# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## 3 Results  
### Plant Recognition Efficiency Comparison  
The impact of threshold selection within VARI and TM methods for plant detection is significant. While lower threshold values may increase sensitivity and capture more detections, they can also introduce noise and false positives. Higher thresholds enhance specificity and reduce false positives but may risk missing some valid detections. In the context of the VARI-based method, a threshold of 0.6 is used to ensure that a broader range of potential plant detections is included, allowing for greater sensitivity. This is particularly useful in early growth stages when variations in plant health and density may not be as pronounced, and we want to capture as many relevant areas as possible without missing potential detections. Nevertheless, the higher threshold of 0.7 for the template correlation image indicates a more stringent requirement for similarity between the template and the image window. This higher threshold helps to minimize false positives by ensuring that only those areas with a strong correlation to the template are considered significant. In this case, prioritizing accuracy over inclusivity is essential, especially when precise identification of specific plant types or conditions is required, and represents one but crucial step in the density estimation and determination of the impact of the velocity of sowing. The overall metrics of qualitative evaluation were calculated for all methods and are presented in Table 3. For the third method, Mask R-CNN, a total of 21 images out of 810 were utilized for training, containing 588 masks for corn plants and 42 masks for non-corn objects. The training was conducted over 4000 epochs with a batch size of 2 images and a learning rate of 0.00025. After training, the model's performance was assessed on a fully georeferenced orthomosaic, employing a threshold of 0.7 for plant detection. Figure 3: The proposed procedure for the identification of rows and plants within them. Slightly lower results are obtained in [47], but those plants are detected in the presence of weeds. An unsupervised method based on VARI and semi-supervised TM with threshold t values of 0.6 and 0.7, respectively, demonstrated lower performance in corn plant detection compared to a supervised Mask R-CNN method in terms of all evaluation metrics shown in Table 3. Although VARI has a lower precision score than TM, its recall is higher compared to TM recall, indicating that VARI detected significantly more relevant instances (corn plants), which are represented through the F1 score. The best-performing method, Mask R-CNN, has very high (>0.95) and balanced scores for precision and recall, resulting in an F1 score of 0.98, showing superior detection of corn plants. Overall, while VARI and TM have their strengths, particularly in precision, Mask R-CNN provides the most robust and balanced performance across all metrics. Even a novel object detection algorithm, such as Yolov8, performs similarly for plant detection tasks [28]. To provide more insight into model performance, the number of true positives (TPs), false positives (FPs), true negatives (TNs), and false negatives (FNs) were calculated and are presented in Table 4 as additional performance metrics. Mask R-CNN had the highest number of true positives (16,156), indicating it correctly identifies the most actual instances, and the lowest number of false positives (135), suggesting it rarely misidentifies an instance that is not actually present. On the other side, it has the highest number of true negatives (916), suggesting it correctly identifies negative instances more frequently, and the lowest number of false negatives (500), indicating it misses fewer positive instances. VARI shows a moderate number of true positives (9611), meaning it correctly identifies a significant but smaller number of instances compared to Mask R-CNN, but has the highest number of false positives (1505) and the lowest number of true negatives (74), suggesting it has the highest rate of inaccuracy.