Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery
Abstract
:1. Introduction
- Intensive or conventional tillage leaves less than 15% CRC or fewer than 500 pounds per acre (560 kg/ha) of crop residue. Intensive tillage disturbs all the soil by involving multiple operations with implements such as a moldboard, disk, or chisel plow.
- Strip-tillage merges the benefits of conventional tillage with the soil-protecting advantages of no-till by mixing up only the portion of the soil that contains the seed row (about one-third of the row width).
- No-till aims to achieve a 100% CRC, leaving most of the soil undisturbed. In this practice, the only disturbance between harvest and planting is nutrient injection (https://www.extension.purdue.edu/extmedia/ct/ct-1.html, accessed on 21 March 2024).
2. Study Region and Data
2.1. Study Region
2.2. Data
2.2.1. Field Measurements/Line-Point Transect Method
2.2.2. Global Positioning System (GPS) Survey Data
2.2.3. Unmanned Aircraft Systems (UAS) Imagery
3. Methods
3.1. K-Means Clustering Algorithm
- Elbow method: The elbow method assists in identifying the most appropriate number of clusters by analyzing the SSE graph. A sharp “elbow” or a significant drop in SSE values with a changing number of clusters suggests the optimal number of clusters [34]. This method systematically evaluates changes in SSE to pinpoint the best number of clusters in K-means clustering.
- Sum of squared error (SSE): In K-means clustering, each data point is assigned to a cluster based on its Euclidean distance from the cluster’s centroid . The SSE, a metric for clustering effectiveness [37], quantifies the variance within a cluster as the total squared difference between each data point and its cluster mean. An SSE of zero indicates perfect homogeneity within the cluster. Mathematically, SSE is defined as
- Silhouette score: The silhouette score measures how well a data point fits within its assigned cluster. It is calculated by comparing the mean intracluster distance (cohesion) to the mean nearest-cluster distance (separation) [38]. Scores range from −1 to 1, with values close to 1 indicating strong cluster fit, values near 0 suggesting overlapping clusters, and values around −1 highlighting misclassified data points. A high silhouette score denotes clear differentiation between clusters and tight grouping within them, whereas a low score may indicate clustering inaccuracies. The silhouette score is calculated asHere, is the silhouette score, represents cohesion, and indicates separation. The average silhouette score across all data points is then calculated for each K.
3.2. PCA-Otsu Method
3.2.1. Principal Component Analysis
3.2.2. Otsu Threshold
3.3. Validation
4. Results
4.1. Result of CRC Estimation Using K-Means Unsupervised Method
4.2. Result of CRC Estimation Using PCA-Otsu Method
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
UAS | Unmanned Aerial Systems |
CRC | Crop Residue Cover |
PCA | Principal Component Analysis |
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Field | Crop | Tillage Practice | Longitude | Latitude |
---|---|---|---|---|
Mckinnis North | Corn | Conventional | −86.985901 | 40.4813251 |
Mckinnis South | Corn | Conventional | −86.983845 | 40.4742143 |
Griner–Wag–Rus | Corn | Conventional | −86.961483 | 40.5629297 |
Field 57 | soybeans | Conventional | −86.999923 | 40.4892381 |
Field 69 | soybeans | Conventional | −87.000129 | 40.4895974 |
Chris 40 | soybeans | Conventional | −86.952211 | 40.541898 |
Field 267 East | Corn | Strip-till | −86.534623 | 41.7025976 |
Field 267 West | Corn | Strip-till | −86.533099 | 41.7030615 |
Field M1 | Corn | Strip-till | −85.15134 | 40.2488210 |
Field J | soybeans | Strip-till | −85.152896 | 40.2582106 |
Field 354 North | soybeans | Strip-till | −86.464354 | 41.7420796 |
Field 354 South | soybeans | Strip-till | −86.465298 | 41.7411566 |
County Line | Corn | No-till | −86.965141 | 40.5627906 |
Church East | Corn | No-till | −86.967117 | 40.5822479 |
Church West | Corn | No-till | −86.969163 | 40.5807252 |
Don 209 Top | soybeans | No-till | −86.961481 | 40.5751723 |
Don 209 Side | soybeans | No-till | −86.956971 | 40.5750181 |
Don 209 Bottom | soybeans | No-till | −86.962394 | 40.5700434 |
Field | First Visit (Date) | First Visit CRC | Second Visit (Date) | Second Visit CRC |
---|---|---|---|---|
Mckinnis North | 20 December 2021 | 43.25 | 27 April 2022 | 62.12 |
Mckinnis South | 20 December 2021 | 43.86 | 27 April 2022 | 64.37 |
Griner–Wag–Rus | 4 January 2022 | 56.75 | 12 May 2022 | 84.75 |
County Line | 4 January 2022 | 91.00 | 29 April 2022 | 94.37 |
Church East | 4 January 2022 | 89.87 | 12 May 2022 | 83.62 |
Church West | 4 January 2022 | 95.75 | 12 May 2022 | 82.37 |
Field 57 | 4 March 2022 | 47.25 | 27 April 2022 | 57.5 |
Chris 40 | 4 March 2022 | 86.00 | 29 April 2022 | 85.00 |
Field M1 | 3 April 2022 | 91.75 | 11 May 2022 | 65.5 |
Field J | 3 April 2022 | 73.87 | 11 May 2022 | 52.75 |
Field 69 | 10 April 2022 | 60.37 | 29 April 2022 | 67.00 |
Don 209 Top | 10 April 2022 | 87.12 | 19 May 2022 | 78 |
Don 209 Side | 10 April 2022 | 76.87 | 19 May 2022 | 79.25 |
Don 209 Bottom | 2 April 2022 | 88.00 | 19 May 2022 | 70.5 |
Field 354 North | 10 May 2022 | 82.75 | - | - |
Field 354 South | 10 May 2022 | 85.75 | - | - |
Field 267 North | 10 May 2022 | 95.12 | - | - |
Field 267 South | 10 May 2022 | 93.37 | - | - |
Number of Clusters | SSE | Variance in SSE () | Average Silhouette | Variance in Silhouette () |
---|---|---|---|---|
2 | 126,905,848 | 348 | 0.58 | 1.8 |
3 | 64,285,609 | 73 | 0.53 | 0.76 |
4 | 39,426,688 | 25 | 0.51 | 0.31 |
5 | 26,977,695 | 11 | 0.498 | 0.15 |
6 | 19,876,733 | 6.4 | 0.483 | 0.17 |
7 | 15,421,452 | 4 | 0.472 | 0.24 |
8 | 12,471,223 | 2.7 | 0.461 | 0.33 |
9 | 10,397,093 | 2 | 0.451 | 0.44 |
10 | 8,891,208 | 1.5 | 0.44 | 0.52 |
Eigen Value () | Eigen Vector | ||
---|---|---|---|
First Component | Second Component | Third Component | |
7.05 | 0.57 | 0.42 | −0.15 |
0.03 | 0.57 | 0.09 | 0.28 |
0.01 | 0.56 | −0.50 | −0.11 |
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Azimi, F.; Jung, J. Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery. Remote Sens. 2024, 16, 1135. https://doi.org/10.3390/rs16071135
Azimi F, Jung J. Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery. Remote Sensing. 2024; 16(7):1135. https://doi.org/10.3390/rs16071135
Chicago/Turabian StyleAzimi, Fatemeh, and Jinha Jung. 2024. "Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery" Remote Sensing 16, no. 7: 1135. https://doi.org/10.3390/rs16071135