کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1711077 | 1519532 | 2015 | 11 صفحه PDF | دانلود رایگان |
• The small hexacopter is an ideal tool for acquiring high resolution images.
• True colour images are useful to examine the plant biomass.
• Normalised Green–Red Difference Index (NGRDI) was correlated significantly with plant biomass.
• No correlation between NGRDI and leaf area index.
• NGRDI analysis allowed identification of the spatial pattern of biomass variability.
The use of unmanned aerial vehicles has been recently increasing in precision agriculture as an alternative to very costly and not readily available satellites or airborne sensors. Vegetation indices based solely on visible reflectance, which can be derived from true colour images may be a simple and cheap alternative compared to near infrared indices. A remote-controlled hexacopter with an RGB digital camera was tested for evaluating crop biomass. The hexacopter was flown over a field in which peas and oats were grown as sole crops and intercrops, fertilised with horse manure and yard–waste compost (10 t C ha−1). The images were taken at flowering stage. Based on the aerial photographs, the Normalised Green–Red Difference Index (NGRDI) was calculated, and related to aboveground biomass and leaf area index (LAI). The mean of NGRDI values ranged from 0.09 to 0.13 without any effect of cropping system, while the fertiliser significantly affected the yield and the corresponding NGRDI values. NGRDI values were positively and significantly correlated with the aboveground biomass (r = 0.58–0.78). A high autocorrelation of NGRDI, and thus biomass, was found within the treatment plots and used for block kriging to show the spatial variability in the field. No relationship was found between NGRDI and LAI in peas (P = 0.68) or oats (P = 0.15). Nevertheless, true colour images from a hexacopter and the derived NGRDI values are a cost-effective tool for biomass estimation and the establishment of yield variation maps for site-specific agricultural decision making.
Journal: Biosystems Engineering - Volume 129, January 2015, Pages 341–351