Abstract
Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture—in cooperation with image processing technologies—for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis.
Highlights
Image analysis approaches have been used for various purposes in agriculture such as vigor diagnosis and phenotyping
An example of results of the super-resolution with magnification scale 4 is shown in Figure 2, where low-resolution images generated by 5 different conventional methods are shown
At magnification scale 6, the classification accuracy was slightly improved (≥+0.03), but it was very much less than that at the smaller magnification scales. These results indicate that our approach recovers the information lost in low-resolution images and has a good influence on further image analysis, which has not been objectively evaluated in the previous similar studies
Summary
Image analysis approaches have been used for various purposes in agriculture such as vigor diagnosis and phenotyping. Some recent studies have attempted to detect tomato fruits including immature ones [1], to measure the internode length of tomato seedlings [2], to estimate yield in an apple orchard [3], to detect citrus fruit [4] and immature peach fruit [5], to measure the vegetation index of wheat [6], and to detect the flowering timing of paddy rice [7] under actual cultivation conditions These studies have developed many algorithms of image analysis to capture vigor information and plant phenotypes. The algorithms of image analysis are applied to the images captured by UAVs to assess the occurrence of potato disease [8], to map vegetation fraction in wheat fields [9], to detect weeds [10], to estimate leaf area index (LAI) [11], to monitor soil erosion [12] and water stress of crops [13], and to measure the height of sorghum plant [14,15]
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