by 59 · see citing works
Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method
Kung, H.-Y., Kuo, T., Chen, C., et al. (2016) · Sustainability · doi:10.3390/su8080735 · Cited by 54 · see citing works
Self-citationAutomatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks
Chen, J., Fan, Y., Wang, T., et al. (2018) · Agronomy · doi:10.3390/agronomy8080129 · Cited by 49 · see citing works
Lin, H.-F., & Chen, C. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.07.050 · Cited by 46 · see citing works
Self-citationJoint Alternate Small Convolution and Feature Reuse for Hyperspectral Image Classification
Gao, H., Yang, Y., Li, C., Zhou, H., & Qu, X. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7090349 · Cited by 46 · see citing works
Jiang, T., Liu, X., & Wu, L. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7110418 · Cited by 36 · see citing works
An Imputation Method for Missing Data Based on an Extreme Learning Machine Auto-Encoder
Lu, C., & Mei, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2868729 · Cited by 31 · see citing works
Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network
Jung, E., Chikontwe, P., Zong, X., Lin, W., Shen, D. G., & Park, S. H. (2019) · IEEE Access · doi:10.1109/access.2019.2896911 · Cited by 31 · see citing works
An Augmented Reality Question Answering System Based on Ensemble Neural Networks
Chen, C., Wu, C. L., Lo, C., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2743746 · Cited by 23 · see citing works
Self-citationVehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
Lai, W. K., Kuo, T., & Chen, C. (2016) · Applied Sciences · doi:10.3390/app6020047 · Cited by 22 · see citing works
Self-citationEmbedding Logic Rules Into Recurrent Neural Networks
Chen, B., Hao, Z., Cai, X., Cai, R., Wen, W., Zhu, J. L., & Xie, G. (2019) · IEEE Access · doi:10.1109/access.2019.2892140 · Cited by 18 · see citing works
Research into the E-Learning Model of Agriculture Technology Companies: Analysis by Deep Learning
Lin, C.-H., Wang, W., Liu, C.-Y., et al. (2019) · Agronomy · doi:10.3390/agronomy9020083 · Cited by 18 · see citing works
Entity representation for pairwise collaborative ranking using restricted Boltzmann machine
Hazrati, N., Shams, B., & Haratizadeh, S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.013 · Cited by 17 · see citing works
Ding, L., Xiao, L., Zhou, K.-Q., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2896983 · Cited by 17 · see citing works
Learning Robust Auto-Encoders With Regularizer for Linearity and Sparsity
Shi, Y., Lei, M., Ma, R., & Niu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2895884 · Cited by 16 · see citing works
Traffic State Estimation of Signalized Intersections Based on Stacked Denoising Auto-Encoder Model
Xiang, J., & Chen, Z. (2018) · Wireless Personal Communications · doi:10.1007/s11277-018-5466-2 · Cited by 11 · see citing works
Echo-State Restricted Boltzmann Machines: A Perspective on Information Compensation
Sun, X., Li, Y., Gui, G., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2893252 · Cited by 11 · see citing works
Tang, X., Wei, H., & Hao, K. (2018) · IEEE Access · doi:10.1109/access.2018.2885006 · Cited by 10 · see citing works
Dempster–Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation
Lee, S., & Chang, J. (2018) · Applied Sciences · doi:10.3390/app9010096 · Cited by 10 · see citing works
Recurrent Neural Networks With Finite Memory Length
Long, D., Zhang, R., & Mao, Y. (2019) · IEEE Access · doi:10.1109/access.2018.2890297 · Cited by 10 · see citing works
Lin, H. F., & Chen, C. (2019) · doi:10.3966/160792642019012001025 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Chen, C.H. Reducing the Dimensionality of Time-Series Data with Deep Learning Techniques. Science 2006, 313, 504-507. Available online: http://science.sciencemag.org/content/313/5786/504/tab-e-letters (accessed on 13 March 2019).
Cited by
Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Chen, C.-H., Kung, H.-Y., & Hwang, F.-J.
- Affiliations
- Fuzhou University (CN)
- National Pingtung University of Science and Technology (TW)
- University of Technology Sydney (AU)
- Corresponding author
- Chen, Chi-Hua
- Venue
- Agronomy
- Publisher
- MDPI AG
- Published
- 2019-03-20
- Volume
- 9
- Issue
- 3
- Pages
- 142
- ISSN
- 2073-4395 (ISSN-L)
- OpenAlex
- W2924759635
- Cited by
- 29
- References
- 46
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 1 · 2023: 3 · 2022: 5 · 2021: 7 · 2020: 6 · 2019: 2
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Agronomy (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- Fuzhou University (510730/XRC-18075)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- image recognition, convolutional neural networks, depth images, wheat production forecasting, LSTM, transfer learning, model ensembling, agricultural technology companies, online learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
