Document Type : Review
Authors
1 ABRII
2 Department of Biotechnology, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran
3 Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Abstract
Machine learning plays a crucial role in identifying specific stressors that impact plant species and provides a comprehensive understanding of the challenges plants face in natural environments. The use of machine learning algorithms has significantly enhanced our ability to classify and differentiate the types of stress. There are two main methodologies in machine learning: supervised learning and unsupervised learning. In supervised learning, the model is trained using input-output data pairs, while unsupervised learning involves training the model without access to output labels. Unsupervised learning is primarily used for data exploration and dimension reduction. This detailed classification helps us better understand the distinct characteristics associated with different stressors and provides a more nuanced view of the plant stress landscape. Machine learning also enables the quantitative assessment of stress intensity and extent, allowing for an accurate evaluation of its impact on plant health and productivity. This quantitative approach helps researchers measure the true extent of stressors and their effects on the overall health of plant ecosystems. By employing advanced algorithms, machine learning can make predictions about future occurrences of stress and their potential consequences on plant ecosystems. This foresight strengthens preventive measures for sustainable agricultural practices, as researchers and practitioners can anticipate and mitigate potential threats to plant health. The purpose of this review is to provide a comprehensive understanding of the applications and concepts of machine learning in uncovering the complexity of plant stress phenotyping and elucidating the involved molecular mechanisms.
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Main Subjects
Anim-Ayeko, A. O., Schillaci, C., & Lipani, A. (2023). Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning. Smart Agricultural Technology, 4, 100178.
Arora, A., Misra, T., Kumar, M., Marwaha, S., Kumar, S., & Chinnusamy, V. (2023). Computer Vision Approaches for Plant Phenotypic Parameter Determination. In Digital Ecosystem for Innovation in Agriculture (pp. 263-270). Springer.
Aslam, M., Maqbool, M. A., Zaman, Q. U., & Akhtar, M. A. Uncovering the tolerance of mungbean (Vigna radiata L. Wilczek) genotypes under saline conditions using k-mean cluster analysis.
Behmann, J., Schmitter, P., Steinrücken, J., & Plümer, L. (2014). Ordinal classification for efficient plant stress prediction in hyperspectral data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 29-36.
Bhatia, A., Chug, A., & Singh, A. P. (2020). Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach. International Journal of Future Generation Communication and Networking, 13(4), 71-78.
Biau, G. (2012). Analysis of a random forests model. The Journal of Machine Learning Research, 13, 1063-1095.
Bock, C., Poole, G., Parker, P., & Gottwald, T. (2010). Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical reviews in plant sciences, 29(2), 59-107.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Calderón, R., Navas-Cortés, J. A., & Zarco-Tejada, P. J. (2015). Early detection and quantification of Verticillium wilt in olive using hyperspectral and thermal imagery over large areas. Remote Sensing, 7(5), 5584-5610.
Cen, H., Weng, H., Yao, J., He, M., Lv, J., Hua, S., Li, H., & He, Y. (2017). Chlorophyll fluorescence imaging uncovers photosynthetic fingerprint of citrus Huanglongbing. Frontiers in plant science, 8, 1509.
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215.
Cynthia, S. T., Hossain, K. M. S., Hasan, M. N., Asaduzzaman, M., & Das, A. K. (2019). Automated detection of plant diseases using image processing and faster R-CNN algorithm. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI),
Daneshafrooz, N., Bagherzadeh Cham, M., Majidi, M., & Panahi, B. (2022). Identification of potentially functional modules and diagnostic genes related to amyotrophic lateral sclerosis based on the WGCNA and LASSO algorithms. Scientific reports, 12(1), 20144.
Das Choudhury, S., Guadagno, C. R., Bashyam, S., Ewers, B. E., & Samal, A. Stress Phenotyping Analysis Leveraging Autofluorescence Image Sequences with Machine Learning. Frontiers in plant science, 15, 1353110.
Dehmer, M., & Basak, S. C. (2012). Statistical and machine learning approaches for network analysis. Wiley Online Library.
Ghahramani, N., Shodja, J., Rafat, S. A., Panahi, B., & Hasanpur, K. (2021). Integrative systems biology analysis elucidates mastitis disease underlying functional modules in dairy cattle. Frontiers in Genetics, 12, 712306.
Gill, T., Gill, S. K., Saini, D. K., Chopra, Y., de Koff, J. P., & Sandhu, K. S. (2022). A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping. Phenomics, 2(3), 156-183.
Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE international conference on computer vision,
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition,
Gou, C., Zafar, S., Hasnain, Z., Aslam, N., Iqbal, N., Abbas, S., Li, H., Li, J., Chen, B., & Ragauskas, A. J. (2024). Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants. Frontiers in Bioscience-Landmark, 29(1), 20.
Hesami, M., Alizadeh, M., Jones, A. M. P., & Torkamaneh, D. (2022). Machine learning: Its challenges and opportunities in plant system biology. Appl Microbiol Biotechnol, 106(9-10), 3507-3530.
Holasou, H. A., Panahi, B., Shahi, A., & Nami, Y. (2024). Integration of machine learning models with microsatellite markers: New avenue in world grapevine germplasm characterization. Biochemistry and Biophysics Reports, 38, 101678.
Islam, M., Dinh, A., Wahid, K., & Bhowmik, P. (2017). Detection of potato diseases using image segmentation and multiclass support vector machine. 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE),
Kaiser, E., Von Gillhaussen, P., Clarke, J., & Schurr, U. (2024). IPPS 2022-plant phenotyping for a sustainable future. Frontiers in plant science, 15, 1383766.
Kaundal, R., Kapoor, A. S., & Raghava, G. P. (2006). Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC bioinformatics, 7(1), 1-16.
Kumar, S. (2021). Plant disease detection using CNN. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 2106-2112.
Maksup, S., Roytrakul, S., & Supaibulwatana, K. (2014). Physiological and comparative proteomic analyses of Thai jasmine rice and two check cultivars in response to drought stress. Journal of plant interactions, 9(1), 43-55.
Meraj, T., Sharif, M. I., Raza, M., Alabrah, A., Kadry, S., & Gandomi, A. H. (2024). Computer vision-based plants phenotyping: A comprehensive survey. Iscience, 27(1).
Naik, H. S., Zhang, J., Lofquist, A., Assefa, T., Sarkar, S., Ackerman, D., Singh, A., Singh, A. K., & Ganapathysubramanian, B. (2017a). A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant methods, 13, 1-12.
Naik, H. S., Zhang, J., Lofquist, A., Assefa, T., Sarkar, S., Ackerman, D., Singh, A., Singh, A. K., & Ganapathysubramanian, B. (2017b). A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant methods, 13(1), 1-12.
Niazian, M., & Niedbała, G. (2020). Machine learning for plant breeding and biotechnology. Agriculture, 10(10), 436.
Panahi, B., Frahadian, M., Dums, J. T., & Hejazi, M. A. (2019). Integration of cross species RNA-Seq meta-analysis and machine-learning models identifies the most important salt stress–responsive pathways in microalga Dunaliella. Frontiers in Genetics, 10, 752.
Panahi, B., Mohammadi, S. A., & Doulati-Baneh, H. (2020). Characterization of Iranian grapevine cultivars using machine learning models. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 90, 615-621.
Panahi, B., Tajaddod, S., Mohammadzadeh Jallali, H., Hejazi, M. A., & Zeinalabedini, M. (2022). Variability and association among some pomological and physiochemical traits in spring frost tolerant genotypes of Persian walnut (Juglans regia L.) and selection of genotypes with superior traits based on machine learning algorithms. Genetic Resources and Crop Evolution, 1-13.
Paul, A., Ghosh, S., Das, A. K., Goswami, S., Choudhury, S. D., & Sen, S. (2020). A review on agricultural advancement based on computer vision and machine learning. In Emerging technology in modelling and graphics (pp. 567-581). Springer.
Peña, J. M., Torres-Sánchez, J., Serrano-Pérez, A., De Castro, A. I., & López-Granados, F. (2015). Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors, 15(3), 5609-5626.
Pierz, L. D., Heslinga, D. R., Buell, C. R., & Haus, M. J. (2023). An image‐based technique for automated root disease severity assessment using PlantCV. Applications in Plant Sciences, 11(1), e11507.
Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., & Murchie, E. H. (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience, 6(10), gix083.
Pujari, D., Yakkundimath, R., & Byadgi, A. S. (2016). SVM and ANN based classification of plant diseases using feature reduction technique. IJIMAI, 3(7), 6-14.
Rajesh, B., Vardhan, M. V. S., & Sujihelen, L. (2020). Leaf disease detection and classification by decision tree. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184),
Raza, S.-e.-A., Smith, H. K., Clarkson, G. J., Taylor, G., Thompson, A. J., Clarkson, J., & Rajpoot, N. M. (2014). Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. Plos one, 9(6), e97612.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
Reza, M. N., Na, I. S., Baek, S. W., & Lee, K.-H. (2019). Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images. Biosystems engineering, 177, 109-121.
Sadeghi, M., Panahi, B., Mazlumi, A., Hejazi, M. A., Komi, D. E. A., & Nami, Y. (2022). Screening of potential probiotic lactic acid bacteria with antimicrobial properties and selection of superior bacteria for application as biocontrol using machine learning models. LWT, 162, 113471.
Sari, W. E., Kurniawati, Y. E., & Santosa, P. I. (2020). Papaya Disease Detection Using Fuzzy Naïve Bayes Classifier. 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI),
Sarkar, C., Gupta, D., Gupta, U., & Hazarika, B. B. (2023). Leaf disease detection using machine learning and deep learning: Review and challenges. Applied Soft Computing, 110534.
Seka, D., Bonny, B., Yoboué, A., Sié, S., & Adopo-Gourène, B. (2019). Identification of maize (Zea mays L.) progeny genotypes based on two probabilistic approaches: Logistic regression and naïve Bayes. Artificial intelligence in agriculture, 1, 9-13.
Shaik, R., & Ramakrishna, W. (2014). Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice. Plant physiology, 164(1), 481-495.
Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., Gechev, T., Hussain, T., & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in plant science, 14, 1158933.
Shrestha, G., Das, M., & Dey, N. (2020). Plant disease detection using CNN. 2020 IEEE Applied Signal Processing Conference (ASPCON),
Silva, J. C. F., Teixeira, R. M., Silva, F. F., Brommonschenkel, S. H., & Fontes, E. P. (2019). Machine learning approaches and their current application in plant molecular biology: A systematic review. Plant Science, 284, 37-47.
Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in plant science, 21(2), 110-124.
Singh, A. K., Ganapathysubramanian, B., Sarkar, S., & Singh, A. (2018). Deep learning for plant stress phenotyping: trends and future perspectives. Trends in plant science, 23(10), 883-898.
Singh, U., Khemka, N., Rajkumar, M. S., Garg, R., & Jain, M. (2017). PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea. Nucleic acids research, 45(22), e183-e183.
Sun, S., Wang, C., Ding, H., & Zou, Q. (2020). Machine learning and its applications in plant molecular studies. Briefings in functional genomics, 19(1), 40-48.
Tahmasebi, A., Niazi, A., & Akrami, S. (2023). Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species. Scientific reports, 13(1), 847.
Tripathi, A., Goswami, T., Trivedi, S. K., & Sharma, R. D. (2021). A multi class random forest (MCRF) model for classification of small plant peptides. International Journal of Information Management Data Insights, 1(2), 100029.
Trivedi, V. K., Shukla, P. K., & Pandey, A. (2022). Automatic segmentation of plant leaves disease using min-max hue histogram and k-mean clustering. Multimedia Tools and Applications, 81(14), 20201-20228.
Vakilian, K. A. (2020). Machine learning improves our knowledge about miRNA functions towards plant abiotic stresses. Scientific reports, 10(1),1-10.
van Dijk, A. D. J., Kootstra, G., Kruijer, W., & de Ridder, D. (2021). Machine learning in plant science and plant breeding. Iscience, 24(1), 101890.
Virnodkar, S. S., Pachghare, V. K., Patil, V., & Jha, S. K. (2020). Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision Agriculture, 21(5), 1121-1155.
Wang, Q., & Qi, F. (2019). Tomato diseases recognition based on faster RCNN. 2019 10th International Conference on Information Technology in Medicine and Education (ITME),
Wetterich, C. B., Kumar, R., Sankaran, S., Junior, J. B., Ehsani, R., & Marcassa, L. G. (2013). A comparative study on application of computer vision and fluorescence imaging spectroscopy for detection of citrus huanglongbing disease in USA and Brazil. Laser Science,
Xu, X., Li, H., Yin, F., Xi, L., Qiao, H., Ma, Z., Shen, S., Jiang, B., & Ma, X. (2020). Wheat ear counting using K-means clustering segmentation and convolutional neural network. Plant methods, 16, 1-13.
Yadav, J., & Sharma, M. (2013). A Review of K-mean Algorithm. Int. J. Eng. Trends Technol, 4(7), 2972-2976.
Yan, J., & Wang, X. (2022). Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology. The Plant Journal, 111(6), 1527-1538.
Zhou, J., Fu, X., Zhou, S., & Zhou, J. (2018). Evaluation of the performance of machine learning methods in soybean segmentation for image-based high-throughput phenotyping in greenhouse. 2018 ASABE Annual International Meeting,
Zubler, A. V., & Yoon, J.-Y. (2020). Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning. Biosensors, 10(12), 193.
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