Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed.

Grasslands are one of the world’s largest ecosystems, accounting for 30% of total terrestrial biomass. Considering that aboveground biomass (AGB) is one of the most essential ecosystem services in grasslands, an accurate and faster method for estimating AGB is critical for managing, protecting, and promoting ecosystem sustainability. Unmanned aerial vehicles (UAVs) have emerged as a useful and practical tool for achieving this goal. Here, we review recent research studies that employ UAVs to estimate AGB in grassland ecosystems. We summarize different methods to establish a comprehensive workflow, from data collection in the field to data processing. For this purpose, 64 research articles were reviewed, focusing on several features including study site, grassland species composition, UAV platforms, flight parameters, sensors, field measurement, biomass indices, data processing, and analysis methods. The results demonstrate that there has been an increase in scientific research evaluating the use of UAVs in AGB estimation in grasslands during the period 2018–2022. Most of the studies were carried out in three countries (Germany, China, and USA), which indicates an urgent need for research in other locations where grassland ecosystems are abundant. We found RGB imaging was the most commonly used and is the most suitable for estimating AGB in grasslands at the moment, in terms of cost–benefit and data processing simplicity. In 50% of the studies, at least one vegetation index was used to estimate AGB; the Normalized Difference Vegetation Index (NDVI) was the most common. The most popular methods for data analysis were linear regression, partial least squares regression (PLSR), and random forest. Studies that used spectral and structural data showed that models incorporating both data types outperformed models utilizing only one. We also observed that research in this field has been limited both spatially and temporally. For example, only a small number of papers conducted studies over a number of years and in multiple places, suggesting that the protocols are not transferable to other locations and time points. Despite these limitations, and in the light of the rapid advances, we anticipate that UAV methods for AGB estimation in grasslands will continue improving and may become commercialized for farming applications in the near future.

Accurate estimation of biomass in grasslands is essential for understanding ecosystem health and productivity. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools for biomass estimation using canopy height models derived from high-resolution imagery. However, the impact of field disturbances, such as lodging and molehills, on the accuracy of biomass estimation using UAV-derived canopy height models remains underexplored. This study aimed to assess the relationship between UAV-derived canopy height and both reference canopy height measurements and dry biomass, accounting for different management systems and disturbance scenarios. UAV data were collected using a multispectral camera, and ground-based measurements were obtained for validation. The results revealed that UAV-derived canopy height models remained accurate in estimating vegetation height, even in the presence of disturbances. However, the relationship between UAV-derived canopy height and dry biomass was affected by disturbances, leading to overestimation or underestimation of biomass depending on disturbance type and severity. The impact of disturbances on biomass estimation varied across cutting systems. These findings highlight the potential of UAV-derived canopy height models for estimating vegetation structure, but also underscore the need for caution in relying solely on these models for accurate biomass estimation in heterogeneous grasslands. Future research should explore strategies to enhance biomass estimation accuracy by integrating additional data sources and accounting for field disturbances.

Grassland ecosystems play a vital role in biodiversity and carbon sequestration, but assessing these ecosystem services accurately is challenging due to their inherent spatial and temporal variability. Conventional field-based methods are often labor-intensive and may not capture this heterogeneity effectively. Recent progress in assessing grassland ecosystems has utilized a combination of structural and spectral data from Unmanned Aerial Vehicles (UAVs), showing promise for a thorough understanding of vegetation behavior. However, this method frequently overlooks an important factor — the horizontal variability within the vegetation, which significantly influences the precision of estimating plant characteristics, particularly in diverse ecosystems. Our study aims to fill this gap by incorporating texture analysis, a critical but often overlooked element in UAV-based assessments. Our research explored the potential of integrating various UAV-derived features to improve the estimation of above-ground biomass (AGB) and species richness in heterogeneous grasslands, key indicators of ecosystem health and productivity. This research investigated the efficacy of combining UAV-derived canopy height, multispectral data, and texture features for AGB and species richness estimation. The study was conducted in a heterogeneous wet grassland ecosystem, using a UAV equipped with multispectral sensors to capture high-resolution imagery. The imagery was processed to extract a range of features, including spectral indices, canopy height models, and textural information using Grey Level Co-occurrence Matrix methods. These features were then used to develop predictive models for AGB and species richness using advanced machine learning techniques, including Random Forest. Model performance was evaluated based on their predictive accuracy and ability to handle the spatial heterogeneity of grassland ecosystems. The study found that models integrating texture analysis with traditional spectral and structural data significantly improved predictive accuracy. For AGB estimation, the best models achieved an R² value of up to 0.84, with a relative root mean square error (rRMSE) of 26.58%. In predicting species richness, the most effective models reached an R² of 0.54 and a relative rRMSE of 31.95%. These results indicate an enhancement in estimation precision compared to models using traditional structural and spectral data types alone. This research demonstrated that UAV-based remote sensing, combined with a fusion of spectral, structural, and textural data, can improve the assessment of grassland characteristics such as AGB and species richness. The findings underscore the potential of integrated UAV-derived datasets in ecological monitoring and highlight the importance of advanced data processing and machine learning techniques in environmental research. This approach offers a promising avenue for more effective grassland management and conservation strategies, contributing to a deeper understanding of ecosystem dynamics.

Partitioning of biomass between roots and the above ground organs of crops is a key plant physiological processes that is closely linked to root growth and, thus, water and nutrient uptake. This makes investigations and knowledge about the partitioning of carbon between below and above ground plant organs important for accurately simulating water and carbon fluxes from croplands. Previous experiments have shown that carbon partitioning between root and shoot of crops could be altered by drought. However, most crop models do not explicitly consider the alteration of carbon partitioning caused by drought. This might partly be due to the difficulties in measuring the complete root biomass under field conditions and, thus, a lack of data on the field scale. Current methodologies such as soil coring and shovelomics are time-consuming and limited with regards to the measured depth, they do not necessarily capture the whole root biomass of deep rooting winter crops like winter wheat.The overall aim of the study is to improve our understanding of responses of below and above ground growth processes to different soil water availability. A field experiment has been conducted to investigate how drought stress affects the root: shoot ratio of different winter wheat cultivars under field conditions. A carbon partitioning subroutine, based on the sink strength principle and considering the direct effects of drought stress on carbon allocation, is implemented in the crop model SIMPLACE. The experimental data was used to test whether this newly developed model could successfully represent the effects of drought stress on biomass partitioning for different wheat cultivars.In the experiment, tubes with a diameter of 11 cm and a length of 1 m, filled with a sandy substrate and closed on the bottom with a fine mesh fleece that allows water to flow through but stops roots from growing through, were buried in 1m deep holes. Winter wheat was sown inside the tubes and the field around them to catch the whole plant biomass under canopy conditions. Half of the tubes were watered during the growth period, the other half were sheltered from rain during early growth stages. Root biomass and traits were investigated after harvesting the tubes. The data from this experiment was used to calibrate the carbon partitioning subroutine in the crop model under non-stressed and water-stressed conditions. The carbon partitioning subroutine calculates organ-specific potential daily growth rates. These growth rates are used to calculate the organ-specific sink strength, which can be affected by water stress and is used to define the amount of carbon distributed to each organ per day.The first experimental results show that water stress did affect the carbon partitioning between root and shoot biomass of winter wheat. The implemented model improved the simulation of biomass partitioning between root and above ground plant organs under drought conditions.