Deep Learning for Image-Based Plant Growth Monitoring: A Review
Deep learning (DL) approaches have received extensive attention in plant growth monitoring due to their ground-breaking performance in image classification; however, the approaches have yet to be fully explored. This review article, therefore, aims to provide a comprehensive overview of the work and the DL developments accomplished over the years. This work includes a brief introduction on plant growth monitoring and the image-based techniques used for phenotyping. The bottleneck in image analysis is discussed and the need of DL methods in plant growth monitoring is highlighted. A number of research works focused on DL based plant growth monitoring-related applications published since 2017 have been identified and included in this work for review. The results show that the advancement in DL approaches has driven plant growth monitoring towards more complicated schemes, from simple growth stages identification towards temporal growth information extraction. The challenges, such as resource-demanding data annotation, data-hungriness for training, and extraction of both spatial and temporal features simultaneously for accurate plant growth prediction, however, remain unsolved.
S. L. Althaus, M. R. Berenbaum, J. Jordan, and D. A. Shalmon, “No Buzz for Bees: Media Coverage of Pollinator Decline,” Proceedings of the National Academy of Sciences, vol. 118, no. 2, Article no. e2002552117, January 2021.
E. Fereres, F. Orgaz, and V. Gonzalez-Dugo, “Reflections on Food Security under Water Scarcity,” Journal of Experimental Botany, vol. 62, no. 12, pp. 4079-4086, August 2011.
P. J. Gregory, J. S. Ingram, and M. Brklacich, “Climate Change and Food Security,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 360, no. 1463, pp. 2139-2148, November 2005.
“World Population Prospects 2019: Data Booklet”, https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf, June 21, 2019.
M. F. Dreccer, G. Molero, C. Rivera-Amado, C. John-Bejai, and Z. Wilson, “Yielding to the Image: How Phenotyping Reproductive Growth Can Assist Crop Improvement and Production,” Plant Science, vol. 282, pp. 73-82, May 2019.
T. T. Tran, J. W. Choi, T. T. H. Le, and J. W. Kim, “A Comparative Study of Deep CNN in Forecasting and Classifying the Macronutrient Deficiencies on Development of Tomato Plant,” Applied Sciences, vol. 9, no. 8, Article no. 1601, April 2019.
G. Xing, K. Liu, and J. Gai, “A High-Throughput Phenotyping Procedure for Evaluation of Antixenosis against Common Cutworm at Early Seedling Stage in Soybean,” Plant Methods, vol. 13, no. 1, Article no. 66, August 2017.
R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, “Performance of Deep Learning vs Machine Learning in Plant Leaf Disease Detection,” Microprocessors and Microsystems, vol. 80, Article no. 103615, February 2021.
R. Bhagwat and Y. Dandawate, “A Review on Advances in Automated Plant Disease Detection,” International Journal of Engineering and Technology Innovation, vol. 11, no. 4, pp. 251-264, September 2021.
K. Mochida, D. Saisho, and T. Hirayama, “Crop Improvement Using Life Cycle Datasets Acquired under Field Conditions,” Frontiers in Plant Science, vol. 6, Article no. 740, September 2015.
S. A. Prado, L. Cabrera-Bosquet, A. Grau, A. Coupel-Ledru, E. J. Millet, C. Welcker, et al., “Phenomics Allows Identification of Genomic Regions Affecting Maize Stomatal Conductance with Conditional Effects of Water Deficit and Evaporative Demand,” Plant, Cell, and Environment, vol. 41, no. 2, pp. 314-326, February 2018.
X. Zhang, C. Huang, D. Wu, F. Qiao, W. Li, L. Duan, et al., “High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth,” Plant Physiology, vol. 173, no. 3, pp. 1554-1564, January 2017.
J. Heaton, “An Empirical Analysis of Feature Engineering for Predictive Modeling,” SoutheastCon, pp. 1-6, April 2016.
U. Shruthi, V. Nagaveni, and B. K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection,” 5th International Conference on Advanced Computing and Communication Systems, pp. 281-284, March 2019.
W. Yi, S. Dai, Y. Jiang, C. Yuan, and L. Yang, “Computer-Aided Visual Modeling of Rice Leaf Growth Based on Machine Learning,” 23rd International Conference on Soft Computing and Measurements, pp. 226-229, May 2020.
A. Paturkar, G. S. Gupta, and D. Bailey, “Plant Trait Segmentation for Plant Growth Monitoring,” 35th International Conference on Image and Vision Computing New Zealand, pp. 1-6, November 2020.
A. H. B. A. Wahab, R. Zahari, and T. H. Lim, “Detecting Diseases in Chilli Plants Using K-Means Segmented Support Vector Machine,” 3rd International Conference on Imaging, Signal Processing, and Communication, pp. 57-61, July 2019.
B. Patel and A. Sharaff, “Feature Fusion Based Growth Analysis of Chhattisgarh Rice Plants Using Machine Learning Technique,” 7th International Conference on Signal Processing and Integrated Networks, pp. 814-818, February 2020.
B. Bose, J. Priya, S. Welekar, and Z. Gao, “Hemp Disease Detection and Classification Using Machine Learning and Deep Learning,” International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom), pp. 762-769, November 2020.
M. Hesami and A. M. P. Jones, “Modeling and Optimizing Callus Growth and Development in Cannabis Sativa Using Random Forest and Support Vector Machine in Combination with a Genetic Algorithm,” Applied Microbiology Biotechnology, vol. 105, no. 12, pp. 5201-5212, June 2021.
N. Nandhini and J. G. Shankar, “Prediction of Crop Growth Using Machine Learning Based on Seed Features,” ICTACT Journal on Soft Computing, vol. 11, no. 1, pp. 2232-2236, October 2020.
J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, “Deep Learning in Computer Vision: A Critical Review of Emerging Techniques and Application Scenarios,” Machine Learning with Applications, vol. 6, Article no. 100134, December 2021.
N. O’Mahony, S. Campbell, A. Carvalho, S. Harapanahalli, G. V. Hernandez, L. Krpalkova, et al., “Deep Learning vs. Traditional Computer Vision,” https://arxiv.org/ftp/arxiv/papers/1910/1910.13796.pdf, October 10, 2015.
D. Radovanović and S. Đukanović, “Image-Based Plant Disease Detection: A Comparison of Deep Learning and Classical Machine Learning Algorithms,” 24th International Conference on Information Technology, pp. 1-4, February 2020.
S. Srinivas, R. K. Sarvadevabhatla, K. R. Mopuri, N. Prabhu, S. S. S. Kruthiventi, and R. V. Babu, “A Taxonomy of Deep Convolutional Neural Nets for Computer Vision,” Frontiers in Robotics and AI, vol. 2, Article no. 36, January 2016.
J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440, June 2015.
H. Snyder, “Literature Review as a Research Methodology: An Overview and Guidelines,” Journal of Business Research, vol. 104, pp. 333-339, November 2019.
J. J. Olas, F. Fichtner, and F. Apelt, “All Roads Lead to Growth: Imaging-Based and Biochemical Methods to Measure Plant Growth,” Journal of Experimental Botany, vol. 71, no. 1, pp. 11-21, January 2020.
O. Baddour, H. Kontongomde, E. Koch, E. Bruns, F. M. Chmielewski, C. Defila, et al., Guidelines for Plant Phenological Observations, Geneva: World Meteorological Organization, 2009.
U. Meier, H. Bleiholder, L. Buhr, C. Feller, H. Hack, M. Heß, et al., “The BBCH System to Coding the Phenological Growth Stages of Plants—History and Publications,” Plant, vol. 61, no. 2, pp. 41-52, February 2009.
G. Zhao, Y. Gao, S. Gao, Y. Xu, J. Liu, C. Sun, et al., “The Phenological Growth Stages of Sapindus Mukorossi According to BBCH Scale,” Forests, vol. 10, no. 6, Article no. 462, May 2019.
C. Campillo, M. I. García, C. Daza, and M. H. Prieto, “Study of a Non-Destructive Method for Estimating the Leaf Area Index in Vegetable Crops Using Digital Images,” HortScience Horts, vol. 45, no. 10, pp. 1459-1463, October 2010.
S. D. Choudhury, S. Goswami, S. Bashyam, T. Awada, and A. Samal, “Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis,” Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2022-2029, October 2017.
B. Chacón, R. Ballester, V. Birlanga, A. G. Rolland-Lagan, and J. M. Pérez-Pérez, “A Quantitative Framework for Flower Phenotyping in Cultivated Carnation (Dianthus Caryophyllus L.),” PLOS ONE, vol. 8, Article no. e82165, December 2013.
D. F. M. Cortes, R. S. Catarina, G. B. D. A. Barros, F. A. S. Arêdes, S. F. d. Silveira, G. A. Ferreguetti, et al., “Model-Assisted Phenotyping by Digital Images in Papaya Breeding Program,” Scientia Agricola, vol. 74, pp. 294-302, August 2017.
A. H. Rosemartin, E. G. Denny, K. L. Gerst, R. L. Marsh, E. E. Posthumus, T. M. Crimmins, et al., “USA National Phenology Network Observational Data Documentation,” U.S. Department of the Interior and U.S. Geological Survey, Report 2018-1060, April 25, 2018.
S. Das Choudhury, A. Samal, and T. Awada, “Leveraging Image Analysis for High-Throughput Plant Phenotyping,” Frontiers in Plant Science, vol. 10, Article no. 508, April 2019.
Z. Li, R. Guo, M. Li, Y. Chen, and G. Li, “A Review of Computer Vision Technologies for Plant Phenotyping,” Computers and Electronics in Agriculture, vol. 176, Article no. 105672, September 2020.
L. Li, Q. Zhang, and D. Huang, “A Review of Imaging Techniques for Plant Phenotyping,” Sensors, vol. 14, no. 11, pp. 20078-20111, November 2014.
A. Brugger, J. Behmann, S. Paulus, H. G. Luigs, M. T. Kuska, P. Schramowski, et al., “Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range,” Remote Sensing, vol. 11, no. 12, Article no. 1401, June 2019.
P. Mishra, S. Lohumi, H. A. Khan, and A. Nordon, “Close-Range Hyperspectral Imaging of Whole Plants for Digital Phenotyping: Recent Applications and Illumination Correction Approaches,” Computers and Electronics in Agriculture, vol. 178, Article no. 105780, November 2020.
Z. Khan, V. Rahimi-Eichi, S. Haefele, T. Garnett, and S. J. Miklavcic, “Estimation of Vegetation Indices for High-Throughput Phenotyping of Wheat Using Aerial Imaging,” Plant Methods, vol. 14, no. 1, Article no. 20, March 2018.
M. Sancho-Adamson, M. I. Trillas, J. Bort, J. A. Fernandez-Gallego, and J. Romanyà, “Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios,” Remote Sensing, vol. 11, no. 6, Article no. 607, March 2019.
S. C. Kefauver, G. El-Haddad, O. Vergara-Diaz, and J. L. Araus, “RGB Picture Vegetation Indexes for High-Throughput Phenotyping Platforms (HTPPs),” Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, vol. 9637, Article no. 96370J, October 2015.
B. T. W. Putra, P. Soni, B. Marhaenanto, S. S. Harsono, and S. Fountas, “Using Information from Images for Plantation Monitoring: A Review of Solutions for Smallholders,” Information Processing in Agriculture, vol. 7, no. 1, pp. 109-119, March 2020.
M. L. Pérez-Bueno, M. Pineda, and M. Barón, “Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging,” Frontiers in Plant Science, vol. 10, Article no. 1135, September 2019.
J. Yao, D. Sun, H. Cen, H. Xu, H. Weng, F. Yuan, et al., “Phenotyping of Arabidopsis Drought Stress Response Using Kinetic Chlorophyll Fluorescence and Multicolor Fluorescence Imaging,” Frontiers in Plant Science, vol. 9, Article no. 603, May 2018.
I. Leinonen, O. M. Grant, C. P. P. Tagliavia, M. M. Chaves, and H. G. Jones, “Estimating Stomatal Conductance with Thermal Imagery,” Plant, Cell, and Environment, vol. 29, pp. 1508-1518, August 2006.
V. Sagan, M. Maimaitijiang, P. Sidike, K. Eblimit, K. T. Peterson, S. Hartling, et al., “UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras,” Remote Sensing, vol. 11, no. 3, Article no. 330, February 2019.
J. Urban, M. Ingwers, M. A. McGuire, and R. O. Teskey, “Stomatal Conductance Increases with Rising Temperature,” Plant Signaling and Behavior, vol. 12, no. 8, Article no. e1356534, August 2017.
C. Baer, S. Gutierrez, J. Jebramcik, J. Barowski, F. Vega, and I. Rolfes, “Ground Penetrating Synthetic Aperture Radar Imaging Providing Soil Permittivity Estimation,” IEEE MTT-S International Microwave Symposium, pp. 1367-1370, October 2017.
T. Roitsch, L. Cabrera-Bosquet, A. Fournier, K. Ghamkhar, J. Jiménez-Berni, F. Pinto, and E. S. Ober, “Review: New Sensors and Data-Driven Approaches—A Path to Next Generation Phenomics,” Plant Science, vol. 282, pp. 2-10, May 2019.
Y. Lin, “LiDAR: An Important Tool for Next-Generation Phenotyping Technology of High Potential for Plant Phenomics?” Computers and Electronics in Agriculture, vol. 119, pp. 61-73, November 2015.
C. S. Bekkering, J. Huang, and L. Tian, “Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement,” Agronomy, vol. 10, no. 9, Article no. 1287, August 2020.
F. Baret, S. Madec, K. Irfan, J. Lopez, A. Comar, M. Hemmerlé, et al., “Leaf-Rolling in Maize Crops: From Leaf Scoring to Canopy-Level Measurements for Phenotyping,” Journal of Experimental Botany, vol. 69, no. 10, pp. 2705-2716, April 2018.
M. Tattaris, M. P. Reynolds, and S. C. Chapman, “A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding,” Frontiers in Plant Science, vol. 7, Article no. 1131, August 2016.
O. N. Lungu, L. M. Chabala, and C. Shepande, “Satellite-Based Crop Monitoring and Yield Estimation—A Review,” Journal of Agricultural Science, vol. 13, no. 1, pp. 180-194, December 2020.
C. Xie and C. Yang, “A Review on Plant High-Throughput Phenotyping Traits Using UAV-Based Sensors,” Computers and Electronics in Agriculture, vol. 178, Article no. 105731, November 2020.
D. C. Tsouros, S. Bibi, and P. G. Sarigiannidis, “A Review on UAV-Based Applications for Precision Agriculture,” Information, vol. 10, no. 11, Article no. 349, November 2019.
S. Tisné, Y. Serrand, L. Bach, E. Gilbault, R. Ben Ameur, H. Balasse, et al., “Phenoscope: An Automated Large-Scale Phenotyping Platform Offering High Spatial Homogeneity,” The Plant Journal, vol. 74, no. 3, pp. 534-544, May 2013.
S. Shajahan, I. Cannayen, and J. Hendrickson, “Monitoring Plant Phenology Using Phenocam: A Review,” ASABE Annual International Meeting, Article no. 162461829, July 2016.
J. Underwood, A. Wendel, B. Schofield, L. McMurray, and R. Kimber, “Efficient In-Field Plant Phenomics for Row-Crops with an Autonomous Ground Vehicle,” Journal of Field Robotics, vol. 34, no. 6, pp. 1061-1083, September 2017.
A. Gebremedhin, P. Badenhorst, J. Wang, K. Giri, G. Spangenberg, and K. Smith, “Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program,” Remote Sensing, vol. 11, no. 21, Article no. 2494, October 2019.
J. Zhang, Y. Huang, R. Pu, P. Gonzalez-Moreno, L. Yuan, K. Wu, et al., “Monitoring Plant Diseases and Pests through Remote Sensing Technology: A Review,” Computers and Electronics in Agriculture, vol. 165, Article no. 104943, October 2019.
M. Minervini, H. Scharr, and S. A. Tsaftaris, “Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner],” IEEE Signal Processing Magazine, vol. 32, no. 4, pp. 126-131, July 2015.
C. Zhao, Y. Zhang, J. Du, X. Guo, W. Wen, S. Gu, et al., “Crop Phenomics: Current Status and Perspectives,” Frontiers in Plant Science, vol. 10, Article no. 714, June 2019.
A. Shrestha and A. Mahmood, “Review of Deep Learning Algorithms and Architectures,” IEEE Access, vol. 7, pp. 53040-53065, April 2019.
X. Yang and M. Sun, “A Survey on Deep Learning in Crop Planting,” IOP Conference Series: Materials Science and Engineering, vol. 490, Article no. 062053, April 2019.
M. P. Pound, J. A. Atkinson, A. J. Townsend, M. H. Wilson, M. Griffiths, A. S. Jackson, et al., “Deep Machine Learning Provides State-of-the-Art Performance in Image-Based Plant Phenotyping,” GigaScience, vol. 6, no. 10, pp. 1-10, October 2017.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communication of the ACM, vol. 60, pp. 84-90, June 2017.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436-444, May 2015.
A. Kamilaris and F. X. Prenafeta-Boldú, “A Review of the Use of Convolutional Neural Networks in Agriculture,” The Journal of Agricultural Science, vol. 156, no. 3, pp. 312-322, June 2018.
S. Samiei, P. Rasti, J. Ly Vu, J. Buitink, and D. Rousseau, “Deep Learning-Based Detection of Seedling Development,” Plant Methods, vol. 16, no. 1, Article no. 103, July 2020.
N. Genze, R. Bharti, M. Grieb, S. J. Schultheiss, and D. G. Grimm, “Accurate Machine Learning-Based Germination Detection, Prediction and Quality Assessment of Three Grain Crops,” Plant Methods, vol. 16, no. 1, Article no. 157, December 2020.
J. R. Ubbens and I. Stavness, “Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks,” Frontiers in Plant Science, vol. 8, Article no. 1190, July 2017.
Y. Jiang, C. Li, R. Xu, S. Sun, J. S. Robertson, and A. H. Paterson, “DeepFlower: A Deep Learning-Based Approach to Characterize Flowering Patterns of Cotton Plants in the Field,” Plant Methods, vol. 16, no. 1, Article no. 156, July 2020.
Y. Perugachi-Diaz, J. M. Tomczak, and S. Bhulai, “Deep Learning for White Cabbage Seedling Prediction,” Computers and Electronics in Agriculture, vol. 184, Article no. 106059, May 2021.
A. Bauer, A. G. Bostrom, J. Ball, C. Applegate, T. Cheng, S. Laycock, et al., “Combining Computer Vision and Deep Learning to Enable Ultra-Scale Aerial Phenotyping and Precision Agriculture: A Case Study of Lettuce Production,” Horticulture Research, vol. 6, Article no. 70, June 2019.
L. Zhang, Z. Xu, D. Xu, J. Ma, Y. Chen, and Z. Fu, “Growth Monitoring of Greenhouse Lettuce Based on a Convolutional Neural Network,” Horticulture Research, vol. 7, Article no. 124, August 2020.
J. Y. Lu, C. L. Chang, and Y. F. Kuo, “Monitoring Growth Rate of Lettuce Using Deep Convolutional Neural Networks,” ASABE Annual International Meeting, Article no. 1900341, July 2019.
S. V. Desai, V. N. Balasubramanian, T. Fukatsu, S. Ninomiya, and W. Guo, “Automatic Estimation of Heading Date of Paddy Rice Using Deep Learning,” Plant Methods, vol. 15, no. 1, Article no. 76, July 2019.
T. Yamaguchi, Y. Tanaka, Y. Imachi, M. Yamashita, and K. Katsura, “Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice,” Remote Sensing, vol. 13, no. 1, Article no. 84, December 2021.
A. Nasiri, A. Taheri-Garavand, and Y. D. Zhang, “Image-Based Deep Learning Automated Sorting of Date Fruit,” Postharvest Biology and Technology, vol. 153, pp. 133-141, July 2019.
X. Ni, C. Li, H. Jiang, and F. Takeda, “Deep Learning Image Segmentation and Extraction of Blueberry Fruit Traits Associated with Harvestability and Yield,” Horticulture Research, vol. 7, Article no. 110, July 2020.
M. Afonso, H. Fonteijn, F. S. Fiorentin, D. Lensink, M. Mooij, N. Faber, et al., “Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning,” Frontiers in Plant Science, vol. 11, Article no. 1759, November 2020.
Y. Tian, G. Yang, Z. Wang, H. Wang, E. Li, and Z. Liang, “Apple Detection during Different Growth Stages in Orchards Using the Improved YOLO-V3 Model,” Computers and Electronics in Agriculture, vol. 157, pp. 417-426, February 2019.
A. A. Azman and F. S. Ismail, “Convolutional Neural Network for Optimal Pineapple Harvesting,” ELEKTRIKA—Journal of Electrical Engineering, vol. 16, no.2, pp. 1-4, August 2017.
N. Teimouri, M. Dyrmann, P. R. Nielsen, S. K. Mathiassen, G. J. Somerville, and R. N. Jørgensen, “Weed Growth Stage Estimator Using Deep Convolutional Neural Networks,” Sensors, vol. 18, no. 5, Article no. 1580, May 2018.
X. Hao, J. Jia, A. M. Khattak, L. Zhang, X. Guo, W. Gao, et al., “Growing Period Classification of Gynura Bicolor DC Using GL-CNN,” Computers and Electronics in Agriculture, vol. 174, Article no. 105497, July 2020.
S. Rasti, C. J. Bleakley, G. C. M. Silvestre, N. M. Holden, D. Langton, and G. M. P. O’Hare, “Crop Growth Stage Estimation Prior to Canopy Closure Using Deep Learning Algorithms,” Neural Computing and Applications, vol. 33, no. 5, pp. 1733-1743, March 2021.
A. Reyes-Yanes, P. Martinez, and R. Ahmad, “Real-Time Growth Rate and Fresh Weight Estimation for Little Gem Romaine Lettuce in Aquaponic Grow Beds,” Computers and Electronics in Agriculture, vol. 179, Article no. 105827, December 2020.
R. G. D. Luna, E. P. Dadios, A. A. Bandala, and R. R. P. Vicerra, “Tomato Growth Stage Monitoring for Smart Farm Using Deep Transfer Learning with Machine Learning-Based Maturity Grading,” Journal of Agricultural Science, vol. 42, no. 1, pp. 24-36, January 2020.
A. Koirala, K. B. Walsh, Z. Wang, and N. Anderson, “Deep Learning for Mango (Mangifera indica) Panicle Stage Classification,” Agronomy, vol. 10, no. 1, Article no. 143, January 2020.
S. Parvathi and S. Tamil Selvi, “Detection of Maturity Stages of Coconuts in Complex Background Using Faster R-CNN Model,” Biosystems Engineering, vol. 202, pp. 119-132, February 2021.
P. Shan, “Image Segmentation Method Based on K-Mean Algorithm,” EURASIP Journal on Image and Video Processing, vol. 2018, Article no. 81, March 2018.
G. Bebis and M. Georgiopoulos, “Feed-Forward Neural Networks,” IEEE Potentials, vol. 13, no. 4, pp. 27-31, October 1994.
S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative Analysis of Deep Learning Image Detection Algorithms,” Journal of Big Data, vol. 8, no. 1, Article no. 66, May 2021.
M. Hashemi, “Enlarging Smaller Images before Inputting into Convolutional Neural Network: Zero-Padding vs. Interpolation,” Journal of Big Data, vol. 6, no. 1, Article no. 98, November 2019.
M. Capra, B. Bussolino, A. Marchisio, M. Shafique, G. Masera, and M. Martina, “An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks,” Future Internet, vol. 12, no. 7, Article no. 113, July 2020.
W. Wang and Y. Yang, “Development of Convolutional Neural Network and Its Application in Image Classification: A Survey,” Optical Engineering, vol. 58, no. 4, Article no. 040901, April 2019.
M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” European Conference of Computer Vision, pp. 818-833, September 2014.
K. Simonyan and A. J. C. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” https://arxiv.org/pdf/1409.1556.pdf, April 10, 2015.
C. Szegedy, L. Wei, J. Yangqing, P. Sermanet, S. Reed, D. Anguelov, et al., “Going Deeper with Convolutions,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, June 2015.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, June 2016.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Weinberger, “Densely Connected Convolutional Networks,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261-2269, July 2017.
X. Glorot and Y. Bengio, “Understanding the Difficulty of Training Deep Feedforward Neural Networks,” 13th International Conference on Artificial Intelligence and Statistics, pp. 249-256, May 2010.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, pp. 1735-1780, November 1997.
Z. Karevan and J. A. K. Suykens, “Transductive LSTM for Time-Series Prediction: An Application to Weather Forecasting,” Neural Networks, vol. 125, pp. 1-9, May 2020.
H. Sak, A. Senior, and F. J. A. Beaufays, “Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition,” https://arxiv.org/pdf/1402.1128.pdf, February 05, 2014.
V. Frinken, F. Zamora-Martínez, S. España-Boquera, M. J. Castro-Bleda, A. Fischer, and H. Bunke, “Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition,” 21st International Conference on Pattern Recognition, pp. 701-704, November 2012.
J. Zhu, H. Chen, and W. Ye, “A Hybrid CNN-LSTM Network for the Classification of Human Activities Based on Micro-Doppler Radar,” IEEE Access, vol. 8, pp. 24713-24720, February 2020.
C. Uyulan, “Development of LSTM & CNN Based Hybrid Deep Learning Model to Classify Motor Imagery Tasks,” https://www.biorxiv.org/content/biorxiv/early/2020/12/28/2020.09.20.305300.full.pdf, November 23, 2020.
M. Alhussein, K. Aurangzeb, and S. I. Haider, “Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting,” IEEE Access, vol. 8, pp. 180544-180557, October 2020.
J. Donahue, L. A. Hendricks, M. Rohrbach, S. Venugopalan, S. Guadarrama, K. Saenko, et al., “Long-Term Recurrent Convolutional Networks for Visual Recognition and Description,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, pp. 677-691, April 2017.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, June 2016.
J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 6517-6525, July 2017.
J. Redmon and A. J. A. Farhadi, “YOLOv3: An Incremental Improvement,” https://arxiv.org/pdf/1804.02767.pdf, April 08, 2018.
A. Bochkovskiy, C. Y. Wang, and H. J. A. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” https://arxiv.org/pdf/2004.10934.pdf, April 23, 2020.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, June 2014.
R. Girshick, “Fast R-CNN,” IEEE International Conference on Computer Vision, pp. 1440-1448, December 2015.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, June 2017.
S. A. Sanchez, H. J. Romero, and A. D. Morales, “A Review: Comparison of Performance Metrics of Pretrained Models for Object Detection Using the TensorFlow Framework,” IOP Conference Series: Materials Science and Engineering, vol. 844, Article no. 012024, June 2020.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” IEEE International Conference on Computer Vision, pp. 2980-2988, October 2017.
S. Paulus, “Measuring Crops in 3D: Using Geometry for Plant Phenotyping,” Plant Methods, vol. 15, no. 1, Article no. 103, September 2019.
M. A. Ganaie, M. Hu, M. Tanveer, and P. J. A. Suganthan, “Ensemble Deep Learning: A Review,” https://arxiv.org/pdf/2104.02395.pdf, April 06, 2021.
S. Sakurai, H. Uchiyama, A. Shimada, and R. I. Taniguchi, “Plant Growth Prediction Using Convolutional LSTM,” 14th International Conference on Computer Vision Theory and Applications, pp. 105-113, February 2019.
R. Yasrab, J. Zhang, P. Smyth, and M. P. Pound, “Predicting Plant Growth from Time-Series Data Using Deep Learning,” Remote Sensing, vol. 13, no. 3, Article no. 331, January 2021.
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