Applications of Photonics in Agriculture Sector: A Review
Abstract
:1. Introduction
2. Classification of Photonics Systems in Agriculture
2.1. Imaging Technique
2.2. Spectroscopy Technique
2.2.1. Ultraviolet-Visible (UV-VIS) Spectroscopy
2.2.2. Fluorescence Spectroscopy
2.2.3. Infrared (IR) Spectroscopy
2.2.4. Near-Infrared (NIR) Spectroscopy
2.2.5. Mid-Infrared (MIR) Spectroscopy
2.2.6. Raman Spectroscopy
2.2.7. Additional Spectroscopy Techniques
2.2.8. Spectroscopy Processing and Analysis
2.3. Spectral Imaging Technique
2.3.1. Classes of Spectral Imaging
2.3.2. Spectral Image Acquisition Methods
2.3.3. Spectral Imaging Sensing Modes
2.3.4. Spectral Imaging System Construction
2.3.5. Spectral Imaging Processing and Analysis
2.3.6. Pros and Cons of Spectral Imaging
2.4. Technique Comparison
3. Optics and Photonics Applications in Agriculture
3.1. Applications of Imaging Technique
3.2. Applications of Spectroscopy Technique
3.3. Applications of Spectral Imaging Technique
4. Photonics Techniques Implementation in Food Safety Inspection and Quality Control
5. Photonics Techniques Implementation in Tropical Countries Agriculture
5.1. Implementation in Palm Oil-Related Activities
5.2. Implementation in Natural Rubber Related Activities
5.3. Implementation in Agro-Food Crops Related Activities
5.4. Possible Challenges
6. Conclusions
- The incorporation of optical sensors into photonics detection techniques that serve as an early warning for drinking water pollution.
- The characterization of canned food or bottled beverages in the NIR (>1100 nm) and MIR wavebands for their optical “fingerprint” that correlates to the quality and food safety level of the product, such as preservatives concentration.
- The characterization on hazardous residual materials in food using optical spectroscopy, Raman spectroscopy and fluorescence.
- The implementation of an agricultural robot to perform better palm oil plantation management, scheduled collection of field latex and weed removal.
- The spectral imaging provides early detection of disease-causing G. boninense in the oil palm.
- Spectroscopy provides moisture content inspection, protein and lipid content detection, as well as improving the rubber vulcanizing process.
- The imaging technique detects external damage or bruises on organic fruits and vegetables.
Author Contributions
Funding
Conflicts of Interest
References
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Wavelength (nm) | Wavenumber (cm−1) | Assignment |
---|---|---|
Water | ||
1454 | 6878 | 1st overtone O–H stretching |
1932 | 5176 | O–H combination |
Proteins | ||
1208 | 8278 | 2nd overtone C–H stretching |
1465 | 6826 | 1st overtone N–H and O–H stretching |
1734 | 5767 | 1st overtone C–H stretching |
1932 2058 2180 | 5176 4859 4587 | N–H combination and O–H stretching |
2302 2342 | 4344 4270 | C–H stretching combination |
Oil | ||
1210 | 8264 | 2nd overtone C–H stretching |
1406 | 7112 | 1st overtone N–H and O–H stretching |
1718 1760 | 5821 5682 | 1st overtone C–H stretching |
2114 | 4730 | N–H combination and O–H stretching |
2308 2346 | 4333 4263 | C–H stretching combination |
Starch | ||
1204 | 8306 | 2nd overtone C–H stretching |
1464 | 6831 | 1st overtone N–H and O–H stretching |
1932 2100 | 5176 4762 | N–H combination and O–H stretching |
2290 2324 | 4367 4303 | C–H stretching combination |
Wavelength (nm) | Wavenumber (cm−1) | Assignment |
---|---|---|
Water | ||
2.778–3.125 | 3200–3600 | O–H stretching |
6.061 | 1650 | H–OH stretching |
Proteins | ||
5.917–6.250 | 1600–1690 | Amide I (C=O stretching) |
6.349–6.757 | 1480–1575 | Amide II (C–N stretching and N–H bending) |
7.692–8.130 | 1230–1300 | Amide III (C–N stretching and N–H bending) |
Fats | ||
3.333–3.571 | 2800–3000 | C–H stretching |
5.731–5.797 | 1725–1745 | C=O stretching |
10.309 | 970 | C=C–H bending |
Carbohydrates | ||
3.333–3.571 | 2800–3000 | C–H stretching |
7.143–12.500 | 800–1400 | Skeletal stretching and bending |
Wavelength (nm) | Wavenumber (cm−1) | Assignment |
---|---|---|
Water | ||
2.778–3.125 | 3200–3600 | O–H stretching |
Proteins | ||
19.608 19.048 18.349 | 510 525 545 | S–S stretching |
14.925–15.873 13.423–14.286 | 630–670 700–745 | C–S stretching |
5.882–6.250 | 1600–1700 | Amide I (C=O stretching and N–H bending) |
8.032–8.097 | 1235–1245 | Amide III (C–N stretching and N–H bending) |
3.876–3.922 | 2550–2580 | S–H stretching |
3.333–3.571 | 2800–3000 | C–H stretching |
Fats | ||
6.940 | 1441 | CH2 bending |
6.863 | 1457 | CH3–CH2 bending |
6.039 | 1656 | C=C stretching |
3.378–3.503 | 2855–2960 | C–H stretching |
Carbohydrates | ||
11.962 | 836 | C–C stretching |
9.398 | 1064 | C–O stretching |
3.434 3.397 | 2912 2944 | C–H stretching |
2.898 | 3451 | O–H stretching |
Characteristics | Imaging | Spectroscopy | Spectral Imaging |
---|---|---|---|
Spectral information | × | ✓ | ✓ |
Spatial information | ✓ | × | ✓ |
Multi-constituent information | × | ✓ | ✓ |
Sensitivity to small-sized objects | ✓ | × | ✓ |
Flexibility of spectral extraction | × | × | ✓ |
Generation of quality-attribute distribution | × | × | ✓ |
Class | Product | Application | Ref. |
---|---|---|---|
Fruit | Apple | Bruise detection (thermal) | [66,67,70,78] |
Apple | Maturity evaluation (thermal) | [70] | |
Apple | Yield estimation (thermal) | [79] | |
Apple | Scab disease detection (thermal) | [68] | |
Green apple | Acquisition of segmented fruit region | [80] | |
Green apple and orange | Yield estimation | [61] | |
Orange | Texture analysis | [81] | |
Orange | Bruise detection (thermal) | [67] | |
Citrus | Water stress evaluation (thermal) | [82] | |
Pear | Maturity evaluation (thermal) | [71] | |
Banana | Maturity evaluation | [62] | |
Banana | Maturity evaluation | [63] | |
Persimmon | Maturity evaluation (thermal) | [71] | |
Passion fruit | Mass and volume estimation | [83] | |
Blueberry | Bruise detection | [56] | |
Grapevine | Pathogen detection (thermal) | [84] | |
Tomato | Fruit detection | [85,86] | |
Tomato | Bruise detection and maturity evaluation | [57] | |
Tomato | Bruise detection (thermal) | [87] | |
Tomato | Maturity evaluation (thermal) | [71] | |
Tomato | Clustered fruit detection | [88] | |
Sweet peppers | Peduncle detection | [89] | |
Onion | Post-harvest quality assessment (thermal) | [90] | |
Lettuce | Segmentation of vegetable | [91] | |
Cucumber | Downy mildew disease detection (thermal) | [69,92,93] | |
Grain | Rice leaf | Nitrogen content detection | [64] |
Wheat | Yield estimation (thermal) | [94,95,96] | |
Corn | Water stress evaluation (thermal) | [97] | |
Macadamia nuts | Yield estimation | [60] | |
Soybean | Identification of foliar disease | [98] | |
Soybean | Identification of leaf disease | [59] | |
Maize | Yield estimation (thermal) | [99] | |
Maize | Identification of leaf disease | [100] | |
Maize | Cultivar identification | [101] | |
Commercial | Cotton | Water stress evaluation (thermal) | [97,102] |
Silkworm | Gender identification | [103] | |
Farm and Plantation | Seed | Viability evaluation (thermal) | [104] |
Wheat field | Estimation of nutrient content | [65] | |
Cauliflower plantation | Weed detection | [73] | |
Asparagus plantation | Crop harvest robot vision | [74] | |
Sugar beet and rape plantation | Agriculture robot vision | [75] | |
Grapevines | Estimation of intra-parcel grape quantities | [105] | |
Cow farm | Behavioural studies | [76,106] | |
Goat and sheep farm | Animal species identification | [107] | |
Fish aquarium | Behavioural studies | [77,108] | |
Baby shrimp farm | Chlorine level detection | [109] | |
Orchid farm | Disease and pest detection | [58] | |
Surface and ground water | Chemical content detection | [110] |
Class | Product | Application | Method | Wavelength (nm) | Ref. |
---|---|---|---|---|---|
Fruit | Apple | Pigment content change during ripening | UV-VIS-NIR | 400–1000 | [111] |
Apple | Soluble solid content detection | VIS-NIR | 500–1100, 1000–2500 | [33] | |
Apple | Pesticide residue detection | Raman | 5–18 µm | [120] | |
Pear | Brown core and soluble solid content detection | UV-VIS-NIR | 200–1100 | [115] | |
Mango | Maturity evaluation | NIR | 1200–2200 | [126] | |
Peach | Peach variety identification | NIR | 833–2500 | [127] | |
Wax jambu | Quality inspection | NIR | 1000–2400 | [116] | |
Grape leaf | Water content estimation | UV-VIS-NIR | 350–2500 | [128] | |
Vegetable | Carrot | Carotenoid, fructose, glucose, sucrose and sugar content detection | NIR | 1108–2490 | [129] |
Potato | Bruise detection | UV-VIS-NIR | 250–1750 | [130] | |
Potato | Protein, fructose, glucose, starch and sucrose content detection | NIR | 1100–2500 | [113] | |
Onion | Soluble solid content detection | VIS-NIR | 500–1200 | [131] | |
Oilseed rape leaf | Aspartic acid content detection | NIR | 1100–2500 | [132] | |
Sugar beet seeds | Quality control | Time-domain spectroscopy | 250–350 GHz | [117] | |
Mushroom | Moisture content detection | VIS-NIR | 600–2200 | [112] | |
Grain | Corn seed | Viability evaluation | NIR Raman | 1000–2500 3.125–59 µm | [133] |
Almond | Internal defect detection | VIS-NIR | 700–1400 | [134,135] | |
Maize | Identification of transgenic ingredients | THz spectral | 0–4.5 THz | [136] | |
Rice, maize and peanut | Germination and growth of crop | UV-VIS FTIR | 380.85–796.62 nm 562.72–3865.11 cm−1 | [137] | |
Meat | Beef | Thermal change inspection | Fluorescence | 250–550 | [138] |
Beef | Adulteration detection | NIR-MIR | 2.5–19 µm | [118] | |
Frozen fish | Freshness evaluation | Fluorescence | 250–800 | [119] | |
Dairy | Egg | Contamination detection | UV-VIS-NIR | 200–860 | [139] |
Goat milk | Fatty acid content detection | VIS-NIR | 400–2498 | [140] | |
Oil | Edible oil | Stability analysis | NMR | 300 MHz (1H) | [141] |
Olive oil | Adulteration detection | Fluorescence | 250–720 | [142] | |
Ocimum essential oil | Antioxidant property identification | NIR-MIR | 2.5–18 µm | [143] | |
Beverage | Tea leaf | Tea polyphenol level detection | UV-VIS-NIR | 347–2506 | [144] |
Green tea leaf | Caffeine and catechins content detection | VIS-NIR | 400–2500 | [114] | |
Coffee | Geographic and genotypic origin identification | NIR | 1100–2498 | [145] | |
Coffee | Roasting degree and blend composition detection | NIR | 800–2857 | [146] | |
Tomato juice | Quality inspection | NIR-MIR | 2.5–14 µm | [147] | |
Apple wine | Volatile compound detection | NIR | 833–2500 | [148] | |
Rice wine | Fermentation monitoring | NIR-MIR | 2.5–25 µm | [149] | |
Commercial | Cotton fibre | Cotton type identification | NIR | 800–2500 | [150] |
Cotton fibre | Cotton fibre micronaire measurement | VIS-NIR | 400–2500 | [151] | |
Natural rubber | Protein and lipid content detection | NIR-MIR | 2.5–25 µm | [152] | |
Natural rubber | Chemical interaction during vulcanizing process | NIR-MIR Raman | 2.5–25 µm 3.125–100 µm, 6.25–50 µm | [153] | |
Natural rubber | Rubber silane reaction | NMR | 400 MHz (1H), 100.6 MHz (13C) | [154] | |
Natural rubber | Moisture content detection | VIS-NIR | 400–1100 | [155] | |
Natural rubber | Vulcanization system effect | Dielectric NMR | 10-1 < Hz < 107 20 MHz (1H) | [124] | |
Neem leaf | Pest control | UV-VIS FTIR XRD | 200–800 nm 250–4000 cm−1 10–80° | [156] | |
Farm and Plantation | Soil | Quality inspection | NIR | 780–5000 | [157] |
Soil | Nitrogen content detection | NIR | 800–2564 | [158] | |
Soil | Chemical and physical property estimation | NIR-MIR | 1430–2500, 2.5–27 µm | [159] | |
Soil | Nitrogen detection | NIR | 900–1700 | [122] | |
Soil | Nitrogen detection | NIR | 900–1700 | [123] | |
Soil and water | Contaminant detection | VIS-NIR | 400–2500 | [121] | |
Water hyacinth Soybean straw | Pollutant concentration detection Detection of biomass | Dielectric Fluorescence Near infrared spectroscopy | 10-1 < Hz < 106 N/A 4000–12,000 cm−1 | [125] [160] | |
Flower | Plant type identification | VIS | 635, 685, 785 | [161] |
Class | Product | Application | Method | Wavelength (nm) | Ref. |
---|---|---|---|---|---|
Fruit | Apple | Bruise detection | Hyper. line scan | 400–2500, 1000–2500 | [78,180] |
Apple | Bruise detection timing | Hyper. line scan | 400–2500 | [181] | |
Apple | Bruise detection | Multi. area scan | 740, 950 | [162] | |
Apple | Bruise and faeces detection | Multi. line scan | 530, 665, 750, 800 | [182] | |
Apple | Firmness evaluation | Multi. area scan | 680, 880, 905, 940 | [169] | |
Citrus | Canker detection | Multi. area scan | 730, 830 | [172] | |
Peach | Firmness evaluation | Hyper. line scan | 500–1000 | [183] | |
Peach | Maturity evaluation | Multi. area scan | 450, 675, 800 | [164] | |
Cantaloupe | Faeces detection | Hyper. line scan | 425–774 | [184] | |
Blueberry | Firmness evaluation, soluble solid content detection | Hyper. line scan | 400–1000 | [177,185] | |
Strawberry | Maturity evaluation | Hyper. line scan | 380–1030 874–1734 | [165] | |
Cherry | Pit detection | Hyper. line scan | 450–1000 | [186] | |
Grape | Quality evaluation | Hyper. line scan | 400–1000 | [170] | |
Banana | Maturity evaluation | Hyper. area scan | 500–700 | [168] | |
Tomato | Maturity evaluation | Hyper. line scan | 396–736 | [166] | |
Tomato | Maturity evaluation | Multi. area scan | 530, 595, 630, 850 | [167] | |
Cucumber | Chilling injury detection | Hyper. line scan | 447–951 | [187] | |
Vegetable | Freeze-dried broccoli | Glucosinolate detection | Hyper. line scan | 400–1700 | [188] |
Potato | Cooking time prediction | Hyper. line scan | 400–1000 | [189] | |
Onion | Sour skin disease detection | Hyper. area scan | 950–1650 | [173] | |
Mushroom | Bruise detection | Hyper. line scan | 400–1000 | [163] | |
Grain | Rice plant | Nitrogen content detection | Hyper. line scan | 400–1000 | [190,191] |
Thai jasmine rice | Rice variety identification | Multi. area scan | 545, 575 | [192] | |
Wheat | Fungus detection | Hyper. area scan | 1000-1600 | [193] | |
Wheat | Damage detection | Hyper. line scan | 1000–2500 | [194] | |
Peanut | Tomato spot wilt disease detection | Multi. Area scan | 475, 560, 668, 717, 840 | [195] | |
Corn | Oil and oleic acid content detection | Hyper. area scan | 950-1700 | [196] | |
Corn | Aflatoxin detection | Hyper. line scan | 400–600 | [197] | |
Meat | Chicken | Skin tumour detection | Hyper. line scan | 420–850 | [174] |
Chicken | Heart disease detection | Multi. area scan | 495, 535, 585, 605 | [198] | |
Chicken | Faeces detection | Multi. area scan | 520, 560 | [199] | |
Chicken | Wholesomeness inspection | Multi. line scan | 580, 620 | [200] | |
Beef | Tenderness evaluation | Hyper. line scan | 400–1000 | [201] | |
Beef | Microbial spoilage detection | Hyper. line scan | 400–1100 | [202] | |
Lamb | Lamb variety identification | Hyper. line scan | 900–1700 | [203] | |
Pork meat | E. coli detection | Hyper. line scan | 470–960 | [204] | |
Pork meat | Quality inspection | Hyper. line scan | 900–1700 | [205] | |
Fish | Moisture and fat content detection | Hyper. line scan | 460–1040 | [206] | |
Fish | Ridge detection | Hyper. line scan | 400–1000 | [207] | |
Salmon | Microbial spoilage detection | Hyper. line scan | 400–1000 880–1720 | [208] | |
Dehydrated prawn | Moisture content detection | Hyper. line scan | 380–1100 | [209] | |
Prawn | Adulteration detection | Hyper. line scan | 380–1030 900–1700 | [210] | |
Dairy | Milk powder | Melamine detection | Hyper. line scan | 990–1700 | [211] |
Milk | Fat content detection | Hyper. line scan | 530–900 | [178] | |
Milk | Melamine detection | Hyper. point scan | 4–98 µm | [212] | |
Oil | Olive oil | Free acidity, peroxide and moisture content detection | Hyper. line scan | 900–1700 | [179] |
Beverage | Tea | Quality inspection | Hyper. line scan | 408–1117 | [171] |
Tea | Moisture content detection | Hyper. line scan | 874–1734 | [213] | |
Tea | Tea variety identification | Multi. area scan | 580, 680, 800 | [214] | |
Farm and Plantation | Tea bush | Tea variety, growth status and disease identification | Hyper. area scan | 325–1075 | [175] |
Coffee crop | Detection of disease/infection | Hyper. area scan | 440–850 | [176] | |
Coffee plantation | Monitoring chlorophyll content | Multi. area scan | 490–2190 | [215] |
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Tan, J.Y.; Ker, P.J.; Lau, K.Y.; Hannan, M.A.; Tang, S.G.H. Applications of Photonics in Agriculture Sector: A Review. Molecules 2019, 24, 2025. https://doi.org/10.3390/molecules24102025
Tan JY, Ker PJ, Lau KY, Hannan MA, Tang SGH. Applications of Photonics in Agriculture Sector: A Review. Molecules. 2019; 24(10):2025. https://doi.org/10.3390/molecules24102025
Chicago/Turabian StyleTan, Jin Yeong, Pin Jern Ker, K. Y. Lau, M. A. Hannan, and Shirley Gee Hoon Tang. 2019. "Applications of Photonics in Agriculture Sector: A Review" Molecules 24, no. 10: 2025. https://doi.org/10.3390/molecules24102025
APA StyleTan, J. Y., Ker, P. J., Lau, K. Y., Hannan, M. A., & Tang, S. G. H. (2019). Applications of Photonics in Agriculture Sector: A Review. Molecules, 24(10), 2025. https://doi.org/10.3390/molecules24102025