## 3 Operational Requirements
### Fruit Identification and Selection
#### 3.1.2 Machine Learning Algorithms  
Machine learning algorithms are essential for processing images captured by a fruit-harvesting robot and making precise decisions. Convolutional Neural Networks (CNNs) are particularly effective in this context due to their specialized design for image pattern recognition. By learning from extensive datasets, CNNs can automatically identify complex visual features such as shapes, colors, and textures. This enables the robot to accurately distinguish between ripe and unripe fruits, as well as detect other important factors like defects or diseases [14; 15; 16; 17]. CNNs are preferred for their high accuracy and efficiency in handling visual tasks. Their ability to process and analyze large volumes of image data allows for precise fruit recognition and quality assessment, which is crucial for successful fruit harvesting. By leveraging CNNs, a robotic arm can reliably perform tasks that require detailed visual inspection, making them the ideal choice for developing advanced fruit-harvesting systems [18; 19]. The algorithms must be tailored to the specific fruit being harvested, as a single algorithm cannot serve all types of fruit. Before starting the design process, it is crucial to define the target fruit, as this will significantly impact the robot's final performance. Figure 3 shows how fruits are categorized resulting in the data in Table 1.  
According to the information provided by [20], each fruit is associated with its own database, resulting in unique weightings for each type. It is important to note that the values presented in Table 1 pertain to the work of [PERSON] and [PERSON], and there is no need to replicate these exact values in independent projects. Figure 2: Ripe fruit recognition system. The use of simple machine vision algorithms and neural network-based systems for fruit recognition in harvesting robots reveals significant differences in focus, accuracy, and adaptability [21]. Simple machine vision algorithms are deterministic and rely on predefined rules, utilizing techniques such as color segmentation and edge detection. These algorithms are easier to implement and require fewer computational resources, but they have limited accuracy and adaptability, particularly when faced with variations in environmental conditions and different types of fruits [22]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Sample** & **Percentage** \\ \hline Banana & 1.067 & 7.89 \\ Cherry & 1.001 & 7.41 \\ Strawberry & 1.004 & 7.43 \\ Lemon & 1.029 & 7.61 \\ Tangerine & 1.005 & 7.44 \\ Mango & 1.013 & 7.49 \\ Apple & 1.032 & 7.64 \\ Blackberry & 1.086 & 8.03 \\ Orange & 1.038 & 7.68 \\ Papaya & 1.026 & 7.59 \\ Pear & 1.070 & 7.92 \\ Pineapple & 1.018 & 7.53 \\ Grape & 1.127 & 8.34 \\ \hline \hline \end{tabular}  
* The results shown in Table 1 apply only to the research of the corresponding authors. If a similar process is replicated, its own data validation must be performed, using an image database of the best possible resolution.  
\end{table} 
Table 1: Resulting weighting for fruits. Figure 3: Images by Fruit Classes. In contrast, neural network-based systems, particularly convolutional neural networks (CNNs), employ a deep learning approach to identify ripe fruits. These networks learn relevant features from large datasets during training, enabling them to better adapt to environmental variations and different fruit types [23]. Although they require an intensive training phase and greater computational resources, neural networks offer higher accuracy and are more robust to changes in environmental conditions [24]. Simple machine vision algorithms are more suitable for systems with hardware constraints and less variable scenarios, while neural network-based systems provide superior accuracy and adaptability, making them ideal for environments with changing conditions and greater data variability. The choice between these approaches will depend on the specific requirements of the system and the operating conditions of the harvesting robot [25].