Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery
| dc.contributor.author | Chandel, Narendra S. | en |
| dc.contributor.author | Rajwade, Yogesh A. | en |
| dc.contributor.author | Dubey, Kumkum | en |
| dc.contributor.author | Chandel, Abhilash K. | en |
| dc.contributor.author | Subeesh, A. | en |
| dc.contributor.author | Tiwari, Mukesh K. | en |
| dc.date.accessioned | 2022-12-12T13:53:37Z | en |
| dc.date.available | 2022-12-12T13:53:37Z | en |
| dc.date.issued | 2022-12-02 | en |
| dc.date.updated | 2022-12-09T20:23:01Z | en |
| dc.description.abstract | Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ET<sub>c</sub>]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (T<sub>c</sub>), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest T<sub>c</sub> (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ET<sub>c</sub>, and highest T<sub>c</sub> (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ET<sub>c</sub>. The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, T<sub>c</sub>, and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Chandel, N.S.; Rajwade, Y.A.; Dubey, K.; Chandel, A.K.; Subeesh, A.; Tiwari, M.K. Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery. Plants 2022, 11, 3344. | en |
| dc.identifier.doi | https://doi.org/10.3390/plants11233344 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/112844 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | winter wheat | en |
| dc.subject | crop water stress | en |
| dc.subject | canopy temperature | en |
| dc.subject | computer vision | en |
| dc.subject | irrigation management | en |
| dc.title | Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery | en |
| dc.title.serial | Plants | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |