The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

Early crop disease detection is one of the most important tasks in plant protection. The purpose of this work was to evaluate the early wheat leaf rust detection possibility using hyperspectral remote sensing. The first task of the study was to choose tools for processing and analyze hyperspectral remote sensing data. The second task was to analyze the wheat leaf biochemical profile by chromatographic and spectrophotometric methods. The third task was to discuss a possible relationship between hyperspectral remote sensing data and the results from the wheat leaves, biochemical profile analysis. The work used an interdisciplinary approach, including hyperspectral remote sensing and data processing methods, as well as spectrophotometric and chromatographic methods. As a result, (1) the VIS-NIR spectrometry data analysis showed a high correlation with the hyperspectral remote sensing data; (2) the most important wavebands for disease identification were revealed (502, 466, 598, 718, 534, 766, 694, 650, 866, 602, 858 nm). An early disease detection accuracy of 97–100% was achieved from fourth dai (day/s after inoculation) using SVM.

Invasive weeds of the Asteraceae family are widespread in the world. Arbuscular mycorrhiza (AM) is one of the main factors contributing to the successful distribution of these species that is most clearly manifested in the subfamily Asteroideae. The benefits of plant-AMF symbiosis are most significant under unfavorable biotic and abiotic conditions. The specificity of the relationship between arbuscular mycorrhizal fungi (AMF) communities and plants and is determined at the presymbiotic stage. The AMF colonization level is higher in invasive species than in native ones, but AMF communities associated with Asteraceae invasive species are less diverse. AMF communities of Asteraceae invaders often include fewer common species (e.g., species belonging to Diversisporales). Invaders also reduce native AMF species richness in new areas. Arbuscular mycorrhizal fungi can form mycorrhizal networks that allow the redistribution of nutrients in plant communities. The most significant influence of AMF associated with invasive Asteraceae plants is seen in the formation of soil and rhizosphere microbiota, including the suppression of beneficial soil bacteria and fungi. This review could be useful in the development of practical recommendations for the use of AMF-based fertilizers.

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.