Jordi Llorens

Ultrasonic and LIDAR Sensors for Electronic Canopy Characterization in Vineyards: Advances to Improve Pesticide Application Methods

Canopy characterization is a key factor to improve pesticide application methods in tree crops and vineyards. Development of quick, easy and efficient methods to determine the fundamental parameters used to characterize canopy structure is thus an important need. In this research the use of ultrasonic and LIDAR sensors have been compared with the traditional manual and destructive canopy measurement procedure. For both methods the values of key parameters such as crop height, crop width, crop volume or leaf area have been compared. Obtained results indicate that an ultrasonic sensor is an appropriate tool to determine the average canopy characteristics, while a LIDAR sensor provides more accuracy and detailed information about the canopy. Good correlations have been obtained between crop volume (CVU) values measured with ultrasonic sensors and leaf area index, LAI (R2 = 0.51). A good correlation has also been obtained between the canopy volume measured with ultrasonic and LIDAR sensors (R2 = 0.52). Laser measurements of crop height (CHL) allow one to accurately predict the canopy volume. The proposed new technologies seems very appropriate as complementary tools to improve the efficiency of pesticide applications, although further improvements are still needed.

Real-Time Tree-Foliage Surface Estimation Using a Ground Laser Scanner

The optimization of most pesticide and fertilizer applications is based on overall grove conditions. In this paper, we propose a measurement system to estimate the foliage surface of a tree crop. The system is based on a ground laser scanner that estimates the volume of the trees and then extrapolates their leaf area using simple and fast algorithms to allow true real-time operation. Tests with pear trees demonstrated that the relation between the volume and the foliage can be interpreted as linear with a coefficient of correlation (R) of 0.81, and the foliage surface can be estimated from this volume with an average error less than 6%.

Georeferenced LiDAR 3D vine plantation map generation.

The use of electronic devices for canopy characterization has recently been widely discussed. Among such devices, LiDAR sensors appear to be the most accurate and precise. Information obtained with LiDAR sensors during reading while driving a tractor along a crop row can be managed and transformed into canopy density maps by evaluating the frequency of LiDAR returns. This paper describes a proposed methodology to obtain a georeferenced canopy map by combining the information obtained with LiDAR with that generated using a GPS receiver installed on top of a tractor. Data regarding the velocity of LiDAR measurements and UTM coordinates of each measured point on the canopy were obtained by applying the proposed transformation process. The process allows overlap of the canopy density map generated with the image of the intended measured area using Google Earth®, providing accurate information about the canopy distribution and/or location of damage along the rows. This methodology was applied and tested on different vine varieties and crop stages in two important vine production areas in Spain. The results indicate that the georeferenced information obtained with LiDAR sensors appears to be an interesting tool with the potential to improve crop management processes.

Use of a Terrestrial LIDAR Sensor for Drift Detection in Vineyard Spraying

The use of a scanning Light Detection and Ranging (LIDAR) system to characterize drift during pesticide application is described. The LIDAR system is compared with an ad hoc test bench used to quantify the amount of spray liquid moving beyond the canopy. Two sprayers were used during the field test; a conventional mist blower at two air flow rates (27,507 and 34,959 m3·h−1) equipped with two different nozzle types (conventional and air injection) and a multi row sprayer with individually oriented air outlets. A simple model based on a linear function was used to predict spray deposit using LIDAR measurements and to compare with the deposits measured over the test bench. Results showed differences in the effectiveness of the LIDAR sensor depending on the sprayed droplet size (nozzle type) and air intensity. For conventional mist blower and low air flow rate; the sensor detects a greater number of drift drops obtaining a better correlation (r = 0.91; p < 0.01) than for the case of coarse droplets or high air flow rate. In the case of the multi row sprayer; drift deposition in the test bench was very poor. In general; the use of the LIDAR sensor presents an interesting and easy technique to establish the potential drift of a specific spray situation as an adequate alternative for the evaluation of drift potential.

Towards an optimized method of olive tree crown volume measurement

Accurate crown characterization of large isolated olive trees is vital for adjusting spray doses in three-dimensional crop agriculture. Among the many methodologies available, laser sensors have proved to be the most reliable and accurate. However, their operation is time consuming and requires specialist knowledge and so a simpler crown characterization method is required. To this end, three methods were evaluated and compared with LiDAR measurements to determine their accuracy: Vertical Crown Projected Area method (VCPA), Ellipsoid Volume method (VE) and Tree Silhouette Volume method (VTS). Trials were performed in three different kinds of olive tree plantations: intensive, adapted one-trunked traditional and traditional. In total, 55 trees were characterized. Results show that all three methods are appropriate to estimate the crown volume, reaching high coefficients of determination: R2 = 0.783, 0.843 and 0.824 for VCPA, VE and VTS, respectively. However, discrepancies arise when evaluating tree plantations separately, especially for traditional trees. Here, correlations between LiDAR volume and other parameters showed that the Mean Vector calculated for VCPA method showed the highest correlation for traditional trees, thus its use in traditional plantations is highly recommended.

Advanced Technologies for the Improvement of Spray Application Techniques in Spanish Viticulture: An Overview

Spraying techniques have been undergoing continuous evolution in recent decades. This paper presents part of the research work carried out in Spain in the field of sensors for characterizing vineyard canopies and monitoring spray drift in order to improve vineyard spraying and make it more sustainable. Some methods and geostatistical procedures for mapping vineyard parameters are proposed, and the development of a variable rate sprayer is described. All these technologies are interesting in terms of adjusting the amount of pesticides applied to the target canopy.

Electronic characterization of the phenological stages of grapevine using a LIDAR sensor

Canopy characteristics and their variation at all growth stages, together with the spatial distribution of plants in the field need to be considered in determining the application volume rate in fruit crops. Spray volume calculation in a vineyard needs to take into account concepts such as tree row volume (TRV), leaf wall area (LWA) and leaf area index (LAI). The objective of this work was to develop one protocol to characterize the canopy geometry of grapevine to determine BBCH stages using data sets measured manually and with LIDAR scanning. With this aim, a package called PROcess TO LIdar Data v.0.1 (PROTOLIDAR) was created in R environment to process the LIDAR scan information. Results showed a significant correlation between LIDAR impacts and LAI at each growth stage. The relationship between the estimated values of TRV or LWA, and the growth stage of the vine was statistically significant (R2=0.99 and R2=0.95, respectively). Finally, the geometry characterizations of the plants were represented in 2 or 3D maps in GRASS-GIS.

Kinect v2 Sensor-Based Mobile Terrestrial Laser Scanner for Agricultural Outdoor Applications

Mobile terrestrial laser scanners (MTLS), based on light detection and ranging sensors, are used worldwide in agricultural applications. MTLS are applied to characterize the geometry and the structure of plants and crops for technical and scientific purposes. Although MTLS exhibit outstanding performance, their high cost is still a drawback for most agricultural applications. This paper presents a low-cost alternative to MTLS based on the combination of a Kinect v2 depth sensor and a real time kinematic global navigation satellite system (GNSS) with extended color information capability. The theoretical foundations of this system are exposed along with some experimental results illustrating their performance and limitations. This study is focused on open-field agricultural applications, although most conclusions can also be extrapolated to similar outdoor uses. The developed Kinect-based MTLS system allows to select different acquisition frequencies and fields of view (FOV), from one to 512 vertical slices. The authors conclude that the better performance is obtained when a FOV of a single slice is used, but at the price of a very low measuring speed. With that particular configuration, plants, crops, and objects are reproduced accurately. Future efforts will be directed to increase the scanning efficiency by improving both the hardware and software components and to make it feasible using both partial and full FOV.

Testing the Suitability of a Terrestrial 2D LiDAR Scanner for Canopy Characterization of Greenhouse Tomato Crops

Canopy characterization is essential for pesticide dosage adjustment according to vegetation volume and density. It is especially important for fresh exportable vegetables like greenhouse tomatoes. These plants are thin and tall and are planted in pairs, which makes their characterization with electronic methods difficult. Therefore, the accuracy of the terrestrial 2D LiDAR sensor is evaluated for determining canopy parameters related to volume and density and established useful correlations between manual and electronic parameters for leaf area estimation. Experiments were performed in three commercial tomato greenhouses with a paired plantation system. In the electronic characterization, a LiDAR sensor scanned the plant pairs from both sides. The canopy height, canopy width, canopy volume, and leaf area were obtained. From these, other important parameters were calculated, like the tree row volume, leaf wall area, leaf area index, and leaf area density. Manual measurements were found to overestimate the parameters compared with the LiDAR sensor. The canopy volume estimated with the scanner was found to be reliable for estimating the canopy height, volume, and density. Moreover, the LiDAR scanner could assess the high variability in canopy density along rows and hence is an important tool for generating canopy maps.

Assessing the optimal liquid volume to be sprayed on isolated olive trees according to their canopy volumes

The application of pesticides to traditional and intensive olive orchards in Southern Spain has led to environmental problems. More specifically, the lack of an accurate, useful criterion to regulate the spray volume in relation to canopy characteristics has led to spray drift and runoff, which are threats to local ecosystems. The aim of this study was to determine the optimal relationship between canopy volume and the spray application volume, called specific spray volume, CV, through laboratory and field trials. In the laboratory trial, 6 specific spray volumes (0.05, 0.08, 0.10, 0.12, 0.15, and 0.20Lm(-3)) were tested in a specially designed structure containing small, live olive trees in order to simulate an intensive plantation system. The model aimed to evaluate the coverage of pesticide application on water sensitive paper (WSP) collectors. In the field trial, the three laboratory specific spray volumes that gave the best coverage values were tested on live, intensively managed trees, whose crown volume was manually measured. Food dye E-102 was used to determine the spray deposition on artificial targets (10×10cm absorbent paper pieces), and WSP was used to evaluate spray coverage. The spray penetration and deposit homogeneity inside the canopy were also evaluated. Weather conditions during the field trial were monitored with a weather station. The results of the laboratory trial showed that the three best specific spray volumes were 0.08, 0.10, and 0.12Lm(-3), resulting in mean coverage values of approximately 30%. The ANOVA of the field trial results showed that the 0.12Lm(-3) was the optimal specific spray volume for isolated olive trees. This specific spray volume gave the highest mean deposits, the best efficiency (as measured by the greatest normalized deposit), the most favourable penetration and homogeneity, and the highest coverage values.