# Optimization Model for Selective Harvest Planning Performed by Humans and Robots
## 5 Numerical Studies--Harvesters with Different Capabilities to Classify Pepper Maturity Levels
### Analysis of the Robotic Harvester Capability  
The assumption is that a robotic harvester can potentially classify fruits into multiple maturity classes, even to their exact DAA, by using AI methods [52]. Peppers are therefore harvested at the optimal time giving higher weights. In this numerical example, we compared different types of human harvesters (Table 1) with a robotic harvester.  
2.1 Difference in the Total Harvest Weight between Robotic and Human Harvesters with the Same Capabilities  
This analysis investigated the difference in the total harvest weight (THW) between a single robot and a number of workers of different types, where the robot and the total number workers had the same harvest capabilities \(C_{h}\). This type of analysis can be applied to estimate the cost efficiency of a robotic harvester and the maximal price a farmer will agree to pay for such a robot for one month of operation. To evaluate how the robot's ability to classify peppers according to their DAA affects the harvest weight (THW) in one month, the THW of a robot was compared to the THW of a number of workers, where the number of workers was set to a number giving the same \(C_{h}\) as one robot (Table 1). The robot's THW was then compared to the THW of the workers for each worker type. Six workers (\(N=6\)) were selected as the basis for comparison, with a total capacity of \(C_{h}=40{,}500\) fruits per working day being equivalent to the ability of one robot. Any number lower than this (produced by six workers) creates an imbalanced use case with a high supply of peppers in the greenhouse, resulting in the harvesting of peppers solely from the highest maturity class. Similarly, any number higher than \(C_{h}=40{,}500\) creates a use case in which all the peppers available in the greenhouse are harvested.  
The results in Table 4 show the robotic harvester's high potential, with a 12.28, 7.33, and 4.9% increase in the THW when compared to workers of Types A, B, and C, respectively. The results show the importance of the ability to classify maturity for evaluating the feasibility of using a robotic harvester. Since the results are presented in terms of harvest weight, the higher the pepper price, the greater the advantage of the robot when compared to the workers. From Table 4, the difference in THW between the robot and the workers can be calculated as quality savings [55] (pp. 683-687) measured for the planning horizon period. For example, the quality savings between the robot and Type B workers is 2716 kg/month, resulting in overall savings of 23,765 kg/year. The quality savings can then be used to calculate return-on-investment measures. This analysis also reveals the importance of decreasing the robot's cost to increase its economic feasibility, as expected.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Harvester Type** & **Robot** & **6 Type A Workers** & **6 Type B Workers** & **6 Type C Workers** \\ \hline THW (kg) & 37,069.03 & 32,885.64 & 34,352.45 & 35,253.99 \\ in one month & & & & \\ \hline THW (kg) & & & & \\ in one year & 324,354 & 287,749.35 & 300,583.93 & 308,472.41 \\ (35 harvest weeks) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The total harvest weight (kilogram) for a harvester robot and for different types of workers with the same \(C_{h}\) for one month and for an estimated growing period of 35 weeks.