# A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
## 4 Discussion  
The first critical point in the study of weed and crop cover is linked to the determination and the availability of \"true values\". It is not easy to obtain an accurate measurement of the plant cover because of difficulties in performing a direct measurement (e.g., by using tools such as planimeters) and in turning the surface values into cover values. In the end, image processing remains the more feasible method to measure plant cover, and the possibility of manually segmenting images, which the software provides, allows users to reach an adequate level of accuracy. Moreover, it is reasonable to assume that any inaccuracies in image segmentation are regularly distributed and therefore they are not able to substantially alter the results. In our study, the equivalency of the TCG values obtained with [PERSON] and [PERSON] provided an additional element of confirmation in the consistency of these methods [44,45]. A different matter, on the other hand, concerns the discrimination between crops and weeds, since the use of spectral information, especially if only RGB images are available, does not provide sufficient information to distinguish one plant species from another. In the early stages of growth, it is particularly difficult to discriminate between crops and weeds, since both have similar reflectance characteristics [46,47]. This can be a serious barrier for farmers who intend to adopt the economic threshold, as a rule, to manage post-emergence weeding, since, as reported by various authors [22,48,49,50], the estimation of the weed cover seems to be the best proxy to evaluate the infestation level in a field and to consequently act. The first of the two proposed methods (M1), based on the joint estimation of the two parameters (AM and AW) showed poor accuracy and reliability in estimating the maize green cover (underestimated) and the weed green cover (overestimated), as also reported by other authors who followed a similar approach [27,51]. These failures are probably due to the high variability in the green cover of each single weed plant if compared to that of maize plants. Only the use of a great number of images (and of the respective counts of maize and weed plants) could allow us to better calibrate AM and AW and reduce the inaccuracy. The second method proposed seems to be more promising. The estimate of the AM based on a different dataset, allowed by the high homogeneity in maize plant growth,  
Figure 5: Determination of the economic threshold. The dotted black line represents the cost of the weeding (115.20 \(\mathbf{\xi}\)). The blue (C1), yellow (C2) and green (C3) curves represent, respectively, the damage curve obtained by using q = 2.5 and m = 0.70 (C1), q = 3.7 and m = 0.39 (C2) and q = 6.2 and m = 0.32 (C3). The threshold is identified by the intersection of the line of the weeding cost with the three curves. The points represent the observed values of WGC and RYL on the control plots (CP). and the consequent subtraction of the M2-MGC\({}_{\rm WP+CP}\) from the Ca-TGC\({}_{\rm WP+CP}\) reduced the uncertainty in the estimate of the M2-WGC\({}_{\rm WP+CP}\), improving the fitting efficacy with respect to both Er-WGC\({}_{\rm WP+CP}\) (EF = 0.66 at 10 m) and RYL (EF = 0.48 at 10 m). The large variability in cover values for weed plants was again the main reason for the discrepancy between predicted and observed values. Image acquisition in an earlier stage of the crop development could reduce the heterogeneity of weed cover values (since newly emerged weeds should have less variability), but there would be the risk of neglecting all the weeds that could emerge later. Regarding the relationship between WGC and RYL, this changes in relation to the weed species composition, the crop type and vigour, and the soil and climatic conditions [22; 50; 52]. The \(q\) and \(m\) values of Equation (10) have to be properly calibrated for site-specific conditions.