Detection and Evaluation of Environmental Stress in Winter Wheat Using Remote and Proximal Sensing Methods and Vegetation Indices—A Review

Abstract

Climate change has a significant impact on winter wheat (Triticum aestivum L.) cultivation due to the occurrence of various environmental stress parameters. It destabilizes wheat production mainly through abiotic stresses (heat waves, drought, floods, frost, salinity, and nutrient deficiency) and improved conditions for pest and disease development and infestation as biotic parameters. The impact of these parameters can be reduced by timely and appropriate management measures such as irrigation, fertilization, or pesticide application. However, this requires the early diagnosis and quantification of the various stressors. Since they induce specific physiological responses in plant cells, structures, and tissues, environmental stress parameters can be monitored by different sensing methods, taking into account that these responses affect the signal in different regions of the electromagnetic spectrum (EM), especially visible (VIS), near infrared (NIR), and shortwave infrared (SWIR). This study reviews recent findings in the application of remote and proximal sensing methods for early detection and evaluation of abiotic and biotic stress parameters in crops, with an emphasis on winter wheat. The study first provides an overview of climate-change-induced stress parameters in winter wheat and their physiological responses. Second, the most promising non-invasive remote sensing methods are presented, such as airborne and satellite multispectral (VIS and NIR) and hyperspectral imaging, as well as proximal sensing methods using VNIR-SWIR spectroscopy. Third, data analysis methods using vegetation indices (VI), chemometrics, and various machine learning techniques are presented, as well as the main application areas of sensor-based analysis, namely, decision-making processes in precision agriculture.

Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties

“…Furthermore, results revealed that soil properties, particularly Ca, Mg, K, and clay, have a moderate positive correlation with wheat crop height. Similar findings from other studies have revealed that an abundance of soil chemical properties such as K, Mg, and Ca have an influence on the wheat crop height throughout the growth period [7,21,59]. Other studies also demonstrated that the clay content, silt%, and pH values are more significant factors influencing plant growth [8,104].…”

“…The influence of intra-field soil physiochemical properties' variation and meteorological conditions are key factors on crop development across various crop stages. Other detrimental effects on wheat growth include abiotic stresses such as droughts, frost, waterlogging, salinity, high temperatures, and other natural calamities [20,21]. The biotic factors, which include the infestation of diseases, competing weeds, and pests, are common challenges for crop development [22][23][24][25].…”

The impacts of temperature averages, variabilities and extremes on China’s winter wheat yield and its changing rate

“…This finding suggests that during the overwintering stage, characterized by the winter wheat's relatively strong cold resistance, a prolonged period of low temperature and multiple occurrences of extreme cold events accompanied by wintery precipitation can have a positive impact on winter wheat yield. This positive impact may be attributed to several factors, such as preventing premature transition to more cold-sensitive growth stages (Kosová et al 2008), facilitating the accumulation of dry matter in wheat, and enhancing resistance to pests and diseases (Guo and Huang 2009, Gaudet et al 2011, Vanková et al 2014, Skendžić et al 2023. In contrast, warm temperatures and an increased frequency of extreme heat events tend to have negative impacts on winter wheat yield.…”

Modelling of Intra-Field Winter Wheat Crop Growth Variability Using in Situ Measurements, Unmanned Aerial Vehicle-Derived Vegetation Indices, Soil Properties, and Machine Learning Algorithms

“…Soil physical and chemical properties including texture, phosphorus (P), nitrogen (N), potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), and pH influence crop growth. The essential soil physio-chemical properties for crops occur in low concentration levels within arid and semi-arid environments, which has a negative impact on crop growth [4]. Other factors that impede crop development include droughts, frost, waterlogging, salinity, high temperatures, diseases, weeds, and pests infestation [4].…”