Among the major concerns of agricultural production includes food safety, environmental impact, efficient energy usage and climate change. To give response to this, the paper introduced the devised indoor farming methodology that utilized red and blue light-emitting diode (LED) as light source for the horticulture of lettuce in Simple Nutrient Addition Program (SNAP) hydroponics setup. The study focused on the evaluation of two experimental setups comparing the conventional SNAP hydroponics to the SNAP hydroponics using LED lights. At the end of the main experiment, the results suggested that LED as light source have a significant effect on the number of leaves (t (41.7) = 6.07, p < 0.05) and leaf area (t (48) = 4.39, p = 0.05) of a lettuce. Specifically, the results showed that when the environment of the lettuce was controlled, their capability to grow significantly more and larger leaves increases. Also, the weight obtained using the SNAP with LED lights setup (3.04 kg) was 30% greater than the weight obtained using conventional (2.18 kg) hydroponics setup.
This paper presents a program which identifies the 4-panel LCC equivalent of rice plants using image processing techniques and pattern recognition of the Backpropagation neural network. Images of the fully expanded healthy leaves were captured by digital camera and processed through RGB acquisition, color transformation, image enhancement, image segmentation and feature extraction procedures. The extracted features were computed using basic statistical methods, then served as the input to the neural network for LCC panel identification. Thirty (30) samples of IRR 82372H -Mestiso 26 variety were tested; divided into three sets with 10 leaf samples per field. The system was observed to provide an accuracy of 93.33%.
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