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

Abstract

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.

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“…[188]. Improvement of accuracy in the present Computer Vision plant phenotyping methods using Deep Learning [189][190][191][192], Fuzzy [193] logic, etc. Biomass and quantity mapping [194], dead or diseased tree detection [195] using Unmanned Aerial Vehicle (UAV) or drone-based imaging.…”