The management of the abundant amount of used plastic bottle waste is a major concern nowadays, because it is a major contributor to landfills and overburdens waste processing facilities. Once disposed of, plastic can take centuries to break down, hence, recycling not only manages the waste efficiently, but it reduces the environmental impact and creates economic opportunities, as well. An incentive-based Reverse Vending machine (RVM) is an effective way to involve the general public in the management of plastic waste. The existing solutions are either very expensive, from a computation and cost perspective, or they lack the robustness and durability necessary for deployment. In this research, we have developed an incentive-based low-cost RVM for the collection, identification, classification, and sorting of used plastic bottles with the addition of a reward-based user application. The developed RVM includes a low-cost computing device running a lightweight image processing algorithm, sensors, camera, and a self-designed mechanical arm. To support the low computing device in the RVM, a lightweight MobileNet model has been trained using transfer learning. A dataset of 10,983 pet bottle images was collected using a camera installed inside the machine for the classification model. The results of the study show that MobileNet achieved 99.2% testing accuracy, which is better than the existing bottle-classification approaches. Moreover, the weight of the developed model was only 12 Mb, which is fourteen and six times less than inceptionV3 and Res-Net (Residual Neural Networks), respectively. Furthermore, the developed RVM costs a fraction of the price, compared to the existing solutions. Two versions of the machine have been deployed at a University for more than 6 months, collecting over 650 kg of plastic waste.

Weed management has become a highly labor-intensive activity, which is the reason for decreased yields and high costs. Moreover, the lack of skilled labor and weed-resistant herbicides severely impact the agriculture sector and food production, hence increasing the need for automation in agriculture. The use of agricultural robots will help in the assurance of higher yields and proactive control of the crops. This study proposes a laser-based weeding vehicle with a unique mechanical body that is adjustable relative to the field structure, called the Robot Operating System (ROS) based robust control system, and is customizable, cost-effective and easily replicable. Hence, an autonomous-mobile-agricultural robot with a 20 watt laser has been developed for the precise removal of weed plants. The assembled robot’s testing was conducted in the agro living lab. The field trials have demonstrated that the robot takes approximately 23.7 h at the linear velocity of 0.07 m/s for the weeding of one acre plot. It includes 5 s of laser to kill one weed plant. Comparatively, the primitive weeding technique is highly labor intensive and takes several days to complete an acre plot area. The data presented herein reflects that implementing this technology could become an excellent approach to removing unwanted plants from agricultural fields. This solution is relatively cost-efficient and provides an alternative to expensive human labor initiatives to deal with the increased labor wages.