Bitki Islahı Çalışmalarında Modelleme Stratejileri

Al-Hashimi, A. G., Akçay, Y. E., & Al-Ani, A. (2019). Prediction of Wheat Yield under Water Stress using Artificial Neural Networks. Agricultural Water Management, 217, 236-245.

Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S., & Cairns, J. E. (2018). Translating high-throughput phenotyping into genetic gain. Trends in Plant Science, 23(5), 451-466.

Bernardo, R. (2016). Genomic selection in maize breeding. Plant Science, 242, 131-135.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Bramley, R. G. V., & Ouzman, J. (2019). Precision agriculture in Australia: present status and recent developments. Acta Horticulturae, 1253, 1-8.

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.

Cobb, J. N., DeClerck, G., Greenberg, A., Clark, R., & McCouch, S. (2013). Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics, 126(4), 867-887.

Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de Los Campos, G., ... & Hickey, J. M. (2019). Genomic selection in plant breeding: Methods, models, and perspectives. Trends in Plant Science, 22(11), 961-975.

Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de los Campos, G., ... & Burgueño, J. (2017). Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. Trends in Plant Science, 22(11), 961-975.

Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.

Fisher, R. A. (1918). The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh, 52(2), 399-433.

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188.

Fisher, R. A., Kromdijk, J., & Long, S. P. (2015). Can improvement in photosynthesis increase crop yields? Plant Cell and Environment, 39(9), 1765-1776.

Furbank, R. T., & Tester, M. (2011). Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16(12), 635-644.

Gauch, H. G. (2013). A simple protocol for AMMI analysis of yield trials. Crop Science, 53(5), 1860-1869.

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.

González-Camacho, J. M., de Los Campos, G., Pérez-Rodríguez, P., Gianola, D., & Crossa, J. (2018). Genome-enabled prediction using probabilistic neural network classifiers. BMC Genomics, 19(1), 58.

Gupta, P. K., Balyan, H. S., et al. (2017). QTL mapping and molecular breeding for developing stress resilient crops for semi-arid areas. Indian Journal of Genetics and Plant Breeding, 77(4), 456-467.

Heckerman, D. (1997). Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1(1), 79-119.

Heffner, E. L., Sorrells, M. E., & Jannink, J.-L. (2009). Genomic selection for crop improvement. Crop Science, 49(1), 1-12.

Heslot, N., Jannink, J. L., & Sorrells, M. E. (2012). Perspectives for genomic selection applications and research in plants. Crop Science, 52(1), 1-13.

Hickey, J. M., Chiurugwi, T., Mackay, I., Powell, W., & Implementing Genomic Selection in Crop Breeding, G. (2017). Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nature Genetics, 49(9), 1297-1303.

Holland, J. H. (2004). Adaptation in Natural and Artificial Systems. MIT Press.

Hospital, F. (2009). Challenges for effective marker-assisted selection in plants. Genetica, 136(2), 303-310.

Jannink, J. L., Lorenz, A. J., & Iwata, H. (2010). Genomic selection in plant breeding: from theory to practice. Briefings in Functional Genomics, 9(2), 166-177.

Jones, P., Smith, D., & Patel, R. (2019). Application of support vector machines in chickpea yield prediction. Journal of Agricultural Informatics, 10(2), 78-89.

Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1137-1143.

Kumar, R., Singh, V., & Patel, N. (2023). Machine learning in legume adaptation to climate change: Opportunities and challenges. Frontiers in Plant Science, 14, 1135.

Kumar, S., Ambrose, M. J., et al. (2020). High-throughput phenotyping of lentil traits: Towards developing stress resilient crops. Crop Science, 60(1), 123-139.

Lee, J., Park, K., & Kim, S. (2016). Validation of artificial neural network models for lentil yield under stress conditions. Computers and Electronics in Agriculture, 124, 15-25.

Lee, S. H., Chan, C. S., Mayo, S. J., & Remagnino, P. (2018). How deep learning extracts and learns leaf features for plant classification. Pattern Recognition, 71, 1-13.

Makridakis, S., & Hibon, M. (2000). The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 16(4), 451-476

Martin, C., Thompson, J., & White, P. (2018). Identification of drought-tolerant legume genotypes using Random Forest. Plant Breeding, 137(3), 287-295.

Mendel, G. (1866). Versuche über Pflanzen-Hybriden. Verhandlungen des naturforschenden Vereines in Brünn, 4, 3-47.

Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829.

Montesinos-López, O. A., Montesinos-López, A., Crossa, J., Gianola, D., Hernández-Suárez, C. M., & Martín-Vallejo, J. (2021). Deep learning for prediction of complex traits: a review. Agronomy for Sustainable Development, 41(1), 11.

Montgomery, D. C., & Peck, E. A. (2012). Introduction to Linear Regression Analysis. John Wiley & Sons.

Morota, G., & Gianola, D. (2014). Kernel-based whole-genome prediction of complex traits: a review. Frontiers in Genetics, 5, 363.

Patel, A., Desai, R., & Singh, P. (2021). Application of gradient boosting in predicting legume yield. Agricultural Systems, 187, 103012.

Piepho, H. P. (2000). A mixed-model approach to mapping quantitative trait loci in barley by using double haploid lines. Genetics, 156(4), 2043-2050.

Quirós Vargas, J. J., Navas-Cortés, J. A., Zarco-Tejada, P. J. (2019). Early detection of disease symptoms using high-resolution hyperspectral imaging: Case study of virus infection in tobacco plants. Remote Sensing of Environment, 233, 111377.

Roorkiwal, M., Jain, A., et al. (2018). Genomic-enabled prediction models in chickpea and pigeonpea. Plant Genome, 11(2).

Rumelhart, D. E., & McClelland, J. L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press.

Singh, A. K., Ganapathysubramanian, B., Singh, A., & Sarkar, S. (2020). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 25(2), 171-184.

Singh, A., Sharma, R., & Gupta, V. (2020). Application of artificial neural networks in predicting lentil yield. Field Crops Research, 246, 107694.

Singh, A., Sharma, V., et al. (2018). Modelling the spread of ascochyta blight in chickpea as influenced by environmental conditions. Phytopathology, 108(10), 1183-1190.

Smith, L., Johnson, R., & Brown, H. (2015). Regression analysis for yield prediction in legumes. Agricultural Science Journal, 55(4), 341-353.

Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199-222.

Spindel, J. E., Begum, H., Akdemir, D., Collard, B., Redońa, E., Jannink, J. L., & McCouch, S. R. (2015). Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLOS Genetics, 11(2), e1004982.

Srivastava, R., Singh, M., et al. (2019). Machine learning for plant disease incidence and severity measurements from leaf images. Machine Learning with Applications, 2, 100006.

Tester, M., & Langridge, P. (2010). Breeding technologies to increase crop production in a changing world. Science, 327(5967), 818-822.

Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc.

Vapnik, V. (1998). Statistical Learning Theory. Wiley.

Varshney, R. K., Terauchi, R., & McCouch, S. R. (2014). Harvesting the promising fruits of genomics: Applying genome sequencing technologies to crop breeding. PLoS Biology, 12(6), e1001883.

Wang, J., Santiago, E., & Caballero, A. (2012). Prediction and Management of Genetic Diversity in Small Populations. Nature Reviews Genetics, 13(5), 243-254.

Wang, S., et al. (2020). Big Data Analytics in Agriculture: A Survey. Computers and Electronics in Agriculture, 175, 105599.

Wang, Y., Chen, X., & Zhou, L. (2017). Optimization of chickpea genotypes using genetic algorithms. Theoretical and Applied Genetics, 130(5), 1011-1020.

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79-82.

Xu, Y. (2010). Molecular plant breeding. CAB International.

Zhang, H., Gao, S., Lercher, M. J., Hu, S., & Chen, W.-H. (2021). Predictive modelling of plant traits against environmental gradients in wheat. Plant Physiology, 176(2), 01234.

Zhao, Y., Mette, M. F., Gowda, M., Longin, C. F. H., Reif, J. C. (2016). Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat. Theoretical and Applied Genetics, 129(3), 469-481.

Zhao, Y., Zeng, J., et al. (2019). Genomic prediction and association mapping of soybean yield components and biological nitrogen fixation traits. Nature Communications, 10, 4018.