Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review
Abstract
:1. Introduction
2. Point Spectroscopy, RGB-, and Hyperspectral-Imaging
3. Machine Learning Algorithms
4. Applications for Weed/Crop Discrimination
4.1. Point Spectroscopy
4.2. RGB Imaging
4.3. Hyperspectral Imaging
5. Discussions
- The sensing system with selected optical filters should be continuously adjusted to improve the spectral image resolution and the detection accuracy.
- It is necessary to obtain accurate reference values of plant characteristics in samples collected for many years to improve the robustness of the training model.
- The simplified machine learning models should be further optimized to ensure its effectiveness for plant specific tasks.
- The final developed system should be robust enough to support automatic weed removal and handle various abnormal situations in a given task.
- More practical and feasibility studies on farmers’ fields should be carried out as crop-weed interaction is a very complex phenomenon.
- The potential cost of automatic weed control should be assessed and compared to conventional approaches in order to commercialize the technology.
6. Conclusions
Funding
Conflicts of Interest
References
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Crop | Weed | Wavelength Range (nm) | Feature Variable (nm) | Model | Optimal Accuracy | Reference |
---|---|---|---|---|---|---|
Sugarcane | Commelina benghalensis, Brachiaria brizantha, Brachiaria decumbens, Panicum maximum cv., Alternanthera tenella, Ipomoea hederifolia, Ipomoea purpurea, Ricinus communis L., Ageratum conyzoides, Crotalaria juncea, Stizolobium aterrimum | 400–2500 | 500–550, 650–750, 1300–1450, 1800–1900 | SIMCA, RF | 97% | [62] |
Maize | Silver beet | 635–785 | 635, 685, 785 | SVM | 97% | [63] |
Rocket salad | Groundsel | 2500–25000 | – | DA | 100% | [57] |
Cabbage | Barnyard grass, Green foxtail, Goosegrass, Crabgrass, Chenopodium quinoa | 350–2500 | 567, 667, 715, 1345, 1402, 1725, 1925, 2015 | Bayesian | 84.3% | [59] |
Maize, Barley, Wheat, Sugar beet | Barnyard grass, Wild oat, Blackgrass, Lambsquarters | 2500–25000 | – | CA | 100% | [58] |
Maize | Dchinochloa crasgalli, Echinochloa crusgalli | 350–2500 | – | SVM | 81.58% | [53] |
Wheat, Chickpea | Broadleaf weeds, Grass weeds | 700–1200 | 675, 715, 705, 745, 690, 875, 850, 1090, 750, 760, and 1070 for wheat; 675, 725, 705, 730, 690, 715, 685, and 680 for chickpe | GDA | 95% | [52] |
Maize | Panicum capillare, Digitaria ischaemum, Echinochloa crus-galli, Setaria glauca, Ambrosia artemisiifolia, Amaranthus retroflexus, Capsella bursa-pastoris, Chenopodium album | 400–760 | 400–425, 425–490 | PLSDA | >94.8% | [56] |
Cotton, Rice | Spine-greens, barnyard-grass | 350–2500 | 375, 465, 585, 705, 1035 | DA | 100% | [61] |
Soybean | Goose, Alligator alternanthera, Emarginate amaranth | 325–1075 | – | NN | 100% | [50] |
Maize | Panicum capillare, Digitaria ischaemum, Echinochloa crus-galli, Setaria glauca, Ambrosia artemisiifolia, Amaranthus retroflexus, Capsella bursa-pastoris | 400–760 | – | LDA | 94% | [64] |
Crop | Weed | Model | Optimal Accuracy | Reference |
---|---|---|---|---|
Tomato, Cotton | Black nightshade, Velvetleaf | CNN | 99.29% | [73] |
Cotton | Palmer amaranth, Red sprangletop | RF | 85.83% | [75] |
Barley | Canola, Wild radish | CNN, k-FLBPCM | 99% | [74] |
Maize, Peanut, Wheat | Chenopodium album, Humulus Scandens, Xanthium sibiricum Patrin ex Widder | CNN | 95.60% | [68] |
Blueberry | Goldenrod | CCMs | 94% | [87] |
Rice | Cyperus iric, L. chinensis | CNN | >94% | [69] |
Cotton | Feathertop, Sowthistle, Wild oats | CNN | 97% | [88] |
Soybean | Digitaria, Bindweed, Cephalanoplos | CNN | 92.89% | [72] |
Sugar beet | Turnip weed, Pigweed, Lambsquarters, Hare’s-ear mustard, Turnip weed | SVM, ANN | 96.67% | [43] |
Maize | Bindweeds, lamb’s quarters, Crabgrass | RF | 94.50% | [76] |
Maize, Wheat, Sugar beet | Black grass, Charlock, Cleavers, Chickweed, Fat hen, Loose silky-bent, Scentless mayweed, Shepherd’s purse, Cranesbill | CNN | 98.21% | [70] |
Sugarcane | Ipomea alba, Convolvulus arvinse, Cocciniagrandis, Trianthemaportulacastrum, Amaranthusviridis, Cyanotisaxillaris, Physalis minima, Comalinabengalensis, Cyperusrotundus | FRTC | 92.90% | [83] |
Soybean | Broadleaf weed, Grass | CNN | 97% | [71] |
Sugar beet | Pigweed, Lambsquarters, hare’s-ear mustard, Turnip weed | WTF | 96% | [86] |
Maize, Sunflower | Pigweed, Mustard, Bindweed, Saltwort | SVM | 91.50% | [15] |
Radish | Cocklebur, Lambs quarters, Morning glory, Velvetleaf | ANN | 95.10% | [77] |
Carrot | Ryegrass, Fat hen | NN | >75% | [89] |
Crop | Weed | Wavelength Range (nm) | Feature Variable (nm) | Model | Optimal Accuracy | Reference |
---|---|---|---|---|---|---|
Rice | Barnyard grass, weedy rice | 380–1080 | 415, 561, 687, 705, 735, 1007 | RF, SVM | 100% | [109] |
Maize | Convolvulus arvensis, Rumex, Cirsium arvense, Zea mays | 601–871 | 601, 636, 644, 669, 677, 764, 814, 871 | RF | 94% | [98] |
Maize | Caltrop, curly dock, baryardgrass, ipomoea spp., polymeria spp. | 391–887 | – | SVM, LDA | >98.35% | [117] |
Maize | Ranunculus repens, Cirsium arvense, Sinapis arvensis, Stellaria media, Tarraxacum officinale, Poa annua, Polygonum persicaria, Urtica dioica, Oxalis europaea, Medicago lupulina | 435–834 | 550, 580, 660, 830 | MOG, SOM | 100% | [20] |
Soybean, Cotton | Ryegrass | 400–900 | 565, 691, 735 | LDA | >90% | [91] |
Cotton | Palmer amaranth | 350–2500 | 705, 2025 | RF | 94.4% | [99] |
Cabbage | Goosegrass | 1000–2500 | – | SAM | 100% | [92] |
Pea, Canola, Wheat | Redroot pigweed, Wild oat | 400–1000 | 480, 550, 600, 670, 720, 840, 930 | ANN | 94% | [112] |
Maize | Ranunculus repens, Cirsium arvense, Sinapis arvensis, Stellaria media, Tarraxacum officinale, Poa annua, Poligonum persicaria, Urtica dioica, Oxalis europaea, Medicago lupulina | 435–855 | 539, 540, 542, 545, 549, 557, 565, 578, 585, 596, 605, 639, 675, 687, 703, 814, 840 | MOG, SOM | >96% | [118] |
Wheat | Musk thistle | 505–900 | – | SVM | 91% | [97] |
Wheat | Broadleaf weeds, Grass weeds | 400–850 | – | PLSDA | 85% | [96] |
Tomato | Solanum nigrum L., Amaranthus retroflexus L. | 384–810 | – | Bayesian | 95.9% | [11] |
Tomato | Black nightshade, pigweed | 400–795 | – | Bayesian | 95.8% | [101] |
Broad bean, wheat | Cruciferous weeds | 400–900 | 480, 485, 490, 520, 565, 585, 590, 595, 690, 720, 725, 730 | ANN | 100% | [113] |
Tomato | Black nightshade, Redroot pigweed | 384–810 | – | Bayesian | 92.2% | [119] |
Tomato | Black nightshade, Redroot pigweed | 400–795 | – | Bayesian | >90% | [103] |
Tomato | Black nightshade, Lambsquarter, Red-root pigweed, Purslane | 384–810 | – | Bayesian | 95% | [102] |
Lettuce | Groundsel, Shepherd’s-purse, Sowthistle | 384–810 | – | SSC | 90.3% | [106] |
Sugar beet | Wild buckwheat, Field horsetail, Green foxtail, Chickweed | 400–1000 | – | LDA | 97.3% | [23] |
Maize | Yellow Nutsedge, Barnyard grass, Crab grass, Canada thistle, Sow thistle, Redroot pigweed, Lamb’s quarter | 409–947 | – | SVM | 93% | [120] |
Soybean | Morningglory | 350–2500 | – | HMW | 100% | [95] |
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Su, W.-H. Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review. Smart Cities 2020, 3, 767-792. https://doi.org/10.3390/smartcities3030039
Su W-H. Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review. Smart Cities. 2020; 3(3):767-792. https://doi.org/10.3390/smartcities3030039
Chicago/Turabian StyleSu, Wen-Hao. 2020. "Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review" Smart Cities 3, no. 3: 767-792. https://doi.org/10.3390/smartcities3030039