# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## 3 Results
### Plant Recognition Efficiency Comparison
TM has the lowest number of true positives (9265), indicating it correctly identifies the lowest number of actual instances among the three methods. The number of false positives (900) indicates it makes some incorrect positive identifications, and the highest number of false negatives suggests that it misses a significant number of positive instances, which could impact its overall effectiveness. A visual representation of detection results for all three methods on the test patch is given in Figure 4.  
Unsatisfactory results with VARI-based and TM methods are caused by falsely detecting weeds and grassland as corn plants. This misclassification undermines the reliability and usefulness of the VARI index, as it fails to provide an accurate representation of the distribution and density of the corn plants. Secondly, the unsupervised segmentation of the VARI-based method heavily relied on the greenness of plants and faced difficulties in accurately detecting smaller plants within the field. Consequently, the challenge of establishing an appropriate threshold that would enable the precise detection of plants of all sizes proved to be a complex and demanding task, even if high-resolution RGB images \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **VARI** & **TM** & **Mask R-CNN** \\ \hline TP & 9611 & 9265 & 16,156 \\ FP & 1505 & 900 & 135 \\ TN & 74 & 78 & 916 \\ FN & 4951 & 7580 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics (true and false positives and negatives) for all three detection methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **VARI (t = 0.6)** & **TM (t = 0.7)** & **Mask R-CNN** \\ \hline Accuracy & 0.61 & 0.52 & 0.96 \\ Recall & 0.66 & 0.55 & 0.97 \\ Precision & 0.88 & 0.91 & 0.99 \\ F1 Score & 0.76 & 0.69 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of plant detection methods. were provided. On the other hand, the main drawback of the template matching approach is the high computational complexity, especially in scenarios where a comprehensive analysis of a large number of plants is required, such as in this study. The Mask R-CNN model is trained using two classes: corn plant and non-corn plant objects, which encompass various elements such as weed plants, rocks, and machinery marks in the soil. This comparison offers insights into how varying levels of algorithm complexity affect precision and, based on the obtained results, still underscores the importance of accurate human labels that are necessary for supervised algorithms, such as deep neural networks. In addition, this comparison highlights the trade-off between algorithm complexity and achieving optimal performance. While simpler methods like this VARI-based one may be easier to implement, they may not yield the same level of precision as more complex approaches like Mask R-CNN in specific tasks such as plant detection, where the accuracy of plants is a crucial part of the estimation of the plant density. Finding the right balance between complexity and performance is crucial in the development of effective plant detection algorithms intended for edge computing, ensuring real-time results.