# Optimization Model for Selective Harvest Planning Performed by Humans and Robots
## 2 Literature Review
created a multiperiod stochastic model to determine the cultivating strategy for crops under rainfall uncertainty [48]. This model did not include _time window constraints_, i.e., the optimal time for harvesting was not considered. [PERSON] and [PERSON] considered four out of the five features described above. Their study dealt with cultivating strategy and machinery selection [49], aiming to maximize profits while minimizing environmental impact. The developed model did not include the _uncertainty_ feature; the researchers used historical weather data to determine the available working hours of the machines but did not consider the weather data effect on crop maturity, yield, and costs [49]. In addition, _none of the above models considered selective harvesting_ (focusing on decisions related to individual fruit). _The current paper addresses this knowledge gap by focusing on decisions related to individual fruit and taking into consideration four features for selective harvesting_. The inventory control feature was excluded, because we assume that fresh fruit are supplied on the same day of harvest. The literature review revealed three studies that solved a problem with similarities to _planning the harvest of a selective crop_. [PERSON] and [PERSON] developed a model for scheduling the harvest operation of grapes to maximize harvest quality with minimum cost. Their model used a quality decay function to measure the quality loss of the grapes and its effect on the profits corresponding to the harvest date [47]. However, in this case, the grape crop was _harvested once in a season_ (not selectively harvested). In contrast, crops such as sweet peppers require _selective harvesting_, _once every few days_. _Each fruit could be harvested at different maturity levels over an extended period_. Therefore, when planning to address the harvest of a selective crop problem, the inventory level and product quality can be controlled via the right decision related to the harvested product's maturity level. [PERSON] et al. developed a multilevel decision-making system for human and machine cotton harvesters. They created a hierarchical system of three levels; the first level determines the harvesting speed, the second level re-allocates the resources to create balanced teams, and the third level re-allocates the teams among farms, aiming to maximize the probability of completing cotton harvesting by a due date [50]. The principle of integrating two types of resources (man-machine) is similar to the current work formulation. However, cotton, similar to grapes, is a non-selective crop and is _harvested all at once_. Therefore, _adjustment for a selective crop_, such as sweet peppers, was needed and was the aim of this paper. [PERSON] et al., suggested an approach for grape-selective harvest scheduling and planning via management zone delineation [51]. Their work minimized the total costs of harvest operations and established planning and scheduling for selective harvest of each selected management zone. Although dealing with selective harvesting, their article _did not consider the fruit growth function_ or the _maturity status of individual fruit_. In this paper, we present _a model for deciding on the selective harvest of an individual fruit_. Four features are considered in the model, which is based on an economic analysis that takes into account robot/worker capabilities and the growth model of the fruit. The model is demonstrated for sweet pepper harvesting, but it is general and can be applied to other crops requiring selective harvesting.