Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms
Abstract
:1. Introduction
1.1. Related Works
1.2. Contributions of This Work
2. Materials and Methods
2.1. Study Area
2.2. Crop Pattern
2.3. Data Input and Processing
- (a)
- Field survey data and frequency
- (b)
- Sampling unit taken from satellite images
- (c)
- Satellite images used
- (d)
- Characteristics used for training and validation
- (e)
- Database
- (f)
- Classification method and algorithm used
- (g)
- Validation of the models on same crop cycle
- (h)
- Testing of the models in the next agricultural cycle
3. Results and Discussion
3.1. Results Obtained with the SVM Algorithm
3.2. Results Obtained with the BT Algorithm
3.3. Comparison of the Results Obtained with the Two Algorithms: SVM and BT
3.4. Test of the SVM Model in the Subsequent Cycle
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Author | Range of Images | Images | Training Samples | Classification Method | Within-Season Mapping |
---|---|---|---|---|---|
Martínez et al. [16] | multi-year | Landsat TM y ETM | Fields survey | Cross classification (IDRISI sotware) | No |
Zheng et al. [17] | Agricultural year | Landsat TM y ETM | Fields survey | SVM | No |
Conrad et al. [21] | Crop Cycle | SPOT 5 y ASTER | Expert knowledge | Rules of classification | No |
Saini and Ghosh [15] | Crop Cycle | Sentinel 2 | Fields Survey | RF and SVM | No |
Yang et al. [14] | Crop Cycle | SPOT 5 | Fields survey | MD, M-distance, MLE, SAM and SVM | No |
Hegarty-Craver et al. [22] | Crop Cycle | Image UAV, Sentinel 1 y Sentinel 2 | Fields survey | RF | No |
Prins and Van Niekerk [23] | Crop Cycle | Aerial Image, LIDAR image, Sentinel 2 | Crop type database | RF, DTs, XGBoost, k-NN, LR, NB, NN, d-NN, SVM-L, and SVM RBF | No |
Cai et al. [20] | Multi-year | Landsat TM y ETM | CDL (USDA) | d-NN | Yes |
Konduri et al. [24] | Multi-year | MODIS | CDL (USDA) | unsupervised classification (phenoregions) | Yes |
Lin et al. [19] | Multi-year | Sentinel 2, Landsat 8 | CDL (USDA) | RF | Yes |
Tran et al. [25] | Crop Cycle | Sentinel 2 | CDL (USDA) | RF | No |
Blickensdörfer et al. [26] | Multi-year | Sentinel 1 Sentinel 2 Landsat 8 | Land Parcel Information System (LPIS). | RF | No |
Defourny et al. [18] | Multi-year | Sentinel 2 | Ground truth data | RF | Yes |
Crop | March | April | May | June | July | August | September | October | ||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Bean | ||||||||||||||||||||||||||||||||
Corn | ||||||||||||||||||||||||||||||||
Alfalfa | ||||||||||||||||||||||||||||||||
Month | Available Images | Images Used | Image Acquisition Date | ||||||
---|---|---|---|---|---|---|---|---|---|
April | 6 | 6 | 2 | 7 | 12 | 17 | 22 | 27 | |
May | 6 | 6 | 2 | 7 | 12 | 17 | 22 | 27 | |
June | 6 | 4 | 1 | 6 | 11 | 16 | 21 | 26 | |
July | 7 | 4 | 1 | 6 | 11 | 16 | 21 | 26 | 31 |
August | 6 | 6 | 5 | 10 | 15 | 20 | 25 | 30 | |
September | 6 | 1 | 4 |
Combination | Number of Descriptors | Number of Images | Dates of Scenes Used | ||
---|---|---|---|---|---|
C1 | 33 | 3 | CD | 30 DB (6 PS) | 60 DB (12 PS) |
C2 | 22 | 2 | CD | 30 DB (6 PS) | |
C3 | 33 | 3 | CD | 15 DB (3 PS) | 30 DB (6 PS) |
C4 | 22 | 2 | CD | 15 DB (3 PS) |
Combination | Scenes Used | ||
---|---|---|---|
C1 | 6 July 2019 | 6 June 2019 | 7 May 2019 |
C2 | 6 July 2019 | 6 June 2019 | |
C3 | 6 July 2019 | 21 June 2019 | 6 June 2019 |
C4 | 6 July 2019 | 21 June 2019 |
Cultivation Type | Date of Analysis | Dates of Images or Scenes Included in the Database | ||
---|---|---|---|---|
corn 1 | 6 September | 4 September | 5 August | 6 July |
corn 1 | 5 August | 5 August | 6 July | 6 June |
corn 1 | 6 July | 6 July | 6 June | 7 May |
corn 1 | 6 June | 6 June | 7 May | 7 April |
corn 77 | 4 September | 4 September | 5 August | 6 July |
corn 77 | 5 August | 5 August | 6 July | 6 June |
corn 77 | 6 July | 6 July | 6 June | 7 May |
corn 77 | 6 June | 6 June | 7 May | 7 April |
Combination | Kappa Coefficient | Overall Accuracy % | Corn | Alfalfa | Bean | |||
---|---|---|---|---|---|---|---|---|
PA % | UA % | PA % | UA % | PA % | UA % | |||
C1 | 0.91 | 94.8 | 97.2 | 96.0 | 94.1 | 97.4 | 89.2 | 87.5 |
C2 | 0.89 | 93.4 | 95.8 | 95.1 | 94.6 | 96.0 | 85.2 | 84.9 |
C3 | 0.91 | 94.4 | 96.6 | 95.0 | 93.9 | 98.0 | 89.0 | 87.5 |
C4 | 0.86 | 91.5 | 94.3 | 93.3 | 90.5 | 97.4 | 85.1 | 80.4 |
Combination | Kappa Coefficient | Overall Accuracy % | Corn | Alfalfa | Bean | |||
---|---|---|---|---|---|---|---|---|
PA % | UA % | PA % | UA % | PA % | UA % | |||
C1 | 0.87 | 91.8 | 95.8 | 95.9 | 89.3 | 91.8 | 84.9 | 81.3 |
C2 | 0.79 | 87.4 | 94.2 | 90.4 | 87.9 | 88.3 | 67.6 | 75.9 |
C3 | 0.84 | 90.6 | 95.1 | 91.5 | 93.1 | 94.1 | 73.8 | 81.6 |
C4 | 0.74 | 84.9 | 93.0 | 85.2 | 89.6 | 90.9 | 57.8 | 74.5 |
3 scenes | C3 | C1 | ||||
SVM | BT | SVM | BT | |||
Kappa coefficient | 0.91 | 0.84 | 0.91 | 0.87 | ||
Global accuracy | 94.4% | 90.6% | 94.8% | 91.8% | ||
2 scenes | C4 | C2 | ||||
SVM | BT | SVM | BT | |||
0.86 | 0.74 | 0.89 | 0.79 | Kappa coefficient | ||
91.5% | 84.9% | 93.4% | 87.4% | Overall accuracy | ||
15 days | 30 days | 60 days |
Combination | Kappa Coefficient | Overall Accuracy % | Corn | Alfalfa | Bean | |||
---|---|---|---|---|---|---|---|---|
PA % | UA % | PA % | UA % | PA % | UA % | |||
C4 | 0.53 | 71.9 | 67.2 | 94.3 | 80.5 | 82.4 | 77.1 | 14.2 |
Combination | Kappa Coefficient | Overall Accuracy % | Corn | Alfalfa | Bean | |||
---|---|---|---|---|---|---|---|---|
PA % | UA % | PA % | UA % | PA % | UA % | |||
C2 | 0.68 | 83.1 | 81.7 | 95.5 | 89.3 | 76.2 | 57.1 | 30.8 |
C3 | 0.65 | 81.0 | 78.5 | 95.3 | 85.5 | 80.6 | 80.0 | 25.2 |
C4 | 0.63 | 80.0 | 77.7 | 95.0 | 83.3 | 80.3 | 82.5 | 24.3 |
Combination | Kappa Coefficient | Overall Accuracy % | Corn | Alfalfa | Bean | |||
---|---|---|---|---|---|---|---|---|
PA % | UA % | PA % | UA % | PA % | UA % | |||
C2 | 0.70 | 85.2 | 89.2 | 91.2 | 81.1 | 84.6 | 52.6 | 32.3 |
C3 | 0.72 | 86.2 | 86.8 | 95.1 | 88.6 | 79.0 | 60.5 | 40.4 |
C4 | 0.72 | 85.9 | 87.5 | 93.9 | 86.7 | 80.6 | 56.4 | 37.3 |
Combination | Kappa Coefficient | Overall Accuracy % | Corn | Alfalfa | Bean | |||
---|---|---|---|---|---|---|---|---|
PA % | UA % | PA % | UA % | PA % | UA % | |||
C1 | 0.70 | 84.4 | 82.6 | 95.5 | 90.5 | 79.5 | 62.1 | 28.6 |
C2 | 0.74 | 87.5 | 89.4 | 94.0 | 88.2 | 83.5 | 51.4 | 36.7 |
C3 | 0.73 | 86.8 | 86.9 | 94.9 | 89.7 | 80.0 | 61.8 | 41.2 |
C4 | 0.73 | 86.9 | 88.2 | 94.0 | 87.5 | 83.4 | 58.8 | 35.1 |
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Espinosa-Herrera, J.M.; Macedo-Cruz, A.; Fernández-Reynoso, D.S.; Flores-Magdaleno, H.; Fernández-Ordoñez, Y.M.; Soria-Ruíz, J. Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms. Sensors 2022, 22, 6106. https://doi.org/10.3390/s22166106
Espinosa-Herrera JM, Macedo-Cruz A, Fernández-Reynoso DS, Flores-Magdaleno H, Fernández-Ordoñez YM, Soria-Ruíz J. Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms. Sensors. 2022; 22(16):6106. https://doi.org/10.3390/s22166106
Chicago/Turabian StyleEspinosa-Herrera, José M., Antonia Macedo-Cruz, Demetrio S. Fernández-Reynoso, Héctor Flores-Magdaleno, Yolanda M. Fernández-Ordoñez, and Jesús Soria-Ruíz. 2022. "Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms" Sensors 22, no. 16: 6106. https://doi.org/10.3390/s22166106
APA StyleEspinosa-Herrera, J. M., Macedo-Cruz, A., Fernández-Reynoso, D. S., Flores-Magdaleno, H., Fernández-Ordoñez, Y. M., & Soria-Ruíz, J. (2022). Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms. Sensors, 22(16), 6106. https://doi.org/10.3390/s22166106