The use of hyperspectral sensors for quality assessment : A quantitative study of moisture content in indoor vertical farming

Sammanfattning: Purpose: This research will study how hyperspectral sensoring can assess the moisture content of lettuce by monitoring its growth in indoor vertical farming. Research questions: “What accuracy can be achieved when using hyperspectral sensoring for assessing the moisture content of lettuce leaves grown in vertical farming?” “How can vertical farming contribute to sustainability in conjunction with integration of NIR spectroscopy?” Methodology: This study is an experimental study with a deductive approach in which experiments have been performed using the hyperspectral technologies singlespot sensor and the hyperspectral camera Specim FX17 to collect spectral data. To analyze the data from the experiments two regression models were used and trained to make it possible to predict future moisture content values in lettuce. In order to get a better understanding and analyze the results from the experiments, a literature review was also conducted on how hyperspectral imaging has been applied to assess the quality of food products. Conclusion: The achieved accuracies were 58.24 % and 65.54 % for the PLS regression model and the Neural Network model respectively. Employing hyperspectral sensoring as a non-destructive technique to assess the quality of food products grown and harvested in vertical farming systems, contributes to sustainability from several aspects such as reducing food waste, minimizing costs and detecting different quality attributes that affect the food products.