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

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

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“…C. Ru et al (2020) explored the effectiveness of the Crop Water Stress Index, which relies on leaf temperature, as a tool for assessing the water status of grapevines. N. Chandel et al (2022) introduced a non-invasive approach utilizing computer vision and thermal-RGB imagery to detect water stress in winter wheat crops. Their method incorporated deep learning and function-approximation models to classify crops based on stress levels, utilizing thermal-RGB images and various input parameters.…”

Research on Red Jujubes Recognition Based on a Convolutional Neural Network

“…For instance, Xu et al [11] used the super green feature algorithm and the maximum between-class variance (OTSU) method for segmentation, observing a remarkable segmentation effect, and was able to achieve a recognition accuracy of 94.1% when the weeding robot was traveling at a speed of 1.6 km/h. Chandel et al [12] found that the combination of computer vision and thermal RGB images helped in the high-throughput mitigation and management of crop water stress. The method proposed by Khan et al [13] can be applied on a large scale to effectively map the crop types of smallholder farms at an early stage, enabling them to plan a seamless supply of food.…”

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