A novel robotic solution for improving tomato harvesting process in greenhouses. Includes design of a new machine learning vision algorithm for harvesting.

In the US alone, 3 billion pounds of consumed fresh tomatoes are grown in greenhouse farms. These farms improve food security, require less land, and consume less resources; however, for these farms to operate, large labor forces are needed to maintain, harvest, and package the tomatoes. These jobs, though paying well above minimum wage, do not attract local workers and force farmers to use immigrant labor. This lack of local labor availability and unsupportive governmental regulations has led to high turnover rates and high training costs. This prevents growers from growing their full potential and limits their ability to expand their operations. With new governmental regulations increasing labor costs by 35% in Ontario (one of the largest greenhouse growing regions in North America) and major labor availability issues in the US, growers are in desperate need of solutions. Many are actively seeking out automation to solve this issue, but there are currently no solutions for them to use. This high rising need, with a current absence of a solution, makes the development of greenhouse automation crucial for the success of this industry.With harvesting as the largest expenditure for greenhouse labor, a tomato harvesting robot is proposed. Previous researchers have attempted to develop a tomato harvesting robot for this environment, but none has yet to enter the market. The main reason for this has been a lack of accuracy and speed in computer vision algorithms; however, in the last five years, convolutional neural networks (CNN) have enabled this once impossible task to become possible. By applying CNNs to tomato harvesting, a higher level of accuracy will be reached, thereby making the creation of a tomato harvesting robot feasible.