- Al-Saddik, H., J. C. Simon, and F. Cointault. 2018. Assessment of the Optimal Spectral Bands for Designing a Sensor for Vineyard Disease Detection: The Case of Flavescence Dorée. Precision Agriculture (0123456789). org/10.1007/s11119-018-9594-1.
- Alisaac, E., J. Behmann, M. T. Kuska, H.W. Dehne, and A. K. Mahlein. Hyperspectral Quantification of Wheat Resistance to Fusarium Head Blight: Comparison of Two Fusarium Species. European Journal of Plant Pathology. doi.org/10.1007/s10658-018-1505-9.
- Breiman, L. 2001. Random Forests. Machine Learning 45 (1): 5-32.
- Hastie, T., R. Tibshirani, and J. Friedman. 2009. Springer Series in Statistics The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer-Verlag New York. springerlink.com/index/10.1007/b94608.
- Kheiralipour, , H. Ahmadi, A. Rajabipour, Sh. Rafiee, M. Javan-Nikkhah, D. S. Jayas, and K. Siliveru. 2016. Detection of Fungal Infection in Pistachio Kernel by Long-Wave near-Infrared Hyperspectral Imaging Technique. Quality Assurance and Safety of Crops and Foods 8 (1): 129-35. doi.org/10.3920/QAS2015.0606.
- Mahlein, A., Christian Oerke, U. Steiner, and H. Wilhelm Dehne. 2012. Recent Advances in Sensing Plant Diseases for Precision Crop Protection. European Journal of Plant Pathology 133 (1): 197-209.
- Moghimi, A., C. Yang, M. E. Miller, F. Shahryar Kianian, and P. M. Marchetto. A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging. Frontiers in Plant Science. doi.org/10.3389/fpls.2018.01182.
- Moghimi, A., M. H. Aghkhani, A. Sazgarnia, and M. H. Abbaspour-Fard. 2011. Improvement of NIR Transmission Mode for Internal Quality Assessment of Fruit Using Different Orientations. Journal of Food Process Engineering 34 (5): 1759-74.
- Moghimi, A., C. Yang, and J. A. Anderson. 2020. Aerial Hyperspectral Imagery and Deep Neural Networks for High-Throughput Yield Phenotyping in Wheat. Computers and Electronics in Agriculture 172: 105299. org/abs/1906.09666.
- Moghimi, A., C. Yang, J. A. Anderson, and S. K. Reynolds. 2019. Selecting Informative Spectral Bands Using Machine Learning Techniques to Detect Fusarium Head Blight in Wheat. In ASABE Annual International Meeting, Boston, MA. org/10.13031/aim.201900815. (August 13, 2019).
- Moghimi, A., C. Yang, and P. M. Marchetto. 2018. Ensemble Feature Selection for Plant Phenotyping: A Journey from Hyperspectral to Multispectral Imaging. IEEE Access 6: 56870-84.
- Mohammadigol, R., H. Khoshtaghaza, R. Malekfar, M. Mirabolfathi, and A. M. Nikbakht. 2013. Detection of Pistachio Aflatoxin Using Raman Spectroscopy and Artificial Neural Networks. Journal of Agricultural Machinery 5 (1): 1-9. (In Persian). http://dx.doi.org/10.22067/jam.v5i1.28122.
- Mutanga, O., and A. K. Skidmore. 2007. Red Edge Shift and Biochemical Content in Grass Canopies. ISPRS Journal of Photogrammetry and Remote Sensing 62 (1): 34-42.
- Nagasubramanian, , S. Jones, S. Sarkar, A. K. Singh, A. Singh, and G. Baskar. 2018.Hyperspectral Band Selection Using Genetic Algorithm and Support Vector Machines for Early Identification of Charcoal Rot Disease in Soybean Stems. Plant Methods 14 (1): 1-13. doi.org/10.1186/s13007-018-0349-9.
- Sankaran, S., A. Mishra, J. M. Maja, and R. Ehsani. 2011. Visible-near Infrared Spectroscopy for Detection of Huanglongbing in Citrus Orchards. Computers and Electronics in Agriculture 77 (2): 127-34. org/10.1016/j.compag.2011.03.004.
- Susic, N., U. Zibrat, S. Sirca, P. Strajnar, J. Razinger, M. Knapic, and B. Stare. 2018. Discrimination between Abiotic and Biotic Drought Stress in Tomatoes Using Hyperspectral Imaging. Sensors and Actuators, B: Chemical 273(June): 842-52.
- Stefan, T., J. Behmann, A. Steier, T. Kraska, O. Muller, U. Rascher, and A. K. Mahlein. Quantitative Assessment of Disease Severity and Rating of Barley Cultivars Based on Hyperspectral Imaging in a Non-Invasive, Automated Phenotyping Platform. Plant Methods 14 (1): 45. doi.org/10.1186/s13007-018-0313-8.
- Wahabzade, M., A. K. Mahlein, C. Bauckhage, U. Steiner, E. C. Oerke, and K. Kersting. Plant Phenotyping Using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants. Scientific Reports 6(February): 22482. http://www.nature.com/articles/srep22482.
|