Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis
Open accessAbstract
High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.
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The study investigates several other commonly known data analysis tools to validate the use of phenomics under the TinyML paradigm.
“It further investigates several other commonly known data analysis tools to validate the use of phenomics under the aegis of TinyML paradigm.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Nakhle and Harfouche, 2021
The Abstract says the tutorial reviews tools, technologies, and services for phenomics image analysis. The “Moving to the cloud” section explains TinyML and its potential for resource-constrained devices, but does not say the tools were investigated to validate phenomics under a TinyML paradigm. Our copy provides no page numbers for these passages. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe phenomics image analysis uses the PlantVillage tomato dataset along with YOLO3 (based on DarkNet-53 architecture) algorithm for automatic detection of tomato leaves.
“It uses the PlantVillage tomato dataset along with YOLO3 (based on DarkNet-53 architecture) algorithm for automatic detection of tomato leaves.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Nakhle and Harfouche, 2021
The study uses the SegNet algorithm for pixel-wise image segmentation.
“The study also uses SegNet algorithm for pixel-wise image segmentation.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Nakhle and Harfouche, 2021
In Nakhle and Harfouche (2021), a phenomics image analysis is performed based on the classification of tomato leaf disease and spider mites.
“In (Nakhle and Harfouche, 2021), a phenomics image analysis is performed based on the classification of tomato leaf disease and spider mites.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Nakhle and Harfouche, 2021
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A survey on generative adversarial networks for imbalance problems in computer vision tasks
Sampath, V., Maurtua, I., Martín, J. J. A., & Gutierrez, A. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00414-0 · Cited by 275 · cited 1× · see citing works
“95 DCGAN instead can generate synthetic images with high visual fidelity.”
BackgroundGenotype–phenotype mapping and the end of the ‘genes as blueprint’ metaphor
Pigliucci, M. (2010) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2009.0241 · Cited by 263 · see citing works
Biomedical image augmentation using Augmentor
Bloice, M. D., Roth, P. M., & Holzinger, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz259 · Cited by 248 · see citing works
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Integrating Machine Learning with Human Knowledge
Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020) · iScience · doi:10.1016/j.isci.2020.101656 · Cited by 243 · cited 1× · see citing works
“49 Consequently, methods such as logic-based ML make use of inductive logic programming 50 to integrate information and patterns extracted from data with human knowledge in order to make predictions.”
MethodGoos, G., Leeuwen, J. V., & Hartmanis, J. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45014-9 · Cited by 237 · cited 1× · see citing works
“25,60 Tracing the origin of EL proved difficult as the idea of training multiple algorithms and combining their predictions has been used for a long time.”
An online database for plant image analysis software tools
Lobet, G., Draye, X., & Périlleux, C. (2013) · Plant Methods · doi:10.1186/1746-4811-9-38 · Cited by 232 · cited 2× · see citing works
“In this regard, Lobet et al.(84) developed an online database (http://plant-image-analysis.”
BackgroundChallenges and Opportunities in Machine-Augmented Plant Stress Phenotyping
Singh, A., Jones, S. E., Ganapathysubramanian, B., Sarkar, S., Mueller, D. S., Sandhu, K. S., & Nagasubramanian, K. (2020) · Trends in Plant Science · doi:10.1016/j.tplants.2020.07.010 · Cited by 223 · cited 2× · see citing works
“…be considered the best choice as different algorithms are to be assessed for different tasks and objectives, our approach is in agreement with common image-based analytical methods, along the lines of those described elsewhere, 36,37,75 as essential key steps in the workflow of image analysis.”
MethodSelvaraj, M. G., Vergara, A., Montenegro, F., Ruiz, H., Safari, N. K., Raymaekers, D., Ocimati, W., Ntamwira, J., Tits, L., Omondi, A. B., et al. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.08.025 · Cited by 222 · see citing works
Programming Language Pragmatics
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“71 In this sense, any Turing complete programming language can be used to implement any AI algorithm.”
MethodElectricity Intensity of Internet Data Transmission: Untangling the Estimates
Aslan, J., Mayers, K., Koomey, J., & France, C. (2017) · Journal of Industrial Ecology · doi:10.1111/jiec.12630 · Cited by 217 · cited 1× · see citing works
“…25,123 further concerns arise from the compute-centric approach, including (1) energy efficiency where data transmission is energy intensive, 124 (2) potential privacy violations caused by insecure data transmission and storage on cloud computers, 125 and (3) inefficient use of storage due…”
BackgroundRise of the Machines: Advances in Deep Learning for Cancer Diagnosis
Levine, A., Schlosser, C. L., Grewal, J., Coope, R., Jones, S. J. M., & Yip, S. S. F. (2019) · Trends in cancer · doi:10.1016/j.trecan.2019.02.002 · Cited by 202 · cited 1× · see citing works
“Since then, DCNNs became the default approach for most AI-based image analysis tasks.(59) Various DL algorithms have been developed and used in plant phenotyping.”
MethodAbdelhalim, I., Mohamed, M. F., & Mahdy, Y. B. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113922 · Cited by 180 · see citing works
Review: New sensors and data-driven approaches—A path to next generation phenomics
Roitsch, T. G., Cabrera‐Bosquet, L., Fournier, A., Ghamkhar, K., Jiménez-Berni, J. A., Pinto, F., & Ober, E. S. (2019) · Plant Science · doi:10.1016/j.plantsci.2019.01.011 · Cited by 177 · cited 1× · see citing works
“Large-scale high-throughput plant phenotyping experiments can produce images in the order of gigabytes, 119 making it crucial to have access to greater computing power.”
BackgroundAn evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat
Grinberg, N. F., Orhobor, O. I., & King, R. D. (2019) · Machine Learning · doi:10.1007/s10994-019-05848-5 · Cited by 163 · see citing works
Liu, Y., Wang, D., He, F., Wang, J., Joshi, T., & Xu, D. H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01091 · Cited by 160 · see citing works
Model Agnostic Supervised Local Explanations
Plumb, G., Molitor, D., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1807.02910 · Cited by 149 · cited 1× · see citing works
“115 Its performance turned out to be the best one when compared with Shapley additive explanations (SHAP) 116 and model agnostic supervised local explanations (MAPLE) 117 to explain image-based classifications.”
MethodHughes, D. P., & Salathé, M. (2015) · arXiv (Cornell University) · Cited by 143 · see citing works
A Study on Data Storage Security Issues in Cloud Computing
Vurukonda, N., & Rao, B. T. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.335 · Cited by 138 · cited 1× · see citing works
“…arise from the compute-centric approach, including (1) energy efficiency where data transmission is energy intensive, 124 (2) potential privacy violations caused by insecure data transmission and storage on cloud computers, 125 and (3) inefficient use of storage due to unnecessary data transmission.”
BackgroundProgramming the logic theory machine
Newell, A., & Shaw, J. C. (1957) · Western Joint IRE-AIEE-ACM Computer Conference (WJCC) · doi:10.1145/1455567.1455606 · Cited by 135 · cited 1× · see citing works
“This is an open access article under the CC BY-N While this example is used as an illustration for almost every programming language introduced after C, it is also being incorporated in tutorials for older programming languages, such as list processor (LISP), the first high-level functional programming language, desig…”
MethodAre random forests truly the best classifiers
Wainberg, M., Alipanahi, B., & Frey, B. J. (2016) · Journal of Machine Learning Research · doi:10.5555/2946645.3007063 · Cited by 133 · see citing works
Transcriptome-Based Prediction of Complex Traits in Maize
Azodi, C. B. D., Pardo, J., VanBuren, R., Campos, G. D. L., & Shiu, S. (2019) · The Plant Cell · doi:10.1105/tpc.19.00332 · Cited by 133 · see citing works
A KNN Undersampling Approach for Data Balancing
Beckmann, M., Ebecken, N. F. F., & de Lima, B. S. L. P. (2015) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2015.74010 · Cited by 132 · cited 1× · see citing works
“When used for undersampling on 33 different datasets, KNN outperformed the results of the edited NN (ENN), neighbor cleaning rule (NCL), and random undersampling algorithms.(101) Figure 2 shows an example of KNN circling three detected neighbors with similar features belonging to the same class.”
MethodAppyters: Turning Jupyter Notebooks into data-driven web apps
Clarke, D. J. B., Jeon, M., Stein, D. J., Moiseyev, N., Kropiwnicki, E., Dai, C. S., Xie, Z., Wojciechowicz, M. L., Litz, S., Hom, J., et al. (2021) · Patterns · doi:10.1016/j.patter.2021.100213 · Cited by 120 · cited 1× · see citing works
“These notebooks are ideal to share well-documented, reproducible data analysis pipelines, promoting transparency and reusability.(121) The increasing availability of cloud computing has led to an overuse of large amounts of data in a compute-centric paradigm, where data are analyzed by clusters in data centers.”
BackgroundArtificial Intelligence: Chess match of the century
Hassabis, D. (2017) · Nature · doi:10.1038/544413a · Cited by 105 · cited 1× · see citing works
“20 In 2012, the AI-based image analysis research marked a significant milestone when Alex Krizhev-sky and colleagues published a highly influential paper introducing AlexNet, an AI algorithm that dramatically reduced image analysis error rates and won the ImageNet Large Scale Visual Recognition…”
MethodStructural properties of genotype–phenotype maps
Ahnert, S. E. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0275 · Cited by 104 · cited 1× · see citing works
“34 These aspects of digital phenomics are crucial to accelerate breeding.”
BackgroundA Study of Color Histogram Based Image Retrieval
Chakravarti, R., & Meng, X. (2009) · International Conference on Information Technology: New Generations · doi:10.1109/itng.2009.126 · Cited by 100 · see citing works
Artificial intelligence: a paper symposium
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Julia: come for the syntax, stay for the speed
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“For example, Julia combines the interactivity of scripting languages, such as Python and R, with the speed of compiled languages such as C. 72 It was originally designed for high-performance scientific computing and data analysis.”
BackgroundEvaluating local explanation methods on ground truth
Guidotti, R. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103428 · Cited by 99 · cited 1× · see citing works
“118 We used LIME to explain our three trained models, RF, the standard DCNN, and the pre-trained DCNN.”
MethodSpringer Handbook of Bio-/Neuroinformatics
Liang, L., Krishnamurthi, R., Kasabov, N. K., & Feigin, V. (2013) · Springer handbooks · doi:10.1007/978-3-642-30574-0 · Cited by 98 · see citing works
Image Harvest: an open-source platform for high-throughput plant image processing and analysis
Knecht, A. C., Campbell, M. T., Caprez, A., Swanson, D. R., & Walia, H. (2016) · Journal of Experimental Botany · doi:10.1093/jxb/erw176 · Cited by 92 · cited 1× · see citing works
“Many types of plant image processing software, such as plant computer vision (PlantCV), 80,81 Image Harvest, and HTPheno, 82 can help with these tasks and extract features from images by automating complex techniques such as histogram thresholding and color analysis.”
MethodLearning programs by learning from failures
Cropper, A., & Morel, R. (2021) · Machine Learning · doi:10.1007/s10994-020-05934-z · Cited by 86 · cited 1× · see citing works
“51,52 Various ML algorithms have been developed and used in plant phenotyping, as reviewed by Singh et al. 47 and van Dijk et al. 53 While unsupervised, supervised, and semi-supervised learning can be useful depending on the goal to be achieved, in this tutorial, we focus on supervised learning,…”
MethodChao, X., Kou, G., Li, T., & Peng, Y. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.07.030 · Cited by 85 · cited 1× · see citing works
“22 The program simulated millions of Go games by playing against itself and progressively learned the game, accumulating thousands of years of human knowledge during a period of just a few days.”
BackgroundJulia language in machine learning: Algorithms, applications, and open issues
Gao, K., Mei, G., Piccialli, F., Cuomo, S., Tu, J., & Huo, Z. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100254 · Cited by 85 · see citing works
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Programming Language Pragmatics
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Environment-to-phenotype mapping and adaptation strategies in varying environments
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Francesconi, S., Harfouche, A. L., Maesano, M., & Balestra, G. M. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.628575 · Cited by 66 · see citing works
Self-citationPromoting Ethically Responsible Use of Agricultural Biotechnology
Harfouche, A. L., Petousi, V. G., Richard Meilan, C., Sweet, J., Twardowski, T., & Altman, A. (2021) · Trends in Plant Science · doi:10.1016/j.tplants.2020.12.015 · Cited by 65 · see citing works
Self-citationDual midbrain and forebrain origins of thalamic inhibitory interneurons
Jager, P., Moore, G., Calpin, P., Durmishi, X., Salgarella, I., Menage, L., Kita, Y., Wang, Y., Kim, D. W., Blackshaw, S., et al. (2021) · eLife · doi:10.7554/elife.59272 · Cited by 61 · see citing works
Streich, J., Romero, J., Gazolla, J. G. F. M., Kainer, D., Cliff, A. M., Prates, E. T., Brown, J. B., Khoury, S., Tuskan, G. A., Garvin, M. R., et al. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.01.010 · Cited by 58 · cited 2× · see citing works
“With supercomputers breaking the exascale barrier (performing a quintillion floating-point operations per second) 26,27 and the ability of AI to analyze massive amounts of data, researchers can take advantage of big data, rather than relying on smaller representative samples, to extract knowledge…”
BackgroundSelf-citationWhat is science for? The Lighthill report on artificial intelligence reinterpreted
AGAR, J. (2020) · The British Journal for the History of Science · doi:10.1017/s0007087420000230 · Cited by 54 · cited 1× · see citing works
“12 This caused a loss of confidence and henceforth a decline in resources for AI research, 13,14 starting a period known as the AI winter.”
BackgroundMaesano, M., Khoury, S., Nakhle, F., Firrincieli, A., Gay, A. P., Tauro, F., & Harfouche, A. L. (2020) · Remote Sensing · doi:10.3390/rs12203464 · Cited by 54 · cited 1× · see citing works
“For example, red-green-blue (RGB) imaging data can be used to monitor plant stress,(76) while light detection and ranging (LiDAR) data allow measurement of plant architecture, canopy height, and growth rates.(25,77) Publicly available plant phenotyping datasets have a 2-fold advantage: on the one hand, the phenomics d…”
BackgroundSelf-citationMaize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping
Zhou, S., Chai, X., Yang, Z., Wang, H., Yang, C., & Sun, T. (2021) · Plant Methods · doi:10.1186/s13007-021-00747-0 · Cited by 51 · cited 1× · see citing works
“83 In this regard, Lobet et al. 84 developed an online data-base (http://plant-image-analysis.org) indexing both commercial and open-source plant image processing software.”
BackgroundPrinciples of programming languages: Design, evaluation, and implementation
Yeung, G. C. N., & Yeung, H. C. (1985) · Proceedings of the IEEE · doi:10.1109/proc.1985.13274 · Cited by 45 · cited 1× · see citing works
“This is an open access article under the CC BY-N While this example is used as an illustration for almost every programming language introduced after C, it is also being incorporated in tutorials for older programming languages, such as list processor (LISP), the first high-level functional programming language, desig…”
MethodRandom sketch learning for deep neural networks in edge computing
Li, B., Chen, P., Liu, H., Guo, W., Cao, X., Du, J., Zhao, C., & Zhang, J. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00039-6 · Cited by 40 · cited 1× · see citing works
“…generated models are compressed using compression techniques such as network pruning, low-rank approximation, weight quantization, and network architecture transform 127 to allow for their deployment in devices with low computational resources, without signifi-cant decrease in their performance.”
MethodNascent exascale supercomputers offer promise, present challenges
Mann, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2015968117 · Cited by 32 · cited 1× · see citing works
“With supercomputers breaking the exascale barrier (performing a quintillion floating-point operations per second) 26,27 and the ability of AI to analyze massive amounts of data, researchers can take advantage of big data, rather than relying on smaller representative samples, to extract knowledge…”
BackgroundEffect of Superpixel Aggregation on Explanations in LIME – A Case Study with Biological Data
Schallner, L., Rabold, J., Scholz, O., & Schmid, U. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-43823-4_13 · Cited by 31 · see citing works
A Primer on Data Analytics in Functional Genomics: How to Move from Data to Insight?
Grabowski, P., & Rappsilber, J. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.10.010 · Cited by 26 · cited 1× · see citing works
“Its influence is far less than that of other popular programming languages such as Python and R, which are considered the standard tools of the trade in research and industry, with Python being amore popular choice among biologists(74); they both have an easy-to-learn syntax and have no major speed differences when it…”
BackgroundRajareddy, G. N. V., Devi, K. S., & Vadaparthi, N. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09849-y · Cited by 26 · see citing works
Performance prediction of crosses in plant breeding through genotype by environment interactions
Ansarifar, J., Akhavizadegan, F., & Wang, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-68343-1 · Cited by 25 · cited 1× · see citing works
“The remarkable flexibility and adaptability of EL helped the proliferation of its application in plant phenotyping where it has been used for various tasks, including biotic stress identification and classification,(65,66) aboveground biomass estimation,(67,68) and performance prediction of crosses in breeding.(69,70)”
MethodBhattacharya, A., & Cui, Y. (2017) · Scientific Reports · doi:10.1038/s41598-017-04070-4 · Cited by 24 · cited 1× · see citing works
“GPUs have thousands of these cores that process data in parallel.(120) Some academic supercomputers offer access to GPUs, but, because of the rapid progress in their technology, they are at risk of becoming outdated.”
BackgroundProceedings. Visualization '97 (Cat. No. 97CB36155)
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Copeland, J., & Proudfoot, D. (2007) · Springer eBooks · doi:10.1007/978-1-4020-6710-5_9 · Cited by 20 · see citing works
A Sociological History of the Neural Network Controversy
Olazaran, M. (1993) · Advances in computers · doi:10.1016/s0065-2458(08)60408-8 · Cited by 13 · cited 1× · see citing works
“12 This caused a loss of confidence and henceforth a decline in resources for AI research, 13,14 starting a period known as the AI winter.”
BackgroundExpert Systems: Looking Back and Looking Ahead
Feigenbaum, E. A. (1980) · Informatik-Fachberichte · doi:10.1007/978-3-642-67838-7_1 · Cited by 12 · cited 1× · see citing works
“15,16 In the 1980s, with Edward Feigenbaum introducing expert systems, 17,18 and the Japanese government funding the fifth-generation computer project, 19 the AI winter ended.”
The Japanese national Fifth Generation project: Introduction, survey, and evaluation
Feigenbaum, E. A., & Shrobe, H. E. (1993) · Future Generation Computer Systems · doi:10.1016/0167-739x(93)90003-8 · Cited by 10 · see citing works
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“51,52 Various ML algorithms have been developed and used in plant phenotyping, as reviewed by Singh et al. 47 and van Dijk et al. 53 While unsupervised, supervised, and semi-supervised learning can be useful depending on the goal to be achieved, in this tutorial, we focus on supervised learning,…”
MethodAnecdotes-a very early expert system
Simon, H. A. (1993) · IEEE Annals of the History of Computing · doi:10.1109/85.222851 · Cited by 3 · cited 1× · see citing works
“9 In 1956, their program, often cited as the first AI program, 10 was presented at the Dartmouth summer research project on AI conference hosted by John McCarthy and Marvin Minsky 11 during which McCarthy coined the term AI.”
BackgroundPredicting rice phenotypes with meta and multi-target learning
Orhobor, O. I., Alexandrov, N. N., & King, R. D. (2020) · Machine Learning · doi:10.1007/s10994-020-05881-9 · Cited by 3 · cited 1× · see citing works
“40–42 However, complex AI models typically operate as black boxes and require a leap of faith to believe their predictions.”
BackgroundModeling Vision with the Neocognitron
Fukushima, K. (2013) · Springer eBooks · doi:10.1007/978-3-642-30574-0_44 · Cited by 1 · cited 1× · see citing works
“It was capable of analyzing images 57,58 and led to the birth of deep convolutional neural networks (DCNNs).”
BackgroundFifty Years Is Not a Lot of Time!
ZADPOOR, A. A. (2019) · Matter · doi:10.1016/j.matt.2019.10.011 · Cited by 1 · cited 1× · see citing works
“15,16 In the 1980s, with Edward Feigenbaum introducing expert systems, 17,18 and the Japanese government funding the fifth-generation computer project, 19 the AI winter ended.”
Augmentor: An Image Augmentation Library for Machine Learning
Bloice, M. D., Stocker, C., & Holzinger, A. (2017) · arXiv · doi:10.48550/arxiv.1708.04680
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A review on TinyML: State-of-the-art and prospects
Ray, P. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.019 · Cited by 460 · see citing works
TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
Schizas, N., Karras, A., Karras, C., & Sioutas, S. (2022) · Future Internet · doi:10.3390/fi14120363 · Cited by 236 · see citing works
Tiny Machine Learning: Progress and Futures [Feature]
Lin, J., Zhu, L., Chen, W.-M., Wang, W.-C., & Han, S. (2023) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2023.3302182 · Cited by 216 · see citing works
“• Ecology and agriculture: TinyML can also help with ecological, agricultural, environmental, and phenomics applications so as to conserve endangered species or forecast weather activities [35, 36, 37, 38, 39, 40].”
TinyML: Tools, applications, challenges, and future research directions
Kallimani, R., Pai, K., Raghuwanshi, P., Iyer, S., & López, O. L. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16740-9 · Cited by 128 · see citing works
“The study by [78] performed a phenomics image analysis which is based on tomato leaf disease and spider mites classification.”
Anjali, Jena, A., Bamola, A., Mishra, S., Jain, I., Pathak, N., Sharma, N., Joshi, N., Pandey, R., Kaparwal, S., et al. (2024) · Food Production Processing and Nutrition · doi:10.1186/s43014-023-00205-5 · Cited by 107 · see citing works
“Open-source projects like Orange data mining software provide standardized data processing workflows for various machine learning problems, streamlining data processing and analysis (Nakhle et al. 2021).”
MethodExplainable deep learning in plant phenotyping
Mostafa, S., Mondal, D., Panjvani, K., Kochian, L. V., & Stavness, I. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1203546 · Cited by 95 · see citing works
Wan, X., Li, Z., Yu, W., Wang, A., Ke, X., Guo, H., Su, J., Li, L., Gui, Q., Zhao, S., et al. (2023) · Advanced Materials · doi:10.1002/adma.202305192 · Cited by 81 · see citing works
Harfouche, A. L., Nakhle, F., Harfouche, A., Sardella, O. G., Dart, E., & Jacobson, D. (2022) · Trends in Plant Science · doi:10.1016/j.tplants.2022.08.021 · Cited by 52 · see citing works
Self-citationUnderstanding plant phenotypes in crop breeding through explainable AI
Danilevicz, M. F., Upadhyaya, S. R., Batley, J., Bennamoun, M., Bayer, P. E., & Edwards, D. (2025) · Plant Biotechnology Journal · doi:10.1111/pbi.70208 · Cited by 43 · see citing works
A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping
Ye, D., Wu, L., Li, X., Opeyemi, A. T., Wu, W., & Weng, H. (2023) · Plants · doi:10.3390/plants12081698 · Cited by 32 · see citing works
Federated explainable artificial intelligence (fXAI): a digital manufacturing perspective
Kusiak, A. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2238083 · Cited by 29 · see citing works
“Nakhle and Harfouche (2021) published a tutorial on fundamentals of artificial intelligence and its applications in image analysis.”
BackgroundDasgupta, S. (2024) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0175 · Cited by 25 · see citing works
“Several success stories demonstrate how these digital technologies have been effectively integrated across various omics fi elds, providing valuable insights that were previously inaccessible through conventional methods (Nakhle and Harfouche, 2021; Harfouche et al., 2023).”
Yu, H., Gao, M., Zhang, H., Yue, G., & Zhang, Z. (2023) · Energy · doi:10.1016/j.energy.2023.128338 · Cited by 12 · see citing works
Yu, H., Gao, M., Zhang, H., Chen, J., Liu, J., & Yue, G. (2024) · Journal of Thermal Science · doi:10.1007/s11630-024-1935-y · Cited by 12 · see citing works
“With the rapid advancement of AI technology [14–16], data-driven methods have proven their effectiveness and play a crucial role in the evolution towards smarter and more flexible operation of power plants [17, 18].”
Oakes, B., Famelis, M., & Sahraoui, H. A. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3638243 · Cited by 10 · see citing works
“For example, Nakhle and Harfouche provide four detailed Jupyter notebooks(52) walking domain experts in phenomics (plant sciences) through four steps of a ML task [82].”
AI-Powered Satellite Imagery Processing for Global Air Traffic Surveillance
Kayusi, F., Chavula, P., Juma, L., & Mishra, R. (2025) · LatIA · doi:10.62486/latia202580 · Cited by 9 · see citing works
Jeger, M. J., Beresford, R. M., Berlin, A., Bock, C. H., Fox, A., Gold, K. M., Newton, A. C., Civera, A. V., & Xu, X. (2024) · Plant Pathology · doi:10.1111/ppa.13927 · Cited by 8 · see citing works
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Shrinking the giants: Paving the way for TinyAI
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Self-citationImage-based yield prediction for tall fescue using random forests and convolutional neural networks
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Artificial-Intelligence-Based School Assistant for Detecting the Behavior of University Students
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“ii) PyCharm: This is an integrated development environment (IDE) that enables text editing, debugging, and complete interpretation of Python code [9].”
BackgroundMIPDB: A maize image-phenotype database with multi-angle and multi-time characteristicsPreprint
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Charting the rise of ONNX in swift image processing: a publications exploration
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“Moreover, ONNX's ability to accommodate various deep learning models and its alignment with top AI frameworks have spurred the creation of efficient, scalable image processing solutions [32-41].”
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“As a kind of feedforward neural network, the CNN has been usually used in industry process, such as image processing, Go AI programs [14, 15], whose main structure include input layer, convolution layer, ReLU layer, pooling layer, and full connection.”
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University Student Control Detection System Based on Machine Learning and Artificial Intelligence
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- Authors
- Nakhle, F., & Harfouche, A. L.
- Affiliations
- Università degli Studi della Tuscia (IT)
- Corresponding author
- Harfouche, Antoine L.
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 2
- Issue
- 9
- Pages
- 100323
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC8441561
- OpenAlex
- W3201637954
- Cited by
- 28
- References
- 152
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 9 · 2023: 8 · 2022: 4 · 2021: 1
- FWCI
- 0.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- UnitusOpen (Tuscia University) (submittedVersion)
- UnitusOpen (Tuscia University) (submittedVersion, cc-by-nc-nd)
- Funders
- European Commission
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Seventh Framework Programme (311929)
- 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
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, phenomics, image analysis, explainable AI, high-throughput phenotyping, plant breeding, machine learning, deep learning, ensemble learning
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