## 3 Results  
The system under study was tested using 175 images captured in a real environment for two different crop types: beef and cluster tomatoes. For each type, the success and failure rates (Tables 1 and 2) were calculated in relation to three different \"objects\":  
Figure 17: Geometric relationship based on tomato morphology.  
Figure 18: Convex area center (C) and peduncle (P) tomato detection.  
There are three different types of failures, which we will call:  
* Failure 1: An object that should have been detected/located is NOT detected or located;
* Failure 2: An object is detected or located that should NOT have been detected/located;
* Failure 3: An object that should be detected/located by the system, is detected/located but not correctly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Success and Failure Rates**} & \multicolumn{2}{c}{**Total Elements That Should**} \\ \multicolumn{2}{c}{**Have Been Correctly Located by the System \({}^{*}\)**} \\ \hline \multirow{4}{*}{(a) Tomatoes} & Success & 90\% \\ \cline{2-3}  & Failure 1 & 10\% \\ \cline{2-3}  & Failure 2 & 0\% \\ \cline{2-3}  & Failure 3 & 0\% \\ \hline \multirow{4}{*}{(b) Peduncles} & Success & 91.3\% \\ \cline{2-3}  & Failure 1 & 8.7\% \\ \cline{2-3}  & Failure 2 & 0\% \\ \cline{2-3}  & Failure 3 & 0\% \\ \hline \multirow{4}{*}{(c) Tomatoes peduncles} & Success & 80.8\% \\ \cline{2-3}  & Failure 1 & 19.2\% \\ \cline{2-3}  & Failure 2 & 0\% \\ \cline{2-3}  & Failure 3 & 0\% \\ \hline \multicolumn{3}{c}{\({}^{*}\) Tomato peduncles} \\ \end{tabular}
\end{table}
Table 1: Results provided by the system for all beef tomato images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Success and Error Rates**} & \multicolumn{2}{c}{**Of the Total Elements That Should Have**} \\ \multicolumn{2}{c}{**Been Correctly Located by the System \({}^{*}\)**} \\ \hline \multirow{4}{*}{(a) Tomatoes} & Success & 79.7\% \\ \cline{2-3}  & Failure 1 & 6.8\% \\ \cline{2-3}  & Failure 2 & 0\% \\ \cline{2-3}  & Failure 3 & 11.9\% \\ \hline \multirow{4}{*}{(b) Peduncles} & Success & 69.5\% \\ \cline{2-3}  & Failure 1 & 27.1\% \\ \cline{2-3}  & Failure 2 & 0\% \\ \cline{2-3}  & Failure 3 & 3.4\% \\ \hline \multirow{4}{*}{(c) Tomatoes peduncles} & Success & 63.2\% \\ \cline{2-3}  & Failure 1 & 29.4\% \\ \cline{2-3}  & Failure 2 & 0\% \\ \cline{2-3}  & Failure 3 & 7.4\% \\ \hline \multicolumn{3}{c}{\({}^{*}\) Tomato peduncles = ((number of successful tests)/(total number of tests)) \(\times 100\).} \\ \end{tabular}
\end{table}
Table 2: Success and error rates for the cluster tomatoes.  
The reason why the system must only detect fruit located in the foreground, and which are not occluded (or that their occlusion is not relevant), is because only tomatoes meeting these characteristics (in addition to those related to the degree of maturity) can be collected first. In order to detect (and collect) the occluded ripe tomatoes, it is first necessary to harvest the tomatoes that lie in front. Moreover, one must take into account that each time a tomato is harvested, the position of the other ripe fruit is usually affected. For these reasons, if the system were implemented on a real robot picker, a new image of the plant would need to be taken after each tomato is collected, and then recalculate the position of the next tomato for harvesting, since the picking of its neighbor could have altered its previous position.