Next Article in Journal
Simulation and Evaluation of Heat Transfer Inside a Diseased Citrus Tree during Heat Treatment
Previous Article in Journal
Transition of Agricultural Mechanization, Agricultural Economy, Government Policy and Environmental Movement Related to Rice Production in the Mekong Delta, Vietnam after 2010
Article

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

Facultad de Ingeniería, Universidad Nacional de Asunción, Campus Universitario, San Lorenzo C.P. 111421, Paraguay
*
Author to whom correspondence should be addressed.
Received: 5 November 2020 / Revised: 6 December 2020 / Accepted: 15 December 2020 / Published: 4 January 2021
This work presents an alternative method, referred to as Productivity Index or PI, to quantify the production of hydroponic tomatoes using computer vision and neural networks, in contrast to other well-known metrics, such as weight and count. This new method also allows the automation of processes, such as tracking of tomato growth and quality control. To compute the PI, a series of computational processes are conducted to calculate the total pixel area of the displayed tomatoes and obtain a quantitative indicator of hydroponic crop production. Using the PI, it was possible to identify objects belonging to hydroponic tomatoes with an error rate of 1.07%. After the neural networks were trained, the PI was applied to a full crop season of hydroponic tomatoes to show the potential of the PI to monitor the growth and maturation of tomatoes using different dosages of nutrients. With the help of the PI, it was observed that a nutrient dosage diluted with 50% water shows no difference in yield when compared with the use of the same nutrient with no dilution. View Full-Text
Keywords: artificial neural networks; digital image processing; precision agriculture artificial neural networks; digital image processing; precision agriculture
Show Figures

Figure 1

MDPI and ACS Style

Palacios, D.; Arzamendia, M.; Gregor, D.; Cikel, K.; León, R.; Villagra, M. Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing. AgriEngineering 2021, 3, 1-18. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3010001

AMA Style

Palacios D, Arzamendia M, Gregor D, Cikel K, León R, Villagra M. Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing. AgriEngineering. 2021; 3(1):1-18. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3010001

Chicago/Turabian Style

Palacios, Diego, Mario Arzamendia, Derlis Gregor, Kevin Cikel, Regina León, and Marcos Villagra. 2021. "Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing" AgriEngineering 3, no. 1: 1-18. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3010001

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop