Idaho is popularly known for potatoes, but the state grows other specialty crops which include peaches. Numerous types of peaches are grown in the southwestern part of Idaho, which is warmer as compared to other regions. The state produces about 5300 tons of peaches [1
]. In addition to peaches, Idaho agriculture produces apples, pears, cherries, apricots, nectarines, plums and grapes. The specialty crop industry in Idaho is thriving. However, the industry is currently facing the challenges of labor shortage, increasing labor cost, and the pressure of a growing market. Because of these challenges, fruit growers need to adopt new technologies that can aid in optimizing crop production.
One of these new technologies, known as precision agriculture, is an agricultural management concept based on measuring crop variability in the field and responding to field issues [2
]. Crop variability has both temporal and spatial components that need to be considered. The spatial component is facilitated by the use of the global positioning system (GPS), which enables the farmer to locate the precise location in the field. In combination with advanced sensors that could measure field conditions such as moisture levels, nitrogen levels, and organic matter content, it allows the creation of maps that show the spatial variability of the field.
Although precision agriculture has been used mostly for row crops such as corn and wheat, studies have shown that the technology has been adopted for specialty crops which include fruit trees [3
]. One of the precision agriculture technologies that has been reported is remote sensing. Remote sensing can be implemented using a satellite or aerial system [4
]. The downsides of using satellites are the cost for real-time, high-resolution images and the frequency of data collection, which could affect the temporal aspect of crop production [5
]. Another remote sensing method is using aerial systems, which can be classified as manned or unmanned. Similar to satellites, a manned aerial system is costly, and it may not be economically feasible for smaller fields. However, with the proliferation of cheap commercial unmanned aerial systems (UAS) such as the 3DR Iris and DJI Phantom series (Figure 1
), remote sensing using unmanned aerial systems can be very promising for fruit growers with small acreages.
A number of researchers have used unmanned aerial systems for civilian applications which include power line detection, roadway traffic monitoring, wetland analysis, and agriculture. Li et al. [6
] developed an image processing algorithm for power line detection using Hough transform. A pulse-coupled neural filter was used to remove background noise before applying the Hough transform. Coifman et al. [7
] investigated the use of UASs to monitor roadway traffic to facilitate offline planning and real-time management applications. A feasibility study by Ro et al. [8
], which conducted a field experiment at a local interstate using UASs, concluded that UAS applications will become popular in the transportation area in the near future. The use of UAS photogrammetry provided a valuable and accurate enhancement to wetland delineation, classification, and health assessment [9
Another area that has received a lot of attention for UAS application is agriculture. One of the examples of the use of unmanned aerial systems (UASs) for fruit trees is the crop monitoring and assessment platform (C-MAP) developed at Northwest Nazarene University [10
]. The C-MAP is composed of an off-the-shelf UAS equipped with a multispectral camera. Figure 2
shows one of the C-MAP UASs flying over an experimental apple orchard with different watering methods, a drip and a sprinkler. An image processing algorithm was developed in this study to calculate the enhanced normalized difference vegetation index (ENDVI), which is a combination of the near-infrared band, green band, and blue band, and generated a false color image. The red color region has high ENDVI while the blue color region has the lowest ENDVI values. The false color image clearly shows the variability of the field caused by the difference in water input [11
In this paper, the application of CMAP is extended to the detection of blossoms of peaches using a customized image processing algorithm. It has been reported that there is an increase of photosynthetic activity during the bloom period, which correlates with the fruiting process [12
]. Peaches follow a linear pattern of crop development each year that allows the farmers to manage the fruit production and make sure that the crop is progressing as it should. In addition, farmers scout the orchard during the blooming season and use the observed amount of blooms with other parameters including crop density and the number of leaves on trees to predict yield. Early prediction of yield helps growers in marketing their products and in the packing operations [13
]. The objectives of this study are: (1) to expand the use of CMAP to detect peach blossoms; and (2) to develop an image processing algorithm to detect peach blossoms.
3. Results and Discussion
The results from the peach blossom detection algorithm showed that the blossoms were properly segmented from the raw multispectral image, with an average detection success rate of 84.3%. One of the reasons for the effective blossom detection is the use of the modified multispectral camera. With the modified filter of the camera, objects with high chlorophyll will have high reflectance in the near-infrared and green bands, but low reflectance in the blue band. In the image, the weeds have a red-brown hue because of the high chlorophyll content as compared with the other objects in the image. The colors of the peach blossoms are composed of a white and light pink hue. Some of the blossoms have a hue similar to that of the branch and some part of the ground. It can also be observed in Figure 7
that the light color of the blossom shows the high amount of near-infrared, green, blue values as compared to the weeds. Furthermore, the contrast stretching operation helped the thresholding process by increasing the separation of the pixel values between the blossoms and the ground specifically in the blue band. The contrast stretching did not affect the distribution in the near-infrared band. On the other hand, the morphological size filtering operation may have affected the detection success rate by removing small blossom pixels that were considered noise. However, the noise filtering operation was required to remove noise pixels.
Using the binary image of the blossom detection algorithm, the blossom density could be generally approximated by doing a series of calculations. Knowing the approximate height above the blossoms at which the pictures were taken, and having the images from the drone being flown over a known 2 m × 2 m square PVC pipe at that height, the density of the blossoms could be obtained. Processing this image as shown in Figure 10
, the number of square meters per pixel was found for that given height, which could then be applied to the binary peach blossom detection images, yielding an approximate density of the blossoms in square units. Using the flying height of 10 m, the size of the PVC square, and the image spatial resolution of 4000 × 3000 pixels, the approximate coverage area was 600 square meters. When the density of the peach blossoms was correlated to the square units, the result would not be perfect, but as long as the height of the images was consistent across all images, a correlation to fruit yield could be attempted.
Since the peach trees are planted at about 3 m intervals, the trees in the images were separated by creating a grid over the image and putting the trees in individual boxes. Figure 11
shows the result of this grid as well as the resulting peach segmentation over the image. The blossom density from each tree can then be estimated by doing a pixel count in each box.
Although such a process of tree segmentation could not be done for every image and would be very inaccurate, future work of this study will involve the detection of individual trees by way of boundaries. Using the boundaries, the blossom density of each tree would then be directly and accurately calculated. A blossom density map can then be produced, which could be used to aid yield estimation and other subsequent orchard management operations. The farmer could also use the blossom density map to provide a temporal analysis of the orchard blossoms.