In agricultural operations, the timing plays a crucial role. If the operations are completed too early or too late, the profitability is reduced due to the decrement in quality. In the pesticide spraying operation, the late completion of operation can cause severe damage to the plants as the epidemic prevention need to be taken immediately after the disease is being detected. The major challenge in optimizing the timing in agricultural operation is in the sense of route planning that is closely related to the amount of inputs consumed such as fuel, pesticide and labors. All these factors will affect the cost of routes that need to be followed by agricultural vehicle to complete the specific tasks in agriculture.
Nowadays, the greenhouse environment has becoming a popular choice to the farmers due to its ability to optimize the productivity of the plants in any weather conditions. To execute the agricultural operations inside the greenhouse, robotics technology has been widely used. However, most of the previous works in greenhouse robotics only focusing on the mechanization and task completion such as plant detection and optimizing the robot arm’s movement in harvesting and spraying operation. Therefore, the work which focuses to minimize the timing in greenhouse operation by designing an efficient navigation system has not being explored.
Despite the lack of work which focuses to optimize the timing in the greenhouse operation, several related works have been found to optimize the operational time in the open field. Some approaches only consider a single objective in planning the routes with the main aim to minimize the travelling distance in completing the agricultural operation. However, as most of the real problems in agriculture has several constraints and objectives such as such as minimizing the distance, number of turnings, amount of spraying, completion time and maximizing yield in the open field, the multi-objective approach is becoming the most preferred approach in designing the agricultural routes. Therefore, several works has been focusing on methods to optimize several objectives. However, the implemented works in the open field only considered a limited number of destinations which mainly used represent the field entry.
In selective spraying operation, the mobile robot requires to navigate to the specific number of plants to execute the operation. The number of plants is usually affected by the epidemic spreading rate inside the greenhouse environment which is usually high in number. Therefore, an efficient multi-objective algorithm that can plan the routes considering multiple objectives with a higher number of destinations is needed to minimize the operational costs in greenhouse environment for selective spraying operation.