# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## 3 Results
### In-Row Plant Uniformity Estimators in the Context of Plant Detection  
In Figure 5, the estimated distribution of plant spacings inferred from considered methods for plant detection is illustrated. The distributions of estimated plant spacings extracted from TM and VARI-based plant detection methods indicate a wide range of distances, with a relatively high proportion of spacings outside the expected distance range. In the case of Mask R-CNN plant detections, the estimated distribution of extracted spacings among plants is the most similar to the actual values indicated by the ground truth (GT). Figure 4: The efficiency of corn plant detection techniques is demonstrated on a portion of the patch. In the following, our analysis will be focused on establishing an association between the identified plant spacings rather than solely relying on corn plant detection. The number of misses N\({}_{\text{miss}}\) and doubles N\({}_{\text{multiple}}\) provides relatively trustworthy indications of the method's performance, and further research was carried out. The GT of the manually labeled plants revealed a total of 16,698 plants in the surveyed location. The VARI-based and TM methods accurately recognized 9611 and 9256 plants, respectively (Figure 6a). In the observed area, the Mask R-CNN method achieved the highest number of recognized plants (16,156), indicating that only 3.3% of plants remained unidentified. These findings suggest that the Mask R-CNN method could be used to predict the quantity of maize plants in various planting density scenarios. Another query that requires an answer is the structural correspondence between the GT plant spacings and the plant spacings estimated according to the detected plants by the considered methods. Figure 6b indicates that the Mask R-CNN exhibited inferior relative accuracy in identifying doubles, recognizing only 11, in contrast to VARI's 63 and TM's 76. Because VARI and TM produced poor results in terms of plant detection, the consequence is a significantly higher number of missed spacings, with the majority of them being irrelevant. When the 3569 and 4089 missing plants that are yielded via VARI-based and TM are taken into consideration, the missed spacings that are derived from the Mask R-CNN (1602) technique are rather close to the GT (1347). Understanding missed spacing, which can be either a single miss or multiple misses, represents an additional level of plant detection quality analysis. Taking a look at Figure 6, it is evident that the Mask R-CNN (8.45%) algorithm achieved results that were comparable to those of the GT algorithm (7.54%) in terms of the relative share of single missed spacings. The VARI-based algorithm demonstrates a significantly higher relative quantity of single missed spacings (18.87%), and the TM algorithm provided the greatest discrepancy from GT parameters (23.59%). An identical outcome was observed concerning the double missed spacings, wherein the GT spacings recorded a mere 0.64%. The percentage of double missed spacings by the Mask R-CNN was 1.2%, whereas it was significantly higher for the TM (9.78%) and marginally lower for the VARI-based (7.10%). Figure 5: Distribution of plant spacing determined by the plant detection method. The distribution of GT spacings illustrates the disparity in plant detection accuracy among the chosen methods. It can be inferred that the ratio of achieved outcomes for various detection techniques remains consistent across all levels of spacing errors. Figure 7 can provide insights into specific attributes of the tested models. QFi illustrates (Figure 7) the precision with which the models perceive the population of maize plants. The predicted percentages of 90.10% for Mask R-CNN, 67.33% for the VARI-based method, and 59.03% for TM show that Mask R-CNN is better at estimating the plant population within the expected spacing range than the other two methods.