Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images

Zhao, Bingyu; Liu, Meiling; Wu, Jianjun; Liu, Xiangnan; Liu, Mengxue; Wu, Ling

doi:10.1109/access.2020.3043003

Cited by 7 publications

(1 citation statement)

References 69 publications

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“…Traditional techniques to detect diseases and pests in many crops rely on human scouting, which is time-consuming, expensive, and sometimes impractical or prone to human error. Modern PA techniques are based on the use of remote sensing platforms that can obtain images such as satellites [4][5][6][7], manned aircraft [8][9][10][11], UAVs [12][13][14][15], or combinations of them [16][17][18][19][20]. In this work, the use of satellite imagery has been ruled out due to the high costs and low availability of high-resolution imagery.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

Martinez-Heredia

Gálvez

Colodro

et al. 2023

Drones

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Drones can be very helpful in precision agriculture. Currently, most drone-based solutions for plant disease detection incorporate multispectral, hyperspectral, or thermal cameras, which are expensive. In addition, there is a trend nowadays to apply machine learning techniques to precision agriculture, which are computationally complex and intensive. In this work, we explore the feasibility of detecting ochre spot disease in almond plantations based on conventional techniques of computer vision and images from a very low-cost RGB camera that is placed on board a drone. Such an approach will allow the detection system to be simple and inexpensive. First, we made a study of color on the ochre spot disease. Second, we developed a specific algorithm that was capable of processing and analyzing limited-quality images from a very low-cost camera. In addition, it can estimate the percentage of healthy and unhealthy parts of the plant. Thanks to the GPS on board the drone, the system can provide the location of every sick almond tree. Third, we checked the operation of the algorithm with a variety of photographs of ochre spot disease in almonds. The study demonstrates that the efficiency of the algorithm depends to a great extent on environmental conditions, but, despite the limitations, the results obtained with the analyzed photographs show a maximum discrepancy of 10% between the estimated percentage and the ground truth percentage of the unhealthy area. This approach shows great potential for extension to other crops by making previous studies of color and adaptations.

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