Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Narendra S Chandel; Yogesh A Rajwade; Kumkum Dubey; Abhilash K Chandel; A Subeesh; Mukesh K Tiwari

doi:10.3390/plants11233344

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Plants (Basel). 2022 Dec 2;11(23):3344. doi: 10.3390/plants11233344.

Authors

Narendra S Chandel¹, Yogesh A Rajwade², Kumkum Dubey¹, Abhilash K Chandel^{3

4}, A Subeesh¹, Mukesh K Tiwari⁵

Affiliations

¹ Agricultural Mechanization Division, ICAR-Central Institute of Agricultural Engineering, Bhopal 462038, MP, India.
² Irrigation and Drainage Engineering Division, ICAR-Central Institute of Agricultural Engineering, Bhopal 462038, MP, India.
³ Department of Biological Systems Engineering, Virginia Tech Tidewater AREC, Suffolk, VA 23437, USA.
⁴ Center for Advanced Innovation in Agriculture (CAIA), Virginia Tech, Blacksburg, VA 24061, USA.
⁵ College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra 389001, GJ, India.

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_c]). 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_c), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest T_c (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ET_c, and highest T_c (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ET_c. 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_c, 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.

Keywords: canopy temperature; computer vision; crop water stress; irrigation management; winter wheat.

Grants and funding

824/Indian Council of Agricultural Research