Enhancing citrus fruit yield investigations through flight height optimization with UAV imaging

Citrus fruit yield is essential for market stability, as it allows businesses to plan for production and distribution. However, yield estimation is a complex and time-consuming process that often requires a large number of field samples to ensure representativeness. To address this challenge, we investigated the optimal altitude for unmanned aerial vehicle (UAV) imaging to estimate the yield of Citrus unshiu fruit. We captured images from five different altitudes (30 m, 50 m, 70 m, 90 m, and 110 m), and determined that a resolution of approximately 5 pixels/cm is necessary for reliable estimation of fruit size based on the average diameter of C. unshiu fruit (46.7 mm). Additionally, we found that histogram equalization of the images improved fruit count estimation compared to using untreated images. At the images from 30 m height, the normal image estimates fruit numbers as 73, 55, and 88. However, the histogram equalized image estimates 88, 71, 105. The actual number of fruits is 124, 88, and 141. Using a Vegetation Index such as IPCA showed a similar estimation value to histogram equalization, but I1 estimation represents a gap to actual yields. Our results provide a valuable database for future UAV field investigations of citrus fruit yield. Using flying platforms like UAVs can provide a step towards adopting this sort of model spanning ever greater regions at a cheap cost, with this system generating accurate results in this manner.

easy to approach because of the fruit color.The color of the C. unshiu fruit peel is orange, distinguished from the green leaf of a tree.For the C. unshiu fruits, the peel is green that accumulates β, ε-carotenoid at the primary time; by the time flow, the peel accumulations change to β, β-carotenoid that color is orange [27][28][29] .The image analysis can be separated from the tree and counted using the color difference between the leaf and fruit.
Counting fruit from C. unshiu tree image by the algorithm is already been studied and proven efficient 30 .Also, it was suggested that using UAV is a kind of remotely sensed real-time quantification, which is good for optimizing resource utilization 31 .Therefore, for predicting the yield of C. unshiu, using a UAV aerial image is one of the effective methods.The possibility of this method was reported on a limited basis that tested only 15 m flying height 32 .Even there are few references for the flying height of UAVs and image analysis.Additionally, which image analysis methods are proper for fruit yield prediction have barely been studied.Histogram equalization is the method that improves the contrast of the image for medical image analysis or other fruitcounting studies [33][34][35][36] .For farmers' profit and to reduce resource waste, accurate and adequate predicting of yield is essential.Also, decreasing the effort, such as human labor or time consumption, is emphasized during the data collection for estimating the final yield.For example, in data collection, researchers in Korea estimate fruit yield every August by manually dispatching and counting the fruit from 640 trees across 320 orchards.This approach is very inefficient and produces many errors.To solve these waste and inefficient, the appropriate application of technology affects the efficiency of yield prediction, would be resulting in an increment in profit for farmers and consumers and decrement in resource waste.In the current study, we compared the five flying heights of UAV images of C. unshiu.Also, we compared normal images to histogram equalization images in which flying height and analysis methods were used.

Study area and data collection
The study areas are 1821-1, OraI-dong, Jeju-si, Jeju-do, the Republic of Korea as area A and 94, Haryegwangjangro 41beon-gil, Namwon-eup, Seogwipo-si, Jeju-do, the Republic of Korea as area B. The area of each field is 1500 m 2 and 1200 m 2 .The image of a field-planted C. unshiu tree was taken by unmanned aerial vehicles (UAV) as the Matrice 300 (SZ DJI Technology Co., China), the UAV which mounted RGB camera (X5S, SZ DJI Technology Co., China), and multispectral image sensor (Rededge M, Micasense) record the image of trees at each height.The FOV of UAV is 145°, and the image sensor size is 13 mm × 17.

Image collection and analysis
The entire steps were processed in MATLAB.First, the histogram equalization was pre-processed by the histeq () command to enhance the contrast of the images.Next, the equalized RGB images obtained through the UAV were converted into HSV images by rgb2hsv() syntax, and the area of the citrus trees was separated from the ground using I PCA (principal component analysis index) (Eq.1).
In addition, color thresholding was applied to obtain the area of the citrus and obtain the corresponding image.The upper and lower HSV thresholds are (19, 31, 221) and (39, 243, 255), respectively (Eq. 2).
The tree area and citrus area overlapped, color was applied only to the areas overlapping with the tree, and the number of pixels was counted.The same process was repeated for all altitudes.
In order to use a vegetation index to detect fruits, VI methods (I PCA , CIVA, I 1 ) were applied to the original image (Eqs. 1, 3, 4) 37 .
(3) CIVA = 0.441R − 0.811G + 0.385B + 18.78, (4) Before the comparison, it confirmed that the data satisfied the t-test assumption; the data from 90 and 110 m flight altitudes did not satisfy it.Therefore, R software conducted the t-test for 30 m, 50 m, and 70 m and the Wilcoxon rank-sum tests for 90 m and 110 m.

Results
As shown in Fig. 2, the aerial photographs were collected at various altitudes (30 m, 50 m, 70 m, 90 m, and 110 m).The fruits may be identified by their small yellow spots with just the bare eyes.Figure 2A,B show a regular RGB image and an RGB image that has been histogram-equalized for comparative purposes.The characteristics of each altitude picture are shown in Table 1.The 110 m flying altitude image (5460 × 8192 pixels) comprises 0.22 hectares per image, with 2 pixels roughly equivalent to 1 cm 2 of objects.The image taken from a height of 30 m, in comparison, comprises 0.02 ha per image; to depict an item that is 1 cm 2 in size, one needs around 27 pixels.In other words, photographs taken from high altitudes include a lot of information from a large area.Unfortunately, a low-flight altitude image's narrow field of view prevents it from gathering much information from the region that was acquired, despite the fact that the image is still distinct and detailed.According to the data, a C. unshiu fruit requires 34 pixels for a picture taken from 110 m above sea level and 464 pixels from 30 m above.
The detail of the image affects fruit detection.Tables 2 and 3 represent the number of fruit pixels in the image and fruit estimation from each tree.Table 2 shows the results from a normal RGB image.At a low altitude, successfully distinguished and detected fruit from the tree.However, the high altitude fails to distinguish fruit from the tree.In the image from a flight height of 110 m, tree number 2 and 3 represent no fruit in the tree at analysis.Table 3 shows the results from the image after the histogram equalization.It can detect more fruits than a normal image; the detected fruit is increased by approximately 10%, and it can detect fruit from a 110 m altitude image, which fails to detect fruit in the normal version.Figure 3 displays the fruit detected in the images from each altitude.The image at a 30 m altitude showed more fruit than the images captured at other altitudes (Fig. 3A,B).Additionally, the histogram-equalized images detected more fruit than the untreated images at high altitudes such as 90 m and 110 m (Fig. 3A,B).
Applying histogram equalization to the image is statistically significant at lower flight altitudes (30 m and 50 m), while relatively high altitudes (70 m, 90 m, and 110 m) show no statistically significant.Table 4 shows that at the flight altitudes of 30 m and 50 m, the p-value is lower than 0.05 at the estimated fruit number.However, at the number of fruit pixels in the image, the image from a 30 m altitude is not significantly different between the normal and histogram-equalized images.
The results of additionally applied various vegetation indexes such as I PCA , CIVE, and I 1 at 30 m altitude are in Table 5.Also, the actual yield of citrus fruits from each tree is represented.In the case of actual yields, the fruits hidden by the leaf are added so that the number is larger than our estimate.Nevertheless, the estimates using the histogram equalized image are similar to the actual yields among the images used in this comparison.Also, using the VI I PCA image showed a similar estimation level to the histogram equalized image.In contrast, applying VI I 1 represents a gap from the actual yield that is inappropriate for estimating fruit yield.

Discussion
In recent years, UAVs have drawn a lot of attention to measuring secondary traits, such as plant height and spectral reflectance, in a large area.This is due to the benefits of UAVs, which include their ease of operation, highly flexible and timely control, super-high spatial resolution, and ability to quickly retrieve large amounts of field data due to a reduction in planning time.A UAV may be fitted with a variety of sensors, including multispectral and RGB cameras, which are valuable in agricultural applications.Also, the development of inexpensive UAVs and image sensors has made UAVs a hot topic in the realm of agricultural remote sensing.In particular, UAVs provide an entirely new perspective to the agricultural landscape by collecting remote sensing data at very low altitudes.The literature on the use of UAV image collection and analysis for ecological applications, natural resource monitoring, and agricultural management has exploded during the past 10 years [39][40][41][42] .There is a growing body of literature on the use of UAVs in agriculture, notably in the field of precision agriculture.UAV workflows are being added to agricultural management in order to precisely observe, measure, and monitor crop conditions throughout the growing season [43][44][45][46] estimate and measure crop yields 47,48 .The fundamental job of feature (or object) extraction is central to these requirements.Globally, the gathering of UAV photography for agricultural applications is growing, and more of these unique examples are required to create more standard procedures that will aid field and research managers in managing vast amounts of high-quality images.Ground sample distance (GSD), which varies depending on flight height and camera, may vary from a few centimeters 22  www.nature.com/scientificreports/ to considerably greater in UAV-collected pictures.The image sizes at these high resolutions will accumulate.
For instance, a 4-band picture mosaic of a 100 ha (250 acres) study area at 10 cm GSD may be only moderately large (e.g., 0.5 Gigabyte), but the entire data package, which includes all of the input photographs and the final photogrammetric outputs, may be as large as 5 Gb.Uavs are currently gaining popularity for monitoring purposes and their use in agriculture.Not only in agriculture but also in other crucial fields like power-line inspection, pipeline monitoring, construction, structural monitoring, etc., UAVs are becoming more and more common for monitoring 49 .The Dronfruit initiative was created under the context of the Andalucia Region's 2014-2020 Rural Development Program, with funding from the Agricultural European Innovation Partnership (EIP-AGRI).EIP-AGRI was established in 2021 to promote sustainable farming and forestry that produces more and better results with fewer resources.The primary objective of the Dronfruit project was to create a deep learning-based automated image processing system for identifying, counting, and estimating the size of citrus fruits on individual trees.Twenty trees from a commercial citrus plantation were tracked throughout the course of three yearly campaigns using pictures taken by UAV.Fruit sizes were measured during the hand harvest of these plants.When the estimated and actual yields per tree were compared, the approximation error was found to be SE = 4.53%, and the standard deviation was found to be SD = 0.97 kg.Comparisons were made between the actual total yield, the anticipated total yield, and the total yield calculated by a skilled technician.Whereas the model's mistakes were SE = 7.22% and SD = 4083.58kg, the technician's estimating error was SE = 13.74% 50.
In our study, the average number of fruits of a C. unshiu tress was 842 in August 2021.However, considering the current images were collected in May, which is earlier than normal data collection, and the fruit is detected from the top side, the data that detects more fruit would be close to the actual fruit yield.Therefore, the image from 30 m is more appropriate than the image from 110 m.Also, using the histogram equalized image might be more accurate than using data from the normal image.This study tried vegetation indices (VI) to detect fruits.
Vis might be more sensitive to finding fruits than using a histogram equalization image.The I PCA showed similar results to histogram equalization methods, but the I 1 is not fit for detecting citrus fruits.However, it is possible to roughly predict the number of hidden fruits by various correction coefficients.The results of this experiment confirmed that the sensitivity for finding fruit may vary depending on the type of VI, and the accuracy of yield prediction may also vary depending on post-processing.www.nature.com/scientificreports/ The algorithms for fruit recognition based on object detection have limitations since they cannot identify unseen fruits hidden by other fruits or vegetation 51 .Citrus are viewed based on their very changing color, texture, and shape.As a result, while the model cannot recognize all fruits, it can detect the majority of visible fruits.According to Gongal et al., the majority of research on fruit detection has attempted to count the number of fruits per tree as a way to estimate yields 52 .
Efficient and precise crop management requires using high-resolution optical imagery to identify, count, and track individual trees in agricultural settings.Monitoring tree growth, fruit production, and pest and disease occurrence is critical for effective crop management, making the automated delineation of individual trees a valuable tool for long-term management.With the increasing use of UAVs for agricultural applications, there is a growing need for standardized workflows that can help field and research managers effectively integrate large volumes of high-resolution imagery into their management operations.More individual cases are necessary to develop these workflows and improve crop management practices [53][54][55] .Using flying platforms like UAVs provides a step towards the adoption of this sort of model spanning ever greater regions and at a cheap cost with a system that generates modified results in this manner.Moreover, a new dataset with more photos may be created to provide a better result for yield production prediction.UAVs have recently been increasingly employed as cutting-edge remote sensing platforms for environmental applications 56 .In contrast to field-collected data, UAVs can easily fly over the target region to capture images with extremely high spatial (e.g., centimeters) and temporal (e.g., daily observations) resolutions, which significantly lowers labor and time expenses 57 .The ability to employ a sensor that can be customized on UAVs and the adaptability of altering UAV flying height and attitude can provide us with quick access to data with the spatial and spectral resolutions that customers want 58 .The image is provided with resolutions that are adequately chosen for in-depth observations of crop growth in the field, which is especially advantageous for precision agriculture.
This study developed an approach for computing citrus yield using UAV images.The approach is simple but strongly indicates that spectral mixture analysis should be considered when estimating yield, especially for images that clearly demonstrate the yield of unique spectral components.In order to investigate how resilient our methodology is to variations in climatic factors like temperature, humidity, precipitation, and wind speed, in our future study, we would like to test this method on crops planted in different places under varying weather circumstances.

Conclusions
The optimal flight altitude depends on the kind of sensor, sensor sensitivity, and the goal of the image used.For instance, a flight altitude of 70 m can enable the optical sensor to achieve a 3D resolution of centimeter-level 5 .However, in this case, 70 m of flight altitude cannot detect fruit accurately.Also, in the other study, the image's goal was to identify citrus trees; 104 m of the flight altitude was enough to identify citrus trees.However, it is not the appropriate flight height for estimating fruit yield.Considering the UAV image's goal and the image sensor's spec in the current study, the 30 m altitude is appropriate, and it might be lower flight height would be better.Using histogram equalization methods increases sensitivity to detect fruit in the images.Besides, it is possible that applying appropriate VIs to detect citrus fruit would be efficient.In this study, Ipca showed good estimation.However, the I 1 represents inaccurate estimation results.Also, these estimations would increase accuracy with the correlation coefficient.We can easily get data with the spatial and spectral resolutions required by consumers if we can utilize a sensor that can be customized on a UAV and alter the UAV's flight height and attitude.Precision agriculture benefits significantly from the image's appropriately selected resolutions for in-depth monitoring of crop growth in the field.In future research, we plan to build an accurate citrus yield prediction model using more UAV sample images, and these technologies and accumulated knowledge can be used not only for yield prediction but also for ground navigation using UAVs.
3 mm.The flight height of the UAV for taking the plant image was 30 m, 50 m, 70 m, 90 m, and 110 m.The image used in the current research was taken in 2021.5.7.

( 6 )
Estimate fruit number = Number of fruit pixel at each altitued image Average pixel per fruit at each altitude .

Figure 1 .
Figure 1.The relation of UAV image sensor size, focal length, field of view, and fling altitude.

Table 1 .
Image characteristics of RGB images from various UAV altitudes.
a Annual average citrus fruit size of 2021 (46.7 mm) was used for these values calculation as a constant.UAV flying altitude (m)Real area/image (ha/image) Pixels/

Table 2 .
Image analysis results from the normal RGB image.

Table 3 .
Image analysis results from applying histogram equalized image.

Table 4 .
T-test and Wilcoxon rank sum test results between normal images and histogram equalized image.
a T-test was used at 30 m, 50 m, and 70 m because the data satisfied the T-test assumption.The Wilcoxon rank-sum test was used for comparison at 90 m and 110 m data that did not satisfy the t-test assumption.bNS, nonsignificant at p > 0.05, *significant at 0.05, and **significant at 0.01.UAV flying altitude (

Table 5 .
Compare the actual yield of three citrus trees and estimate methods by various images (Normal RGB image, Histogram equalized image, VI I PCA , CIVE, and I 1 applied images).a Actual yield from each citrus tree.