Optimal Autonomous Robotic Harvest Systems for Cotton: Parameterizing a Whole-Farm Linear Programming Model

Improved equipment management has been a perpetual task of agricultural producers. This is especially true of cotton producers due to specialized equipment used for the sole purpose of harvest. Questions regarding autonomous robotics regarding reducing or replacing traditional cotton pickers have been raised by farmers and researchers. Iterations between autonomous “swarm bots” and status quo equipment were developed to ascertain optimal levels of robotics. Autonomous robotics may make multiple passes through the field and harvest lint from bolls as it matures therefore increasing fiber quality and minimizing risk. This study parameterizes a whole-farm linear programming (LP) model evaluates benefits and costs a range of autonomous cotton harvesting systems. A standard LP model of farm production was used to evaluate a typical Mid-south farm operation. The evaluation of marginal costs of sequentially smaller equipment were accomplished by building upon the linear programming models. Once the base farm was defined, the model parameters were modified in a series of linear programming (LP) runs. Linear programming is a mathematical tool for solving an objective function such as maximizing returns to fixed costs with respect to a set of whole-farm constraints on land, unpaid family labor, and capital under a given weather regime. The impact of autonomous cotton harvest was evaluated by specifying the optimization problem as a linear programming model. A set of simulations were conducted to assess break points that autonomous robotics are physically feasible and economically sound. Results are pertinent to cotton producers and equipment manufacturers as this may reduce one of their largest expenses in cotton production.