## 4 Discussion  
Throughout the tested batch sizes, 'banana' exhibited superior performance according to most of the assessment metrics. In contrast,'sugarcane' and 'weeds' showed relatively low precision rates, while'spinach' and 'pepper' yielded average precision. Notably, at batch size 20 all crops returned above average precision. Additionally, the best precision for 'pepper' was recorded at batch size 60. The impressive performance of the model in automatic classification of the banana class, despite using the same quantity and quality of training data as other classes, can be attributed to several factors. First, the distinct visual features of banana leaves, such as their shape, size, and texture, set them apart from other plants on the farm. Although the colors of the other plants, including weeds, are similar, these unique visual characteristics make it easier for the model to distinguish bananas from other classes. Additionally, the banana class exhibited less variability within itself, meaning that the appearances of bananas in the images were more consistent. This consistency allowed the model to learn and recognize them more accurately. In addition, the banana plants were likely less obstructed by other objects or plants, making their features more visible. This clear visibility aids in more accurate classification by the model. Furthermore, bananas tend to have a higher contrast with their background and neighboring crops, which makes their features stand out more prominently in the images. This high contrast enhances the model's ability to detect and classify them accurately. The poor performance of the model in terms of precision when classifying certain classes, such as 'weeds' and'sugarcane', can be attributed to several factors. One significant \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Batch Size** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline
10 & 50.60 & 95 & 84 & 18 \\
20 & 51.30 & 84 & 82 & 34 \\
30 & 53.60 & 96 & 78 & 29 \\
40 & 44.90 & 87 & 79 & 20 \\
50 & 51.20 & 82 & 75 & 8 \\
60 & 57.70 & 89 & 83 & 18 \\
70 & 49.00 & 93 & 77 & 17 \\
80 & 52.80 & 98 & 84 & 23 \\
90 & 48.40 & 95 & 82 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Overall performance of the model (non-crop-specific). issue is the lack of distinct visual features in these classes compared to others such as bananas. In particular, weeds tend to have a wide variety of appearances, making it difficult for the model to learn a consistent set of characteristics for accurate identification. This high intra-class variability means that weeds can look very different from one another, leading to more classification errors. Another factor is the similarity in color and texture between weeds and the other crops in the dataset. This similarity can confuse the model, as it struggles to differentiate between weeds and certain crops, especially in mixed-crop environments. These overlapping visual features lead to lower precision, as the model incorrectly identifies non-weed objects as weeds and vice versa. For sugarcane, the performance issues could stem from the physical characteristics and growing patterns of the plant. Sugarcane plants are often tall and densely packed, which can result in significant occlusion. This occlusion means that parts of the sugarcane plants are blocked from view, preventing the model from seeing the complete structure and reducing the accuracy of classification. Additionally, the repetitive and similar appearance of sugarcane stalks can make it challenging for the model to identify unique features that distinguish sugarcane from other classes. Furthermore, spinach leaves are typically smaller and can be more variable in shape and size, which can make it difficult for the model to learn a consistent set of features by which to identify them accurately. Spinach often grows close to the ground and can be covered by other vegetation, resulting in occlusion that makes it challenging for the model to acquire a clear view of the entire plant.