The growing use of commercial unmanned aerial vehicles (UAV) and the need to adjust N fertilization rates in maize (Zea mays L.) currently constitute a key research issue. In this study, different ...multispectral vegetation indices (green-band and red-band based indices), SPAD and crop height (derived from a multispectral compact camera mounted on a UAV) were analysed to predict grain yield and determine whether an additional sidedress application of N fertilizer was required just before flowering. Seven different inorganic N rates (0, 100, 150, 200, 250, 300, 400 kg times N times ha-1), two different pig slurry manure rates (Ps) (150 or 250 kg times N times ha-1) and four different inorganic-organic N combinations (N100Ps150, N100Ps250, N200Ps150, N200Ps250) were applied to maize experimental plots. The spectral index that best explained final grain yield for the N treatments was the Wide Dynamic Range Vegetation Index (WDRVI). It identified a key threshold above/below 250-300 kg times N times ha-1. WDRVI, NDVI and crop height showed no significant response to extra N application at the economic optimum rate of fertilization (239.8 kg times N times ha-1), for which a grain yield of 16.12 Mg times ha-1 was obtained. This demonstrates their potential as yield predictors at V12 stage. Finally, a ranking of different vegetation indices and crop height is proposed to overcome the uncertainty associated with basing decisions on a single index.
The use of 3D sensors combined with appropriate data processing and analysis has provided tools to optimise agricultural management through the application of precision agriculture. The recent ...development of low-cost RGB-Depth cameras has presented an opportunity to introduce 3D sensors into the agricultural community. However, due to the sensitivity of these sensors to highly illuminated environments, it is necessary to know under which conditions RGB-D sensors are capable of operating. This work presents a methodology to evaluate the performance of RGB-D sensors under different lighting and distance conditions, considering both geometrical and spectral (colour and NIR) features. The methodology was applied to evaluate the performance of the Microsoft Kinect v2 sensor in an apple orchard. The results show that sensor resolution and precision decreased significantly under middle to high ambient illuminance (>2000 lx). However, this effect was minimised when measurements were conducted closer to the target. In contrast, illuminance levels below 50 lx affected the quality of colour data and may require the use of artificial lighting. The methodology was useful for characterizing sensor performance throughout the full range of ambient conditions in commercial orchards. Although Kinect v2 was originally developed for indoor conditions, it performed well under a range of outdoor conditions.
The leaf area index (LAI) is defined as the one-side leaf area per unit ground area, and is probably the most widely used index to characterize grapevine vigor. However, LAI varies spatially within ...vineyard plots. Mapping and quantifying this variability is very important for improving management decisions and agricultural practices. In this study, a mobile terrestrial laser scanner (MTLS) was used to map the LAI of a vineyard, and then to examine how different scanning methods (on-the-go or discontinuous systematic sampling) may affect the reliability of the resulting raster maps. The use of the MTLS allows calculating the enveloping vegetative area of the canopy, which is the sum of the leaf wall areas for both sides of the row (excluding gaps) and the projected upper area. Obtaining the enveloping areas requires scanning from both sides one meter length section along the row at each systematic sampling point. By converting the enveloping areas into LAI values, a raster map of the latter can be obtained by spatial interpolation (kriging). However, the user can opt for scanning on-the-go in a continuous way and compute 1-m LAI values along the rows, or instead, perform the scanning at discontinuous systematic sampling within the plot. An analysis of correlation between maps indicated that MTLS can be used discontinuously in specific sampling sections separated by up to 15 m along the rows. This capability significantly reduces the amount of data to be acquired at field level, the data storage capacity and the processing power of computers.
LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ...ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from
r
= 0.56 to
r
= 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.
In recent decades there has been an increasing use of remotely sensed data for precision agricultural purposes. Sericulture, the activity of rearing silkworm (Bombyx mori L.) larvae to produce silk ...in the form of cocoons, is an agricultural practice that has rarely used remote sensing techniques but that could benefit from them. The aim of this work was to investigate the possibility of using satellite imaging in order to monitor leaf harvesting in mulberry (Morus alba L.) plants cultivated for feeding silkworms; additionally, quantitative parameters on silk cocoon production were related to the analyses on vegetation indices. Adopting PlanetScope satellite images, four M. alba fields were monitored from the beginning of the silkworm rearing season until its end in 2020 and 2021. The results of our work showed that a decrease in the multispectral vegetation indices in the mulberry plots due to leaf harvesting was correlated with the different parameters of silk cocoons spun by silkworm larvae; in particular, a decrease in the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) had high correlations with quantitative silk cocoon production parameters (R2 values up to 0.56, p < 0.05). These results led us to the conclusion that precision agriculture can improve sericultural practice, offering interesting solutions for estimating the quantity of produced silk cocoons through the remote analysis of mulberry fields.
Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year ...study, multispectral VIs (green- and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12–VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0–30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYONr) (216.8 kg N ha−1), but underestimated the grain yield optimum N available (GYONa) (248.6 kg N ha−1). Red-based VIs slightly overestimated the GYONr and GYONa, while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EONr) and economic optimum nitrogen available (EONa) were determined below the GYONr and the GYONa, demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments.
One of the challenges in orchard management, in particular of hedgerow tree plantations, is the delineation of management zones on the bases of high-precision data. Along this line, the present study ...analyses the applicability of vegetation indices derived from UAV images to estimate the key structural and geometric canopy parameters of an almond orchard. In addition, the classes created on the basis of the vegetation indices were assessed to delineate potential management zones. The structural and geometric orchard parameters (width, height, cross-sectional area and porosity) were characterized by means of a LiDAR sensor, and the vegetation indices were derived from a UAV-acquired multispectral image. Both datasets summarized every 0.5 m along the almond tree rows and were used to interpolate continuous representations of the variables by means of geostatistical analysis. Linear and canonical correlation analyses were carried out to select the best performing vegetation index to estimate the structural and geometric orchard parameters in each cross-section of the tree rows. The results showed that NDVI averaged in each cross-section and normalized by its projected area achieved the highest correlations and served to define potential management zones. These findings expand the possibilities of using multispectral images in orchard management, particularly in hedgerow plantations.
•Ranked Set Sampling improves sampling efficiency in fruit growing compared to SRS.•High resolution agricultural sensors help to implement sampling techniques, like RSS.•UAV-based tree canopy ...projected area contributes efficiently to improve fruit load.•Higher image resolution improves the precision of fruit load estimates.
Fruit load estimation at plot level before harvest is a key issue in fruit growing. To face this challenge, two sampling methods to estimate fruit load in a peach tree orchard were compared: simple random sampling (SRS) and ranked set sampling (RSS). The study was carried out in a peach orchard (Prunus persica cv. ‘Platycarpa’) covering a total area of 2.24 ha. Having previously sampled the plot systematically to cover the entire area (104 individual trees or sampling points), both sampling methods (SRS and RSS) were tested by taking samples from this population with varying sample sizes from N = 4 to N = 12. Since RSS requires ancillary information to obtain the samples (ranking mechanism), several proximal and remote sensors already used or recently introduced in agriculture were assessed as data sources. A total of 14 variables provided by 5 different sensors and platforms were considered as potential ancillary variables. Among them, RGB images captured by an unmanned aerial vehicle (UAV), and used to estimate the canopy projected area of individual trees, proved to be the best of the options. This was shown by the high correlation (R = 0.85) between this area and the fruit load, providing RSS with the UAV-based canopy projected area the lowest Coefficient of Error (CE) for a given tree sample size. Then, comparing relative efficiency between random sampling (SRS) and RSS, the latter enables more precise fruit load estimates for any of the considered sample sizes. Interest and opportunity of RSS can be raised from two points of view. In terms of confidence, RSS managed to reduce the variance of fruit load estimates by about half compared to SRS. Sampling errors above the 10% threshold were always produced significantly fewer times using RSS, regardless of the sample size. In terms of operation within the plot, sample size could be reduced by 50%, from N = 10 for SRS to N = 5 for RSS, and this being expected sampling errors less than 10% in practically 70% of the samplings performed in both cases. In summary, fruit growers can take advantage of the combined use of appropriate data (RGB images from UAV) and RSS to optimize sample sizes and operational sampling costs in fruit growing.
To optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil ...purposive sampling are the two stages on which the proposal is based. Briefly, ECa data to wet bulb depth provided by the VERIS 3100 soil sensor were mapped before planting using block ordinary kriging. Looking for simplicity and practicality, only two ECa classes were delineated from the ECa map (k-means algorithm) to delimit two potential soil classes within the plot with possible different properties in terms of potential soil water content and/or soil water regime. Contrasting the difference between ECa classes (through discriminant analysis of soil properties at different systematic sampling locations), irrigation sectors were then designed in size and shape to match the previous soil zoning. Taking advantage of the points used for soil sampling, two of these locations were finally selected as candidates to install moisture sensors according to the purposive soil sampling theory. As these two spatial points are expectedly the most representative of each soil class, moisture information in these areas can be taken as a basis for better decision-making for vineyard irrigation management.
PDF
