A Systematic Review of the Literature on Machine Learning Methods Applied to High Throughput Phenotyping in Agricultural Production
Keywords:
High Throughput Phenotyping, Agricultural Production, Digital Image Processing, Machine Learning, Systematic Literature Review, Unmanned Aerial VehiclesAbstract
The amount of images that can be extracted from crops such as soybean, corn, sorghum, etc., has increased exponentially due to the proliferation of remote sensing technologies such as Unmanned Aerial Vehicles (UAV). When processed and analyzed, images can provide valuable information and knowledge about High Throughput Phenotyping (HTP). Advances in HTP technology are essential to ensure that crop genetic improvement meets future global demands for food and fuel. In addition to UAVs, Digital Image Processing (DIP) and Machine Learning (ML) methods have shown to be promising tools in HTP to minimize the time and cost of analyzing entire crops. However, the performance and quality of the results obtained in HTP depend on the techniques used throughout the process. With this limitation in mind, the objective of this article is to present a Systematic Literature Review (SLR) on image capture techniques, DIP and ML applied to HTP. This review focuses on four sources of scientific searches, which initially returned 161 articles to be analyzed, of which 46 were excluded due to the Exclusion Criteria (EC), and 43 were duplicates, leaving only 72 for full reading. Of the 72 articles read, 27 were excluded due to the Exclusion and Quality Criteria (QC). Finally, 45 studies remained, resulting in a useful base on the cameras/sensors used in capturing the images, the most analyzed agronomic traits in the crops, in addition to a survey on the main DIP, ML techniques used in HTP.
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UN, “Un. united nations|population division..” https://www.un.org/ development/desa/pd/, 2021. Acessado: 2021-11-13.
C. Costa, U. Schurr, F. Loreto, P. Menesatti, and S. Carpentier, “Plant phenotyping research trends, a science mapping approach,” Frontiers in plant science, vol. 9, p. 1933, 2019.
A. Shafiekhani, F. B. Fritschi, and G. N. DeSouza, “A new 4d-RGB mapping technique for field-based high-throughput phenotyping.,” in BMVC, p. 322, 2018.
H. Hong, J. Benac, D. Riggsbee, and K. Koutsky, “High throughput imaging and analysis for biological interpretation of agricultural plants and environmental interaction,” in Image Processing: Machine Vision Applications VII, vol. 9024, pp. 93–105, SPIE, 2014.
A. Kumar, M. Taparia, P. Rajalakshmi, W. Guo, B. Balaji Naik, B. Marathi, and U. Desai, “Uav based remote sensing for tassel detection and growth stage estimation of maize crop using multispectral images,” pp. 1588–1591, 2020.
P. Pandey, K. Payn, Y. Lu, A. Heine, T. Walker, J. Acosta, and S. Young, “Hyperspectral imaging combined with machine learning for the detection of fusiform rust disease incidence in loblolly pine seedlings,” Remote Sensing, vol. 13, no. 18, 2021. cited By 4.
J. Zhang, X. Feng, Q. Wu, G. Yang, M. Tao, Y. Yang, and Y. He, “Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning,” Plant methods, vol. 18, no. 1, pp. 1–16, 2022.
M. Maimaitijiang, V. Sagan, P. Sidike, S. Hartling, F. Esposito, and F. Fritschi, “Soybean yield prediction from UAV using multimodal data fusion and deep learning,” Remote Sensing of Environment, vol. 237, 2020.
N. Yu, L. Li, N. Schmitz, L. Tian, J. Greenberg, and B. Diers, “Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle-based platform,” Remote Sensing of Environment, vol. 187, pp. 91–101, 2016.
J. Zhou, X. Fu, S. Zhou, and J. Zhou, “Evaluation of the performance of machine learning methods in soybean segmentation for image-based high-throughput phenotyping in the greenhouse,” 2018.
R. T. Furbank and M. Tester, “Phenomics–technologies to relieve the phenotyping bottleneck,” Trends in plant science, vol. 16, no. 12, pp. 635–644, 2011.
N. Fahlgren, M. A. Gehan, and I. Baxter, “Lights, camera, action: high-throughput plant phenotyping is ready for a close-up,” Current opinion in plant biology, vol. 24, pp. 93–99, 2015.
L. F. Mota, S. Pegolo, T. Baba, F. Peñagaricano, G. Morota, G. Bittante, and A. Cecchinato, “Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data,” Journal of Dairy Science, vol. 104, no. 7, pp. 8107–8121, 2021.
O. Coast, S. Shah, A. Ivakov, O. Gaju, P. B. Wilson, B. C. Posch, C. J. Bryant, A. C. A. Negrini, J. R. Evans, A. G. Condon, et al., “Predicting dark respiration rates of wheat leaves from hyperspectral reflectance,” Plant, Cell & Environment, vol. 42, no. 7, pp. 2133–2150, 2019.
A. Moghimi, C. Yang, and P. M. Marchetto, “Ensemble feature selection for plant phenotyping: a journey from hyperspectral to multispectral imaging,” IEEE Access, vol. 6, pp. 56870–56884, 2018.
R. Khanna, L. Schmid, A. Walter, J. Nieto, R. Siegwart, and F. Liebisch, “A spatiotemporal spectral framework for plant stress phenotyping,” Plant methods, vol. 15, no. 1, pp. 1–18, 2019.
J. L. Araus and J. E. Cairns, “Field high-throughput phenotyping: the new crop breeding frontier,” Trends in plant science, vol. 19, no. 1, pp. 52–61, 2014.
W. Yang, H. Feng, X. Zhang, J. Zhang, J. H. Doonan, W. D. Batchelor, L. Xiong, and J. Yan, “Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives,” Molecular Plant, vol. 13, no. 2, pp. 187–214, 2020.
P. Kumar, R. L. Eriksen, I. Simko, and B. Mou, “Molecular mapping of water-stress responsive genomic loci in lettuce (lactuca spp.) using kinetics chlorophyll fluorescence, hyperspectral imaging, and machine learning,” Frontiers in genetics, vol. 12, p. 191, 2021.
M. V. Giuffrida, P. Doerner, and S. A. Tsaftaris, “Pheno-deep counter: A unified and versatile deep learning architecture for leaf counting,” The Plant Journal, vol. 96, no. 4, pp. 880–890, 2018.
B. Das, K. Manohara, G. Mahajan, and R. N. Sahoo, “Spectroscopy based novel spectral indices, pca-and plsr-coupled machine learning models for salinity stress phenotyping of rice,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 229, p. 117983, 2020.
G. Impollonia, M. Croci, E. Martani, A. Ferrarini, J. Kam, L. M. Trindade, J. Clifton-Brown, and S. Amaducci, “Moisture content estimation and senescence phenotyping of novel miscanthus hybrids combining UAV-based remote sensing and machine learning,” GCB Bioenergy, vol. 14, no. 6, pp. 639–656, 2022.
G. Impollonia, M. Croci, A. Ferrarini, J. Brook, E. Martani, H. Blan- dinières, A. Marcone, D. Awty-Carroll, C. Ashman, J. Kam, et al., “Uav remote sensing for high-throughput phenotyping and for yield prediction of miscanthus by machine learning techniques,” 2022.
P. D’Odorico, A. Besik, C. Y. Wong, N. Isabel, and I. Ensminger, “High-throughput drone-based remote sensing reliably tracks phenology in thousands of conifer seedlings,” New Phytologist, vol. 226, no. 6, pp. 1667–1681, 2020.
H. Garbouge, P. Rasti, and D. Rousseau, “Enhancing the tracking of seedling growth using RGB-depth fusion and deep learning,” Sensors, vol. 21, no. 24, p. 8425, 2021.
R. K. Bannihatti, P. Sinha, D. Raju, S. Das, S. Mandal, R. Raje, C. Viswanathan, S. Kumar, K. Gaikwad, and R. Aggarwal, “Image-based high throughput phenotyping for fusarium wilt resistance in pigeon pea (cajanus cajan),” Phytoparasitica, pp. 1–16, 2022.
W. Yang, C. Yang, Z. Hao, C. Xie, and M. Li, “Diagnosis of plant cold damage based on hyperspectral imaging and convolutional neural network,” Ieee Access, vol. 7, pp. 118239–118248, 2019.
M. Burns, J. Renk, D. Eickholt, A. Gilbert, T. Hattery, M. Holmes, N. Anderson, A. Waters, S. Kalambur, S. Flint-Garcia, et al., “Pre- dictating moisture content during maize nixtamalization using machine learning with nir spectroscopy,” bioRxiv, 2021.
P. Castro-Valdecantos, O. Apolo-Apolo, M. Pérez-Ruiz, and G. Egea, “Leaf area index estimations by deep learning models using rgb images and data fusion in maize,” Precision Agriculture, pp. 1–18, 2022.
K. Dilmurat, V. Sagan, and S. Moose, “Ai-driven maize yield forecasting using unmanned aerial vehicle-based hyperspectral and lidar data fusion,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 3, pp. 193–199, 2022.
X. Feng, Y. Zhan, Q. Wang, X. Yang, C. Yu, H. Wang, Z. Tang, D. Jiang, C. Peng, and Y. He, “Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping,” The Plant Journal, vol. 101, no. 6, pp. 1448–1461, 2020.
M. Maimaitijiang, A. Ghulam, P. Sidike, S. Hartling, M. Maimaitiy- iming, K. Peterson, E. Shavers, J. Fishman, J. Peterson, S. Kadam, et al., “Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 134, pp. 43–58, 2017.
M. Yoosefzadeh-Najafabadi, D. Tulpan, and M. Eskandari, “Using hybrid artificial intelligence and evolutionary optimization algorithms for estimating soybean yield and fresh biomass using hyperspectral vegetation indices,” Remote Sensing, vol. 13, no. 13, p. 2555, 2021.
A. Masjedi and M. M. Crawford, “Prediction of sorghum biomass using time series UAV-based hyperspectral and lidar data,” in IGARSS 2020- 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 3912–3915, IEEE, 2020.
F. E. Gomez, G. Carvalho, F. Shi, A. H. Muliana, and W. L. Rooney, “High throughput phenotyping of morpho-anatomical stem properties using x-ray computed tomography in sorghum,” Plant methods, vol. 14, no. 1, pp. 1–13, 2018.
Y. Zhao, B. Zheng, S. C. Chapman, K. Laws, B. George-Jaeggli, G. L. Hammer, D. R. Jordan, and A. B. Potgieter, “Detecting sorghum plant and head features from multispectral UAV imagery,” Plant Phenomics, vol. 2021, 2021.
A. Masjedi, N. R. Carpenter, M. M. Crawford, and M. R. Tuinstra, “Prediction of sorghum biomass using UAV time series data and recurrent neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0, 2019.
Q. Chen, B. Zheng, K. Chenu, P. Hu, and S. C. Chapman, “Unsupervised plot-scale lai phenotyping via UAV-based imaging, modeling, and machine learning,” Plant Phenomics, vol. 2022, 2022.
G. Théroux-Rancourt, M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles, “Digitally deconstructing leaves in 3d using x-ray microcomputed tomography and machine learning,” Applications in plant sciences, vol. 8, no. 7, p. e11380, 2020.
J. L. Araus, S. C. Kefauver, M. Zaman-Allah, M. S. Olsen, and J. E. Cairns, “Translating high-throughput phenotyping into genetic gain,” Trends in plant science, vol. 23, no. 5, pp. 451–466, 2018.
D. Chen, K. Neumann, S. Friedel, B. Kilian, M. Chen, T. Altmann, and C. Klukas, “Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis,” The plant cell, vol. 26, no. 12, pp. 4636–4655, 2014.
T. Walter, D. W. Shattuck, R. Baldock, M. E. Bastin, A. E. Carpenter, S. Duce, J. Ellenberg, A. Fraser, N. Hamilton, S. Pieper, et al., “Visualization of image data from cells to organisms,” Nature methods, vol. 7, no. 3, pp. S26–S41, 2010.
J. Zhou, H. Mou, J. Zhou, M. Ali, H. Ye, P. Chen, and H. Nguyen, “Qualification of soybean responses to flooding stress using UAV-based imagery and deep learning,” Plant Phenomics, vol. 2021, 2021.
J. Ebersbach, N. Khan, I. McQuillan, E. Higgins, K. Horner, V. Bandi, C. Gutwin, S. Vail, S. Robinson, and I. Parkin, “Exploiting high-throughput indoor phenotyping to characterize the founders of a structured b. napus breeding population,” Frontiers in Plant Science, vol. 12, 2022. cited By 0.
R. Quiñones, F. Munoz-Arriola, S. Choudhury, and A. Samal, “Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping,” PLoS ONE, vol. 16, no. 9 September 2021. cited By 0.
N. Wilke, B. Siegmann, J. Postma, O. Muller, V. Krieger, R. Pude, and U. Rascher, “Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping,” Computers and Electronics in Agriculture, vol. 189, 2021.
S. Zhou, X. Chai, Z. Yang, H. Wang, C. Yang, and T. Sun, “Maize-ias: a maize image analysis software using deep learning for high-throughput plant phenotyping,” Plant Methods, vol. 17, no. 1, 2021.
J. Cuevas, O. Montesinos-López, P. Juliana, C. Guzmán, P. Pérez- Rodríguez, J. González-Bucio, J. Burgueño, A. Montesinos-López, and J. Crossa, “Deep kernel for genomic and near-infrared predictions in multi-environment breeding trials,” G3: Genes, Genomes, Genetics, vol. 9, no. 9, pp. 2913–2924, 2019.
N. Gaggion, F. Ariel, V. Daric, É. Lambert, S. Legendre, T. Roulé, A. Camoirano, D. H. Milone, M. Crespi, T. Blein, et al., “Chronoroot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture,” Giga- Science, vol. 10, no. 7, p. giab052, 2021.
A. Barradas, P. Correia, S. Silva, P. Mariano, M. Pires, A. Matos, A. da Silva, and J. Marques da Silva, “Comparing machine learning methods for classifying plant drought stress from leaf reflectance spectra in Arabidopsis thaliana,” Applied Sciences (Switzerland), vol. 11, no. 14, 2021.
J. A. Gibbs, M. P. Pound, A. P. French, D. M. Wells, E. H. Murchie, and T. P. Pridmore, “Active vision and surface reconstruction for 3d plant shoot modeling,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17, no. 6, pp. 1907–1917, 2019.
M. Yang, J. Cui, E. S. Jeong, and S. I. Cho, “Development of high-resolution 3d phenotyping system using depth camera and RGB camera,” in 2017 ASABE Annual International Meeting, p. 1, American Society of Agricultural and Biological Engineers, 2017.
L. A. Gatys, A. S. Ecker, and M. Bethge, “A neural algorithm of artistic style (2015),” vol. 2, 2015.
M. Alencastre-Miranda, R. M. Johnson, and H. I. Krebs, “Convolutional neural networks and transfer learning for quality inspection of different sugarcane varieties,” IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 787–794, 2021.
F. H. dos Santos Silva, J. P. dos Santos Verçosa, and A. C. F. Tavares, “Evaluating methods to classify sugarcane planting using convolutional neural network and random forest algorithms,” International Journal of Development Research, vol. 10, no. 12, pp. 42807–42811, 2020.
T. Van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Computers and Electronics in Agriculture, vol. 177, p. 105709, 2020.
S. Nabwire, H.-K. Suh, M. Kim, I. Baek, and B.-K. Cho, “Review: Application of artificial intelligence in phenomics,” Sensors, vol. 21, no. 13, 2021.
C. Zheng, A. Abd-Elrahman, and V. Whitaker, “Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming,” Remote Sensing, vol. 13, no. 3, 2021.
A. F. A. Fernandes, J. R. R. Dórea, and G. J. d. M. Rosa, “Image analysis and computer vision applications in animal sciences: An overview,” Frontiers in Veterinary Science, vol. 7, p. 800, 2020.
T. Bresolin and J. R. R. Dórea, “Infrared spectrometry as a high-throughput phenotyping technology to predict complex traits in livestock systems,” Frontiers in Genetics, vol. 11, p. 923, 2020.
R. Furbank, J. Jimenez-Berni, B. George-Jaeggli, A. Potgieter, and D. Deery, “Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops,” New Phytologist, vol. 223, no. 4, pp. 1714–1727, 2019.
A. M. Taberkit, A. Kechida, and A. Bouguettaya, “Algerian perspectives for UAV-based remote sensing technologies and artificial intelligence in precision agriculture,” in Proceedings of the 4th International Conference on Networking, Information Systems & Security, pp. 1–9, 2021.
B. Kitchenham, “Procedures for performing systematic reviews,” Keele, UK, Keele University, vol. 33, no. 2004, pp. 1–26, 2004.
M. Maimaitijiang, V. Sagan, S. Bhadra, C. Nguyen, T. Mockler, and N. Shakoor, “A fully automated and fast approach for canopy cover estimation using super high-resolution remote sensing imagery,” vol. 5, pp. 219–226, 2021.
J. Tello, M. Montemayor, A. Forneck, and J. Ibáñez, “A new image-based tool for the high throughput phenotyping of pollen viability: Evaluation of inter- and intra-cultivar diversity in grapevine,” Plant Methods, vol. 14, no. 1, 2018.
J. Colorado, F. Calderon, D. Mendez, E. Petro, J. Rojas, E. Correa, I. Mondragon, M. Rebolledo, and A. Jaramillo-Botero, “A novel nir- image segmentation method for the precise estimation of above-ground biomass in rice crops,” PLoS ONE, vol. 15, no. 10 October 2020.
M. Henke, A. Junker, K. Neumann, T. Altmann, and E. Gladilin, “A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping,” Plant Methods, vol. 16, no. 1, 2020.
C. Bateman, J. Fourie, J. Hsiao, K. Irie, A. Heslop, A. Hilditch, M. Hagedorn, B. Jessep, S. Gebbie, and K. Ghamkhar, “Assessment of mixed sward using context-sensitive convolutional neural networks,” Frontiers in Plant Science, vol. 11, 2020.
Z. Jiang, H. Tu, B. Bai, C. Yang, B. Zhao, Z. Guo, Q. Liu, H. Zhao, W. Yang, L. Xiong, and J. Zhang, “Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal the genetic variation of rice germplasms in dynamic response to drought stress,” New Phytologist, 2021.
K. Falk, T. Jubery, S. Mirnezami, K. Parmley, S. Sarkar, A. Singh, B. Ganapathysubramanian, and A. Singh, “Computer vision and machine learning enabled soybean root phenotyping pipeline,” Plant Methods, vol. 16, no. 1, 2020.
G. de Oliveira, J. Junior, C. Polidoro, L. Osco, H. Siqueira, L. Rodrigues, L. Jank, S. Barrios, C. Valle, R. Simeão, C. Carromeu, E. Silveira, L. Jorge, W. Gonçalves, M. Santos, and E. Matsubara, “Convolutional neural networks to estimate dry matter yield in a guinea grass breeding program using UAV remote sensing,” Sensors, vol. 21, no. 12, 2021.
S. Madec, X. Jin, H. Lu, B. De Solan, S. Liu, F. Duyme, E. Heritier, and F. Baret, “Ear density estimation from high-resolution RGB imagery using deep learning technique,” Agricultural and Forest Meteorology, vol. 264, pp. 225–234, 2019.
X. Jin, S. Liu, F. Baret, M. Hemerlé, and A. Comar, “Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery,” Remote Sensing of Environment, vol. 198, pp. 105–114, 2017.
S. Wang, A. Feng, T. Lou, P. Li, and J. Zhou, “Lstm-based cotton yield prediction system using UAV imagery,” 2020.
C. Ren, J. Dulay, G. Rolwes, D. Pauli, N. Shakoor, and A. Stylianou, “Multi-resolution outlier pooling for sorghum classification,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2925–2933, June 2021.
Z. Lin and W. Guo, “Sorghum panicle detection and counting using unmanned aerial system images and deep learning,” Frontiers in Plant Science, vol. 11, 2020.
T. Misra, A. Arora, S. Marwaha, V. Chinnusamy, A. Rao, R. Jain, R. Sahoo, M. Ray, S. Kumar, D. Raju, R. Jha, A. Nigam, and S. Goel, “Spikesegnet-a deep learning approach utilizing an encoder-decoder network with an hourglass for spike segmentation and counting in a wheat plant from visual imaging,” Plant Methods, vol. 16, no. 1, 2020.
Y. Ampatzidis and V. Partel, “Uav-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence,” Remote Sensing, vol. 11, no. 4, 2019.
Y. Ampatzidis and V. Partel, “Uav-based high throughput phenotyping in specialty crops utilizing artificial intelligence,” 2019.
S. Varela, T. Pederson, C. Bernacchi, and A. Leakey, “Understanding growth dynamics and yield prediction of sorghum using high temporal resolution UAV imagery time series and machine learning,” Remote Sensing, vol. 13, no. 9, 2021.
M. Herrero-Huerta, P. Rodriguez-Gonzalvez, and K. Rainey, “Yield prediction by machine learning from UAV-based multi-sensor data fusion in soybean,” Plant Methods, vol. 16, no. 1, 2020.
X. Cao, Y. Liu, R. Yu, D. Han, and B. Su, “A comparison of UAV RGB and multispectral imaging in phenotyping for stay green of wheat population,” Remote Sensing, vol. 13, no. 24, 2021. cited By 0.
P. Sharma, L. Leigh, J. Chang, M. Maimaitijiang, and M. Caffé, “Above-ground biomass estimation in oats using UAV remote sensing and machine learning,” Sensors, vol. 22, no. 2, 2022. cited By 0.
J. Wang, B. Wu, M. Kohnen, D. Lin, C. Yang, X. Wang, A. Qiang, W. Liu, J. Kang, H. Li, J. Shen, T. Yao, J. Su, B. Li, and L. Gu, “Classification of rice yield using UAV-based hyperspectral imagery and lodging feature,” Plant Phenomics, vol. 2021, 2021. cited By 1.
S. Liu, X. Jin, C. Nie, S. Wang, X. Yu, M. Cheng, M. Shao, Z. Wang, N. Tuohuti, Y. Bai, and Y. Liu, “Estimating leaf area index using unmanned aerial vehicle data: Shallow vs. deep machine learning algorithms,” Plant Physiology, vol. 187, no. 3, pp. 1551–1576, 2021. cited By 1.
J. Fan, J. Zhou, B. Wang, N. de Leon, S. M. Kaeppler, D. C. Lima, and Z. Zhang, “Estimation of maize yield and flowering time using multi-temporal UAV-based hyperspectral data,” Remote Sensing, vol. 14, no. 13, p. 3052, 2022.
F. Freitas Moreira, H. Rojas de Oliveira, M. Lopez, B. Abughali, G. Gomes, K. Cherkauer, L. Brito, and K. Rainey, “High-throughput phenotyping and random regression models reveal temporal genetic control of soybean biomass production,” Frontiers in Plant Science, vol. 12, 2021. cited By 0.
P. Fu, K. Meacham-Hensold, K. Guan, and C. Bernacchi, “Hyperspectral leaf reflectance as a proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms,” Frontiers in Plant Science, vol. 10, 2019. cited By 35.
M. Danilevicz, P. Bayer, F. Boussaid, M. Bennamoun, and D. Edwards, “Maize yield prediction at an early developmental stage using multi-spectral images and genotype data for preliminary hybrid selection,” Remote Sensing, vol. 13, no. 19, 2021. cited By 0.
A. Masjedi, M. Crawford, N. Carpenter, and M. Tuinstra, “Multi-temporal predictive modeling of sorghum biomass using UAV-based hyperspectral and lidar data,” Remote Sensing, vol. 12, no. 21, pp. 1– 35, 2020. cited By 4.
T. R. Alabi, A. T. Abebe, G. Chigeza, and K. R. Fowobaje, “Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in west Africa,” Remote Sensing Applications: Society and Environment, p. 100782, 2022.
S. Yu, J. Fan, X. Lu, W. Wen, S. Shao, X. Guo, and C. Zhao, “Hyperspectral technique combined with deep learning algorithm for prediction of phenotyping traits in lettuce.,” Frontiers in plant science, vol. 13, pp. 927832–927832, 2022.
F. Xia, L. Quan, Z. Lou, D. Sun, H. Li, and X. Lv, “Identification and comprehensive evaluation of resistant weeds using unmanned aerial vehicle-based multispectral imagery,” Frontiers in Plant Science, vol. 13, 2022.
S. Fei, M. A. Hassan, Y. Xiao, X. Su, Z. Chen, Q. Cheng, F. Duan, R. Chen, and Y. Ma, “Uav-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat,” Precision agriculture, pp. 1–26, 2022.
C. Poudyal, L. F. Costa, H. Sandhu, Y. Ampatzidis, D. C. Odero, O. C. Arbelo, and R. H. Cherry, “Sugarcane yield prediction and genotype selection using unmanned aerial vehicle-based hyperspectral imaging and machine learning,” 2022.
J. A. Cruz, X. Yin, X. Liu, S. M. Imran, D. D. Morris, D. M. Kramer, and J. Chen, “Multi-modality imagery database for plant phenotyping,” Machine Vision and Applications, vol. 27, no. 5, pp. 735–749, 2016.
S. Das Choudhury, S. Bashyam, Y. Qiu, A. Samal, and T. Awada, “Holistic and component plant phenotyping using temporal image sequence,” Plant methods, vol. 14, no. 1, pp. 1–21, 2018.
S. T. Namin, M. Esmaeilzadeh, M. Najafi, T. B. Brown, and J. O. Borevitz, “Deep phenotyping: deep learning for temporal pheno- type/genotype classification,” Plant methods, vol. 14, no. 1, pp. 1–14, 2018.
Y.-t. Zhuang, F. Wu, C. Chen, and Y.-h. Pan, “Challenges and opportunities: from big data to knowledge in ai 2.0,” Frontiers of Information Technology & Electronic Engineering, vol. 18, no. 1, pp. 3–14, 2017.
K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update,” Information and Software Technology, vol. 64, pp. 1–18, 2015.
