As the world’s population exponentially grows, more food production is required. This increasing food production currently has led to the un-sustainable production of chemical fertilizers and resultant overuse. A more sustainable option to enhance food production could be the use of fertilizer derived from food waste. To address this, we investigated the possibility of utilizing a fertilizer derived from food waste to grow hydroponic vegetables. Arugula (Eruca sativa) ‘Slow Bolt’ and lettuce (Lactuca sativa) ‘Cherokee’ and ‘Rex’ were cultivated using indoor deep-flow hydroponic systems at 23 ºC under a photosynthetic photon flux density of 170 µmol∙m−2∙s−1 with an 18-hour photoperiod. Plant nutrient solutions were provided by food waste fertilizer or commercial 15:5:20 NPK fertilizer at the identical electrical conductivity (EC) of 2.3 mS·cm–1. At the EC of 2.3 mS·cm–1, chemical fertilizer contained 150 ppm N, 50 ppm P, and 200 ppm K, while food waste fertilizer had 60 ppm N, 26 ppm P, and 119 ppm K. Four weeks after the nutrient treatments were implemented, compared to plants grown with chemical fertilizer, lettuce ‘Rex’ grown with food waste fertilizer had four less leaves, 27.1% shorter leaves, 68.2% and 23.1% less shoot and root fresh weight, respectively. Lettuce ‘Cherokee’ and arugula grown with food waste fertilizer followed a similar trend with fresh shoot weights that were 80.1% and 95.6% less compared to the chemical fertilizer, respectively. In general, the magnitude of reduction in the plant growth was greatest in arugula. These results suggest that both fertilizers were able to successfully grow lettuce and arugula, although the reduced plant growth with the food waste fertilizer in our study is likely from a lower concentration of nutrients when we considered EC as an indicator of nutrient concentration equivalency of the two fertilizer types.

This thesis proposes a new steering system for agricultural machinery with the aim of improving the automation capabilities of farming robots. Accurate and reliable autonomous machinery has the potential to provide significant benefits to the efficiency of farming operations, but the existing systems for performing one of the most essential automation functions, autonomous steering to keep machinery on the proper course, each have drawbacks that impact their usability in various scenarios. In order to address these issues, a new lidar-based system was developed for automatic steering in a typical farm field. This approach uses a two-dimensional lidar unit to scan the ground in front of the robot to detect and steer based on farm tracks, a common feature in many farm fields. This system was implemented and evaluated, with results demonstrating that the system is capable of providing accurate steering corrections.