Detection of reed using cnn method and analysis of the dry reed (phragmites australis) for a sustainable lake area

Abstract

Background Common reed (Phragmites australis L.) is a highly productive wetland plant and a possible valuable resource of renewable biomass worldwide. For a sustainable management the exploitation of reed is beneficial because the increasing demand for sustainable biomass which presents reed bed areas and wetlands. Knowing the properties of plant biomass obtained from reeds is essential both for the effect on combustion equipment and for the impact on the environment. Brates Lake, situated in Galati, Romania is a natural watershed with reed plantations. Results We used the convolutional neural network method combined with the cropped image techniques represent a powerful tool for high-precision image-based biomass detection in lake areas. The study aimed to investigate the morphological and chemical parameters through SEM–EDX analysis and pH, conductivity, nitrate anion, nitrite anion, total nitrogen, sulphate anion, sulphide anion, phosphate anion concentrations were determined from reed extract. The samples have a moderately acidic reaction pH 4.91–4.98. The number of soluble salts in the reed extract is in the range of 3.24–4.70 g/L, the values are within normal limits, providing the plant with the necessary nutrients. Conclusions This is the first time that neural networks are used for the detection and prediction of areas at risk for biodiversity (reduction of water gloss until it disappears, imbalances caused by keeping reeds dry in water) caused by the aggressive and uncontrolled growth of reeds.

A New Composite Material with Environmental Implications for Sustainable Agriculture

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Preparation of Wheat Straw Hot-Pressed Board through Coupled Dilute Acid Pretreatment and Surface Modification

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Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber

“…Artificial intelligence has already demonstrated its potential across various fields like pharmaceuticals, engineering, and biotechnology. In the case of biotechnology, Obreja et al [6] implemented neural networks to identify and predict areas at risk for biodiversity decline, such as the reduction in water loss to the point of disappearance and imbalances resulting from the unchecked growth of reeds. In the case of heat transfer, Zou et al [7] used a neural regression model for predicting the thermal conductivity of carbon nanotubes nanofluids with multiple base fluids.…”