A cost-effective canopy temperature measurement system for precision agriculture: a case study on sugar beet

Abstract. Easy-to-use data acquisition methods are required for variable-rate irrigation (VRI) decision support systems. Plant canopy temperature is related to plant water stress. Plant height is useful as an indicator of plant health conditions and can be used to estimate yield potential. Therefore, measurements of plant canopy temperature and plant height coupled with spatial information in the field can be used for determining VRI water application depths. A center-pivot-mounted wireless data acquisition (WDAQ) system was developed to collect plant canopy temperature and plant height data in the field. Each WDAQ unit consisted of a GPS receiver, programmable data logger, infrared temperature sensor, ultrasonic distance sensor, solar power supply, and wireless data transmitter/receiver. The system included two WDAQ units installed on a four-span center-pivot VRI system. One unit was mounted at the middle of the third span, and the other was mounted at the middle of the fourth span from the pivot. The infrared temperature sensors were used to detect the canopy temperature, while the ultrasonic distance sensors were used to measure plant height. The WDAQ system was designed to continuously and simultaneously measure plant canopy temperature and plant height and record the spatial coordinates at each measurement location as the center pivot moved around the field. Data collected were wirelessly transferred to a receiver for data processing. This WDAQ system has been tested and evaluated in the field for two years. Test results indicated that the WDAQ system was able to record approximately 3,200 measurements from each sensor in one pivot circle (360°). The measurement error of the ultrasonic distance sensor was 0.2 to 3 cm in a measurement range of 14 to 209 cm, and the sensor-measured plant heights were strongly correlated with manually tape-measured plant heights in soybean and cotton crops (r2 = 0.97). Combined with the spatial information, measurements of plant height and crop canopy temperature were used to generate plant height and crop canopy temperature maps. Spatial variabilities of plant height and canopy temperature across the field could be identified from the maps and used in irrigation research. The WDAQ system has great potential for automatic creation of VRI prescription maps and plant-based irrigation scheduling. Keywords: Canopy temperature, Irrigation scheduling, Plant height, Sensors, Variable-rate irrigation.

Within-field variations in sugar beet yield and quality and their correlation with environmental variables in the East of England

Curly top of sugar beet is a serious, yield-limiting disease in semiarid production areas caused by Beet curly top virus (BCTV) and transmitted by the beet leafhopper. One of the primary means of control for BCTV in sugar beet is host resistance but effectiveness of resistance can vary among BCTV strains. Strain prevalence among BCTV populations was last investigated in Idaho and Oregon during a 2006-to-2007 collection but changes in disease severity suggested a need for reevaluation. Therefore, 406 leaf samples symptomatic for curly top were collected from sugar beet plants in commercial sugar beet fields in Idaho and Oregon from 2012 to 2015. DNA was isolated and BCTV strain composition was investigated based on polymerase chain reaction assays with strain-specific primers for the Severe (Svr) and California/Logan (CA/Logan) strains and primers that amplified a group of Worland (Wor)-like strains. The BCTV strain distribution averaged 2% Svr, 30% CA/Logan, and 87% Wor-like (16% had mixed infections), which differed from the previously published 2006-to-2007 collection (87% Svr, 7% CA/Logan, and 60% Wor-like; 59% mixed infections) based on a contingency test (P < 0.0001). Whole-genome sequencing (GenBank accessions KT276895 to KT276920 and KX867015 to KX867057) with overlapping primers found that the Wor-like strains included Wor, Colorado and a previously undescribed strain designated Kimberly1. Results confirm a shift from Svr being one of the dominant BCTV strains in commercial sugar beet fields in 2006 to 2007 to becoming undetectable at times during recent years.

Accurate and timely assessment of plant stand density is crucial for modern crop production, directly impacting sugar beet yield and profitability. This study aims to develop and validate a highly accurate automated method for counting sugar beet seedlings using unmanned aerial vehicles (UAVs) and deep learning algorithms, optimizing both precision and processing speed. Field experiments were conducted in 2025 on commercial sugar beet fields in the Buzdyak district of the Republic of Bashkortostan. A DJI Phantom 4 Pro UAV equipped with an RGB camera captured aerial imagery from a 20-meter altitude. Initial vegetation segmentation employed the Excess Green (ExG) index, followed by binarization and morphological filtering. The YOLOv8n and YOLOv5m deep learning architectures, trained on a manually annotated dataset of aerial images, were then implemented for seedling detection and classification. Algorithm performance was rigorously evaluated against manual seedling counts on control plots. The YOLOv8n model demonstrated superior performance (Precision: 0.80; Recall: 0.70; AP50: 0.75; R²: 0.99), achieving a minimum relative error of 1.11% and a root mean squared error (RMSE) of 3.0. While YOLOv5m exhibited comparable correlation (R²: 0.98), it displayed lower recall and precision. The developed algorithm enables the generation of spatial distribution maps of seedlings, readily integrated into precision agriculture systems. This technology significantly reduces labor costs for seedling counting - by orders of magnitude compared to manual methods - while also eliminating subjective errors. The obtained results demonstrate the feasibility for industrial implementation, enabling rapid crop condition assessment, informed replanting decisions, and targeted site-specific agro-technological interventions. Future research will focus on expanding the algorithm to incorporate simultaneous weed mapping and adapting it for use with other crops.

The research and development of autonomous unmanned helicopters has lasted for more than one decade. Unmanned aerial vehicles (UAVs) are very useful for aerial photography, gas pollution detection, rescue or military applications. UAVs could potentially replace human beings in performing a variety of tedious or arduous tasks. Because of their ubiquitous uses, the theory and applications of UAVs systems have become popular contemporary research topics. There are many types of UAVs with different functions. Generally UAVs can be divided into two major categories, fixed-wing type and rotary-wing type. The fixed-wing UAVs can carry out long-distance and high-altitude reconnaissance missions. However, flight control of fixed-wing UAVs is not easy in low-altitude conditions. Conversely, rotary-wing UAVs can hover in low altitude while conducting surveys, photography or other investigations. Consequently in some applications, the rotary-wing type UAVs is more useful than the fixed-wing UAV. One common type of rotary-wing type UAVs is the AUH (Autonomous Unmanned Helicopter). AUHs have characteristics including of 6-DOF flight dynamics, VTOL (vertical taking-off and landing) and the ability to hover. These attributes make AUHs ideal for aerial photography or investigation in areas that limit maneuverability.

Millimeter-wave rotary-wing (RW) unmanned aerial vehicle (UAV) air-to-ground (A2G) links face unpredictable Doppler effect arising from the inevitable wobbling of RW UAV. Moreover, the time-varying channel characteristics during transmission lead to inaccurate channel estimation, which in turn results in the deteriorated bit error probability performance of the UAV A2G link. This paper studies the impact of mechanical wobbling on the Doppler effect of the millimeter-wave wireless channel between a hovering RW UAV and a ground node. Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link. Numerical results show that different RW UAV wobbling patterns impact the amplitude and the frequency of ACF oscillation in the millimeter-wave RW UAV A2G link. For UAV wobbling, the temporal ACF decreases quickly and the impact of the Doppler effect is significant on the millimeter-wave A2G link.

A crop water stress index for tall fescue ( Festuca arundinacea Schreb.) irrigation decision-making — a traditional method

In North America, wild populations of Beta vulgaris subsp. maritima, Beta macrocarpa, and respective hybrids with cultivated beet are found in California. These likely originated from contaminated seed imported from Europe (Biancardi et al., 2012). Section Beta includes the wild species B. macrocarpa, and B. v. ssp. maritima, and the cultivated sugar beet, Beta vulgaris subsp. vulgaris (Frese, 2010). Successful hybridization amongst species of section Beta varies. Sugar beet will readily crossfertilize with B. v. ssp. maritima, but there is conflicting evidence for successful hybridization between sugar beet and B. macrocarpa (de Bock, 1986; Bartsch and Ellstrand, 1999; Jung et al., 1993; Frese, 2010). The relationships among the three species of section Beta have been investigated with PCR-based marker and DNA sequencing techniques. Previous research suggests a close relationship between B. v. ssp. vulgaris and B. v. ssp. maritima with a more distant position of B. macrocarpa in the phylogenetic tree (Letschert, 1993; Shen et al., 1998; Villain, 2007). When commercial production areas are adjacent to wild beet populations, gene flow from cultivated beets has the potential to alter the genetic composition of the nearby wild populations (Bartsch and Ellstrand, 1999). Carsner reported populations of B. v. ssp. maritima, B. macrocarpa, and respective hybrids with cultivated beet in the Imperial Valley, California in 1938 and they continue to be identified in California. Plant and root characteristics of Imperial Valley wild beets were compared with collections of B. v. ssp. maritima and B. macrocarpa from European coastlines. The wild beets found in the Imperial Valley differ from typical B. v. ssp. maritima and other wild beets found in California and are most similar to B. macrocarpa (McFarlane, 1975). In 2011, plants were collected from wild Beta populations adjacent to commercial sugar beet fields and while many samples had clear morphological characteristics of B. macrocarpa, several showed B. v. ssp. maritima-like characteristics. This distinction is critical because B. v. ssp. maritima will readily cross hybridize with cultivated sugar beet while B. macrocarpa hybrids occur less frequently and often result in infertile progeny. Further research is needed to evaluate wild beets in the Imperial Valley to understand the origin of populations, determine the species, and explore whether or not gene flow occurs between these wild beets and cultivated beet. Herbarium samples, leaf tissue and seed of weed beet in and around commercial sugar beet fields were collected with the objectives of assigning taxonomy based on morphology and determining genetic variation by genotyping.

Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse

Thermal remote sensing of plant water stress in natural ecosystems

In recent years, rotary-wing unmanned aerial vehicles have been used in many areas. Rotary wing unmanned aerial vehicle (UAV) can carry different payloads according to their duties. For example; if they carry cameras, they are used for reconnaissance / surveillance, cargo if they carry cargo, agriculture if they carry pesticides, mapping if they carry an advanced camera and mapping system, and communication if they carry a base station or relay. Rotary-wing unmanned aerial vehicles are usually commanded to take off manually by a trained UAV operator. Before takeoff, the rotary-wing unmanned aerial vehicle is prepared for take-off by the UAV operator and this preparation takes approximately five minutes. It takes time for rotary-wing unmanned aerial vehicles to take off from the runway and reach their cruising speed, causing time loss in critical areas. A rotary-wing unmanned aerial vehicle launch assembly and a rotary-wing unmanned aerial vehicle with an opening mechanism that can open the thrust arms after launch and continue to fly can be the solution to this time loss. Rotary-wing drones capable of launching and without the intervention of the UAV operator will play an important role in emergency response and defense, where situational awareness is often required. For example; firefighters responding to fires can take advantage of the ability to quickly launch rotary-wing unmanned aerial vehicles from a stationary or moving fire truck. Thanks to the day / thermal camera on the launched rotary wing unmanned aerial vehicles, valuable information can be obtained about the progress of the fire and the damage caused by the fire. Thanks to rapid awareness, the fire can be intervened and fought faster. Similarly, military personnel can quickly deploy launchable rotary-wing drones for reconnaissance and surveillance and perform their duties. In order to be applicable to various types of missions, it is important that the rotary wing unmanned aerial vehicle be portable and low in volume. Since the launchable rotary-wing unmanned aerial vehicle proposed in the thesis has an arm release mechanism after launch, it can automatically open and generate thrust after launching its arms. In this way, it helps lower volume coverage before being launched. This also reduces air friction during launch. It can be deployed to autonomous systems effortlessly as it has closed package, mobile and self-arm management. Different mechanisms will be studied to create an efficient design. A mechanism that allows it to open its arms in a short time will be used in the self-arm-opening management design. Throwable rotary wing unmanned aerial vehicle and launch mechanism will be designed and 3D printers will be used for prototype.

The use of infrared thermometers (IR) to measure canopy temperatures for irrigation scheduling has been successfully applied in arid environments. Functionality of this technique in humid areas has been limited due to the presence of low vapor pressure deficits (VPD) and intermittent cloud cover. This study evaluated an alternate scheduling method for humid environments based on comparing measured canopy temperature with calculated canopy temperature of a well-watered crop. Irrigation was applied when the measured canopy temperature was greater than the predicted canopy temperature for more than three consecutive hours on two consecutive days. This method was evaluated against well-watered, semi-stressed, and dryland treatments of corn, soybean, and cotton on the basis of yield, irrigation amount, and irrigation water use efficiency (IWUE). Canopy temperature was underpredicted when the VPD was greater than 2 kPa. Limiting data to conditions when the solar radiation was greater than 200 W m-2 and the Richardson number was less than 0.2 resulted in very good prediction of canopy temperatures for cotton and soybean, particularly in the later growing period, but corn temperatures were consistently underpredicted. Although soybean and cotton yields were not significantly different across treatments, IWUE was improved for corn and cotton by use of this technique. Corn yield was greater for the well-watered crop, but the IR method resulted in 85% of the maximum yield while requiring less than 50% of the irrigation water. Results from this study suggest that the threshold temperature may be up to 1°C greater for corn and soybean and up to 0.5°C greater for cotton for humid compared to arid environments. This method shows potential as a tool for irrigation scheduling in humid environments. Further work is suggested to determine if conditions of excessive cloud cover and high VPD can be better accommodated, and to refine the threshold temperatures for corn, soybean, and cotton for humid environments.

The temperature stress day (TSD) has been used as a remotely sensed indicator of plant water stress without a thorough knowledge of TSD behavior. The purpose of this study was to demonstrate a dependence of the TSD on net radiation (Rm), air temperature (Ta), and atmospheric water vapor pressure deficit (VPD), and to evaluate a replacement water status index. The dependence on these parameters was proven by the following methods: (i) theoretically, using the Penman‐Monteith resistance equation; (ii) empirically, using the non‐water‐stressed baseline equation relating canopy and air temperatures to VPD; and (iii) in a field experiment with alfalfa [Medicago sativa (L.)] on an Anthropic Torrifluvent soil. The effect of these parameters caused the field‐measured TSD to vary up to 7 °C, rendering its utility as a plant stress indicator questionable. A modification of the crop water stress index (CWSI), which accounts for Rn, Ta, and VPD, was proposed as the TSD replacement. The modification consisted of using a measured instead of estimated well‐watered canopy temperature (Tcl) in the canopy temperature ratio defined by the CWSI. This modification obviates the need for the canopy resistance value required to calculate the theoretically based CWSI. The modified CWSI responded to imposed irrigation regimes as indicated through yield and evapotranspiration comparisons. The modified CWSI appears to be a suitable replacement for the TSD by accounting for environmental dependence while maintaining the measurement simplicity of the TSD.

In parallel with the rapid increase in technological developments, the use of unmanned aerial vehicles (UAVs) has increased intensively in both military and civilian areas. Unmanned Aerial vehicles (UAVs) have the capacity to perform different tasks under dangerous and difficult conditions for humans, in order to benefit human lives, by being controlled from an autonomous or remote ground station. Different missions and applications defined for them have revealed the classification of Unmanned Aerial Vehicles (UAVs). In this article, the UAV class, categorized as Rotary Wing, has been examined. UAVs may be exposed to internal and external disturbances while performing the task defined for them in line with the needs of people. As a result, many control methods have been applied to Rotary Wing UAVs, depending on the control technologies developed in order not to affect the flight quality of the Rotary Wing UAV negatively. One of them is the Interval Type-2 Fuzzy Logic Controllers presented in this article, known as the learning based intelligent control method. Firstly, for this study based on UAV control, different UAV classifications are presented. Later, the highly nonlinear dynamics of Rotary wing UAVs, which are a part of this classification, and control techniques in operating conditions under internal and external disturbances are mentioned. Interval type-2 fuzzy logic set and systems are presented in detail. Finally, many different studies on interval type-2 fuzzy logic control techniques applied to rotary wing UAVs under the framework of learning based intelligent control have been introduced.

&amp;lt;p&amp;gt;Magnetic mapping is commonly used in the academic and industrial sectors for a wide variety of objectives. To comply with a broad range of survey designs, the use of unmanned aerial vehicles (UAVs) has become frequent over the recent years. The majority of existing systems involves a magnetic acquisition equipment and its carrier (an UAV in this context) with no -or very few- connections between the two systems. Terremys is conceiving and optimizing UAVs specifically adapted for geophysical magnetic acquisitions together with the appropriate processing tools, and performs magnetic surveying in challenging environments. Terremys&amp;amp;#8217; &amp;amp;#8220;Q6&amp;amp;#8221; system weights 2.5 kg in air, including UAV &amp;amp; instrumentation, and allows 30 min swarm or individual flights.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Rotary-wing UAVs are found to be the most adaptive systems for a wide range of contexts and constraints (extensive range of flights heights even with steep slopes). They offer more flight flexibility than fixed-wing aircrafts. One of the major problems in the use of rotary-wings UAVs for magnetic mapping is the magnetic field generated by the aircraft itself on the measurements. Towing the magnetic sensor 2 to 5 m under the aircraft reduces data positioning accuracy and decreases the performances of the UAV, which can be critical for high-resolution surveys. To overcome these problems, a deployable 1 m long boom&amp;amp;#160;is rigidly attached to the UAV. The UAV magnetic signal can be divided between 1-the magnetic field of the whole equipment and 2-a low to high frequency magnetic field mostly originating from the motors. The magnetization of the system is the principal source of magnetic noise. It is modelled and corrected by calibration-compensation processes permitted by the use of three-component fluxgate magnetometers. The time-varying noise depends on the motors rotational speed and is minimized by optimizing the UAV components and characteristics along with the boom&amp;amp;#8217;s length.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The final set-up is able to acquire magnetic data with a precision of 1 to 5 nT at any height from 1 to 150 m above ground level. The high-precision magnetic measurements are coupled with a centimetric RTK navigation system to allow for high-resolution surveying. The quality of the obtained data is similar to that obtained with ground or aerial surveys with conventional carriers and matches industrial standards. Moreover, Terremys&amp;amp;#8217; systems merge in real-time data from all the aircraft instruments in order to integrate magnetic measurements, positioning information and all the UAV&amp;amp;#8217;s flight data (full telemetry) into a unique synchronized data file. This opens up many possibilities in terms of QA/QC, data processing and facilitates on-field workflows.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Case studies with diverse designs, flight altitudes and targets are presented to investigate the acquisition performances for different applications, as distinct as network positioning, archaeological prospecting or geological mapping.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The full integration of the magnetic sensor to the drone opens the possibility for implementation additional sensors to the system. The adjoining of other magnetic sensors would allow multi-sensors surveying and increases daily productivity. Diverse geophysical sensors can also be added, such as thermal/infrared cameras, spectrometers, radar/SAR.&amp;lt;/p&amp;gt;