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 | ||||||||||