Remote Sensing Identification and Rapid Yield Estimation of Pitaya Plants in Different Karst Mountainous Complex Habitats

Zhongfa Zhou, Ruiwen Peng (), Ruoshuang Li, Yiqiu Li, Denghong Huang and Meng Zhu
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Zhongfa Zhou: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Ruiwen Peng: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Ruoshuang Li: School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
Yiqiu Li: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Denghong Huang: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Meng Zhu: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China

Agriculture, 2023, vol. 13, issue 9, 1-22

Abstract: The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops’ yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions.

Keywords: Pitaya; unmanned aerial vehicle; color model; Canopy Height Model; deep learning model; plant yield estimation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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