Pantazi, X.E.; Lagopodi, A.L.; Tamouridou, A.A.; Kamou, N.N.; Giannakis, I.; Lagiotis, G.; Stavridou, E.; Madesis, P.; Tziotzios, G.; Dolaptsis, K.;
et al. Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics. Sensors 2022, 22, 5970.
https://doi.org/10.3390/s22165970
AMA Style
Pantazi XE, Lagopodi AL, Tamouridou AA, Kamou NN, Giannakis I, Lagiotis G, Stavridou E, Madesis P, Tziotzios G, Dolaptsis K,
et al. Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics. Sensors. 2022; 22(16):5970.
https://doi.org/10.3390/s22165970
Chicago/Turabian Style
Pantazi, Xanthoula Eirini, Anastasia L. Lagopodi, Afroditi Alexandra Tamouridou, Nathalie Nephelie Kamou, Ioannis Giannakis, Georgios Lagiotis, Evangelia Stavridou, Panagiotis Madesis, Georgios Tziotzios, Konstantinos Dolaptsis,
and et al. 2022. "Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics" Sensors 22, no. 16: 5970.
https://doi.org/10.3390/s22165970
APA Style
Pantazi, X. E., Lagopodi, A. L., Tamouridou, A. A., Kamou, N. N., Giannakis, I., Lagiotis, G., Stavridou, E., Madesis, P., Tziotzios, G., Dolaptsis, K., & Moshou, D.
(2022). Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics. Sensors, 22(16), 5970.
https://doi.org/10.3390/s22165970
