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

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

Comparative Analysis of MCDA Techniques for Identifying Erosion-Prone Areas in the Burhanpur Watershed in Central India for the Purposes of Sustainable Watershed Management

The degradation of land and increasing water scarcity are existing challenges for agricultural sustainability, necessitating the implementation of improved soil-conservation practices at the watershed scale. The identification and selection of critical/prone areas based on erosion-governing criteria is essential and helps in the execution of the management process for determining priority. This study prioritizes erosion-prone sub-watersheds (alternatives) based on morphometric parameters (multiple criteria) via five Multi-Criteria Decision Analysis (MCDA) approaches, i.e., AHP: Analytical Hierarchy Process; TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; VIKOR: VIseKriterijumska Optimizacija I Kompromisno Resenje; SAW: Simple Additive Weighting; and CF: Compound Factor. Based on their priority score, 19 sub-watersheds were classified into four priority classes: low priority (0–0.25), moderate priority (0.25–0.50), high priority (0.50–0.75), and very high priority (0.75–1). The results revealed that about 8.34–30.15% area of the Burhanpur watershed is critically prone to erosion, followed by 23.38–52.05% area classed as high priority, 7.47–49.99% area classed as moderate priority, and 10.33–18.28% area classed as low priority. Additionally, four indices—percentage of changes (∆P), intensity of changes (∆I), the Spearman rank correlation coefficient test (SCCT), and the Kendall tau correlation coefficient test (KTCCT)—were employed to compare the models. This study confirms the efficacy of morphometric parameters for prioritizing sub-watersheds to preserve soil and the environment, particularly in areas for which limited information is available

Evaluation of IoT based smart drip irrigation and ETc based system for sweet corn

Smart irrigation is one form of precision irrigation that can help farmers maximize crop production while preserving water and energy. Many different types of smart irrigation system can be used, but there is no scientific consensus about which type is the finest. The purpose of this study was to compare various irrigation methods: an IoT-based soil moisture monitoring (IoT-SM) method using moisture sensors and an ET–based strategy. The best irrigation scheduling strategy should be determined by commercial production: keeping the soil water content near field capacity will result in a better fresh yield but not a more dry matter. Sweet corn was chosen as the crop under study. Growth characteristics (plant height, yield, and water productivity) were compared for each of the two approaches. For IoT based soil moisture monitoring method, two irrigation regimes were used: 43.5 per cent and 34.8 per cent (as field capacity (FC) of soil and 80% of FC, respectively) and crop evapotranspiration (ETc 100 per cent) for the ET-based method. The results showed that the IoT-SM 43.5 per cent could produce higher yields of about 12.05% and water savings by 11% compared to the ETc 100 per cent-based irrigation method. The developed IoT system was durable and water-resistant, allowing it to be deployed in outdoor agriculture. At the same time, a solar power supply eliminates the need for cabling and reduces sensor node maintenance

A fast radio burst with submillisecond quasi-periodic structure

Fast radio bursts (FRBs) are extragalactic radio transients of extraordinary luminosity. Studying the diverse temporal and spectral behaviour recently observed in a number of FRBs may help to determine the nature of the entire class. For example, a fast spinning or highly magnetised neutron star (NS) might generate the rotation-powered acceleration required to explain the bright emission. Periodic, subsecond components suggesting such rotation were recently reported in one FRB, and may also exist in two more. Here we report the discovery of FRB 20201020A with Apertif, an FRB that shows five components regularly spaced by 0.411 ms. This submillisecond structure in FRB 20201020A carries important clues about the progenitor of this FRB specifically, and potentially about the progenitors of FRBs in general. We therefore contrast its features to what is seen in other FRBs and pulsars, and to the predictions of some FRB models. We present a timing analysis of the FRB 20201020A components carried out in order to determine the significance of the periodicity. We compare these against the timing properties of the previously reported CHIME FRBs with subsecond quasi-periodic components, and against two Apertif bursts from repeating FRB 20180916B, which show complex time-frequency structure. We find the periodicity of FRB 20201020A to be marginally significant at 2.4σ. Its repeating subcomponents cannot be explained as pulsar rotation because the required spin rate of over 2 kHz exceeds the limits set by typical NS equations of state and observations. The fast periodicity is also in conflict with a compact object merger scenario. However, these quasi-periodic components could be caused by equidistant emitting regions in the magnetosphere of a magnetar. The submillisecond spacing of the components in FRB 20201020A, the smallest observed so far in a one-off FRB, may rule out both a NS spin period and binary mergers as the direct source of quasi-periodic FRB structure