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Table 3 The applications of multispectral and hyperspectral imaging in the study of fruit tree phenotypes

From: Phenotypic techniques and applications in fruit trees: a review

Applications Species Scale Spectral range Devices Detected parameters Evaluation parameters Advantages Limitations References
Indices Performance evaluation
Architecture parameters Olive orchard O B, G, R, red edge, NIR Tetracam mini-MCA-6 Canopy area   R2 = 0.94; RMSE = 1.44 m2 Suitable for the information acquisition of the whole orchard canopy High cost; difficult to accurately detect blade orientation [77]
Tree height R2 = 0.90; RMSE = 0.24 m
Cv R = 0.65
O IR Panasonic Lumix DMC-GF1 Tree height   R2 = 0.22 [75]
Crown diameter R2 = 0.58
Vineyard O G, R, NIR ADC-Snap Tree height NDVI   [73]
Pigment and nutrient contents Apple orchard O VIS, red edge, NIR Multispectral imager Chl NDVI R2 = 0.667; RMSE = 0.178 Wide spectral band range; real-time monitoring of a large area Not suitable for parameter determination of single blades [32]
Citrus orchard O 490–950 nm Mini-MCA12 Total N   R = 0.6469; RMSEP = 0.1296 [82]
Total soluble sugar   R = 0.6398; RMSEP = 8.8891
Starch   R = 0.6822; RMSEP = 14.9303
O 400–885 nm Micro-hyperspec VNIR model Chlorophyll fluorescence FLDn R2 = 0.72 [80]
Pear orchard O 550–810 nm Tetracam Micro-MCA Leaf%N M3CI R2 = 0.67; RMSE = 0.24 [83]
Vineyard O 515, 530, 570, 670, 700, 800 nm Multispectral sensor Carotenoid content R515/R570 R2 = 0.43 [78]
400–885 nm Micro-hyperspec VNIR model R515/R570 R2 = 0.48
R515/R570, TCARI/OSAVI R2 = 0.42; RMSE = 0.87
Biochemical parameters Mango orchard T 390.9–887.4 nm Resonon Pika II DM   R2 = 0.64 Quick detection; time saving; No need for chemical treatment Advanced image processing techniques are required [71]
T 390.9–887.4 nm Resonon Pika II Yield   R2 = 0.83 [88]
Vineyard T 400–1000 nm Resonon Pika L TSS   R2 = 0.91 [87]
Anthocyanin concentration R2 = 0.72
Diseases detection Avocado orchard O 390–520 nm; 470–570 nm; 670–750 nm Modified Canon Distinguish laurel wilt disease B/G   Suitable for disease detection over large scales; not influenced by the variation in agronomic characteristics Lack of ability to diagnose disease [94]
O 580, 650, 740, 750, 760, 850 nm Tetracam mini-MCA-6 Distinguish laurel wilt disease TCARI760–650   [95]
NIR/G
O 560, 660, 830 nm ADC Micro Identify white root rot disease NDVI Accuracy is 82% [96]
Olive orchard O 400–885 nm Micro-Hyperspec VNIR VW severity levels FLD3 Accuracy is 79.2% [92]
Almond orchard O 400–885 nm Micro-Hyperspec VNIR Red leaf blotch development FLD2   [93]
Chla+b
Carotenoid
  1. Note: In the “scale” column of the table, the fruit tree objects are divided into individual trees (T) and the whole orchard (O)