# Optimization Model for Selective Harvest Planning Performed by Humans and Robots
## 1 Introduction  
The supply chain for fresh fruit and vegetables involves a number of steps from crop selection to shipment to the customer, including harvesting, processing, packaging, and transporting the produce [1]. Operational research models have been developed for this supply chain at three decision levels: strategic, tactical, and operational [2, 3]. The strategic level includes long-term horizon decisions, for example, deciding on farm and plot locations [4]. The tactical level involves medium-term decisions, such as crop selection, scheduling, and allocation [2, 5]. The operational level involves short-term decisions, such as water allocation, land preparation, pricing decisions, and harvest scheduling [5, 6, 7]. Harvest planning includes several factors (e.g., scheduling and machinery capacity, allocation, and timing) that have been investigated at all three levels. At the strategic level, research has focused on topics such as the number of harvesting machines needed for filling a specific storage capacity [8] and the evaluation of different planting and harvesting alternatives using bio-economics characteristics [2]. At the tactical level, research has focused on allocating harvester machines between a number of fields to reduce costs [9] and on optimizing harvesting and loading sequences to minimize delays in moving products to storage [10]. At the operational level, research has been conducted on optimizing harvest data to maximize mill productivity, and on analyzing the labor effect on fruit quality and quantity at harvest [2, 11, 12]. However, there are no studies examining operational decisions related to the harvesting of individual fruit. Selective harvesting of high-value crops, such as apples, tomatoes, and broccoli, is currently performed mainly by humans, rendering it one of the most labor-intensive and expensive agricultural tasks [13]. Any attempts to mechanize all or parts of the supply chain for these crops must take into account the decades-long research on the robotization of many different agricultural applications, such as transplanting, spraying, cultivating, trimming, and selective harvesting [14]. This research has indeed indicated the technical feasibility of using robotic harvesters in orchards (e.g., apples [15, 16] and citrus [17, 18]),greenhouses (e.g., tomatoes [19] and sweet peppers [20]) and open fields (e.g., melons [21] and asparagus [22]), but the robots developed to date still lack commercial applicability, since they have failed to reach the efficiency of human harvesters [13; 23]. A robotic harvester must detect the fruit, reach the fruit, decide whether to harvest the fruit, and then grasp and disconnect the fruit from the branch. The harvest decision of a selective robotic harvester thus includes a number of aspects. First, methods for maturity classification must be developed [24; 25]. Second, since many fruits are hidden or partially obscured, the best camera viewpoints for maturity classification must be selected [26]. Since additional viewpoints cost time, an intelligent decision as to the necessity for an additional viewpoint and its location is needed [27]. The third and last aspect investigates the operational considerations of maturity classification and harvesting, namely, deciding whether the fruit is ripe and whether to harvest it, based on a consideration of the above points. By addressing these aspects, the harvest decision of a selective robotic harvester can be improved. To the best of our knowledge, no research has examined the decision making of a selective robotic harvester. This research lacuna may be attributed to the research focus on proving the technical feasibility of robotic harvesters [23], i.e., developing sensing and grasping capabilities.