Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress

MLA

Mohamed A. Sharaf-Eldin, et al. “Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress.” Horticulturae, vol. 9, no. 1, Jan. 2023, p. 79. EBSCOhost, https://doi.org/10.3390/horticulturae9010079.

APA

Mohamed A. Sharaf-Eldin, Salah Elsayed, Adel H. Elmetwalli, Zaher Mundher Yaseen, Farahat S. Moghanm, Mohssen Elbagory, Sahar El-Nahrawy, Alaa El-Dein Omara, Andrew N. Tyler, & Osama Elsherbiny. (2023). Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress. Horticulturae, 9(1), 79. https://doi.org/10.3390/horticulturae9010079

Chicago

Mohamed A. Sharaf-Eldin, Salah Elsayed, Adel H. Elmetwalli, Zaher Mundher Yaseen, Farahat S. Moghanm, Mohssen Elbagory, Sahar El-Nahrawy, Alaa El-Dein Omara, Andrew N. Tyler, and Osama Elsherbiny. 2023. “Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress.” Horticulturae 9 (1): 79. doi:10.3390/horticulturae9010079.