. Suroso
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Application of Image Processing and Artificial Neural Network Technologgy for Predicting the Water and Nutrient Status during the Groeth of Read Chili Plant Subrata, I Dewa Made; Suroso, .; Dwinanto, .
Jurnal Keteknikan Pertanian Vol 15, No 2 (2001): Buletin Keteknikan Pertanian
Publisher : PERTETA

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Abstract

The objective of this research is to predict the water and nutrient status during the growth of red chili plant by means of an image processing and artificial neural network algorithm. In this study, about 150 chili plants were cultivated at LeuwiKopos Greenhouse but only 30 plants were chosen as sampbs. The data were collected using a CCD Camera only during the vegetative growth and the collected data were analyzed using a back propagation artificial neural network. The results showed the linear relationship between the predicted and targetvalues of water with a coefficient determination of 0.728 and 0.973 for the training and validation data, respectively. The linear relationships were also found between the predicted and target values of nutrient with a coefficient determination of 0.716 and 0.963 for the training and validation data, respectively
Application of Image Processing and Artificial Neural Network Technologgy for Predicting the Water and Nutrient Status during the Groeth of Read Chili Plant Subrata, I Dewa Made; Suroso, .; Dwinanto, .
Jurnal Keteknikan Pertanian Vol 15, No 2 (2001): Buletin Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The objective of this research is to predict the water and nutrient status during the growth of red chili plant by means of an image processing and artificial neural network algorithm. In this study, about 150 chili plants were cultivated at LeuwiKopos Greenhouse but only 30 plants were chosen as sampbs. The data were collected using a CCD Camera only during the vegetative growth and the collected data were analyzed using a back propagation artificial neural network. The results showed the linear relationship between the predicted and targetvalues of water with a coefficient determination of 0.728 and 0.973 for the training and validation data, respectively. The linear relationships were also found between the predicted and target values of nutrient with a coefficient determination of 0.716 and 0.963 for the training and validation data, respectively