Last generation instrument for agriculture multispectral data collection

In recent years, the acquisition and analysis of multispectral data are gaining a growing interest and importance in agriculture.  On the other hand, new technologies are opening up for the possibility of developing and implementing sensors with relatively small size and featuring high technical performances.  Thanks to low weights and high signal to noise ratios, such sensors can be transported by different types of means (terrestrial as well as aerial vehicles), giving new opportunities for assessment and monitoring of several crops at different growing stages or health conditions.  The choice and specialization of individual bands, within the electromagnetic spectrum ranging from the ultraviolet to the infrared, plays a fundamental role in the definition of the so-called vegetation indices (eg. NDVI, GNDVI, SAVI, and dozens of others), posing new questions and challenges in their effective implementation.  The present paper firstly discusses the needs of low-distance-based sensors for indices calculation and then focuses on development of a new multispectral instrument, namely MAIA, specially developed for agricultural multispectral analysis.  Such instrument features high frequency and high resolution imaging through nine different sensors (1 RGB and eight monochromes with relative band-pass filters, covering the range from 390 to 950 nm).  The instrument allows synchronized multiband imaging owing to integrated global shutter technology, with a frame rate up to 5 Hz, and the exposure time can be as low as 1/5000 s.  An applicative case study is eventually reported on an area featuring different materials (organic and non-organic), to show potential of the new instrument.

A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns

Frequent checks on livestock\u2019s body growth can help reducing problems related to cow infertility or other welfare implications, and recognizing health\u2019s anomalies. In the last ten years, optical methods have been proposed to extract information on various parameters while avoiding direct contact with animals\u2019 body, generally causes stress. This research aims to evaluate a new monitoring system, which is suitable to frequently check calves and cow\u2019s growth through a three-dimensional analysis of their bodies\u2019 portions. The innovative system is based on multiple acquisitions from a low cost Structured Light Depth-Camera (Microsoft Kinect\u2122 v1). The metrological performance of the instrument is proved through an uncertainty analysis and a proper calibration procedure. The paper reports application of the depth camera for extraction of different body parameters. Expanded uncertainty ranging between 3 and 15 mm is reported in the case of ten repeated measurements. Coef\ufb01cients of determination R2> 0.84 and deviations lower than 6% from manual measurements where in general detected in the case of head size, hips distance, withers to tail length, chest girth, hips, and withers height. Conversely, lower performances where recognized in the case of animal depth (R2 = 0.74) and back slope (R2 = 0.12)

Conservative precision agriculture: first economic and energetic assessments within the Agricare project

The integration of conservation tillage techniques with the principles of site-specific management characterising precision agriculture is an innovative feature aimed to achieve better economic and environmental sustainability, increasingly required by Community agricultural policies

Integrated approach for prediction of centrifugal fertilizer spread patterns

The present paper proposes a numerical approach for prediction of behavior of those fertilizers spreaders based on centrifugal disc functioning. In particular results from finite element multi-body simulations provided by commercial software are used in order to define boundary conditions of field tests carried out concurrently. Results are then integrated into a mathematical model to rapidly generate distribution charts and distribution diagrams. Such integrated approach can be implemented to effectively calibrate a theoretical model which provides simulations on different machine settings conditions: as a final point simulations allow fast testing of different distribution conditions, helping definition of those which minimize the variability of the distribution itself

Evaluation of the GreyWater Footprint Comparing the Indirect Effects of Different Agricultural Practices

Increasing global food demand and economic growth result in increasing competition over scarce freshwater resources, worsened by climate change and pollution. The agricultural sector has the largest share in the water footprint of humanity. While most studies focus on estimating water footprints (WFs) of crops through modeling, there are only few experimental field studies. The current work aims to understand the effect of supposedly better agricultural practices, particularly precision agriculture (variable rate application of fertilizers and pesticides) and conservation agriculture (minimum, strip, or no-tillage), on water deterioration and water pollution. We analyzed the results from an experimental field study in the northeast of Italy, in which four different crops are grown across three years of crops rotation. We compared minimum, strip, and no-tillage systems undergoing variable to uniform rate application. Grey WFs are assessed based on a field dataset using yield maps data, soil texture, and crop operations field. Leaching and associated grey WFs are assessed based on application rates and various environmental factors. Yields are measured in the field and recorded in a precision map. The results illustrate how precision agriculture combined with soil conservation tillage systems can reduce the grey water footprint by the 10%. We assessed the grey Water Footprint for all the field operation processes during the three-year crop rotation

Utilizing GIS tools to analyze viticultural choices under climate change scenario in North-East of Italy

Vineyard areas are constantly decreasing in Italy as well as in Europe. North-eastern regions in Italy are showing an opposite trend, steadily expanding with increased winegrowing areas. In viticulture and wine production, climate is arguably the most critical aspect in ripening fruit to achieve optimum characteristics to produce a given wine style. According to WMO and IPCC, climate is changing and the world is experiencing unprecedented climate extremes. Despite recent zoning aimed at defining key factors in determining the suitability of a given region for specific varieties and wine types, the expansion of viticulture in North East of Italy has led to some irrational planting choices about row orientation, dimensions, and slope. Under these conditions, the consequences of some extreme weather events may be more severe. The main objective of this study was to verify whether row orientation, aspect, and slope of vineyards, in combination with climate conditions, may affect yield and fruit quality. An area localized in the Northern Italy was analyzed, taking advantage of QGIS tools. The investigated parameters included: row orientation, slope, area, age of plantation, aspect ratio and distance between rows. Such variables have been combined with management information (planting distances, scion/rootstock combination, use of irrigation) and environmental information (yearly weather conditions). Data resulting from GIS analysis, vineyard management and environmental information have been correlated with 10-years yield and must quality parameters. Furthermore, satellite imagery from sample vineyards were collected and investigated in order to analyze the responses of the plants to different weather conditions. The results of the analysis highlighted how the mean slope of investigated vineyards is in general ranging between 1 and 3 degrees, with a prevalent Southern exposure. Rows do not exhibit a dominant orientation, mainly due to the following reasons: - the reduced dimensions available for vine cultivation, especially in hilly areas, where the vineyards are planted along contours, in order to limit erosion - the need for mechanisation, which calls for longer rather than larger rows. The results enabled to create a connection between row orientation, climate and soil conditions, and grapevine yield and quality responses to be considered as a guide for future planting choices more suitable to the restrictions imposed by increasing extreme weather events

On-barn pig weight estimation based on body measurements by structure-from-motion (SfM)

Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized

Determination of forest road surface roughness by kinect depth imaging

Roughness is a dynamic property of the gravel road surface that affects safety, ride comfort as well as vehicle tyre life and maintenance costs. A rapid survey of gravel road condition is fundamental for an effective maintenance planning and definition of the intervention priorities. Different non-contact techniques such as laser scanning, ultrasonic sensors and photogrammetry have recently been proposed to reconstruct three-dimensional topography of road surface and allow extraction of roughness metrics. The application of Microsoft Kinect\u2122 depth camera is proposed and discussed here for collection of 3D data sets from gravel roads, to be implemented in order to allow quantification of surface roughness. The objectives are to: i) verify the applicability of the Kinect sensor for characterization of different forest roads, ii) identify the appropriateness and potential of different roughness parameters and iii) analyse the correlation with vibrations recoded by 3-axis accelerometers installed on different vehicles. The test took advantage of the implementation of the Kinect depth camera for surface roughness determination of 4 different forest gravel roads and one well-maintained asphalt road as reference. Different vehicles (mountain bike, off-road motorcycle, ATV vehicle, 4WD car and compact crossover) were included in the experiment in order to verify the vibration intensity when travelling on different road surface conditions. Correlations between the extracted roughness parameters and vibration levels of the tested vehicles were then verified. Coefficients of determination of between 0.76 and 0.97 were detected between average surface roughness and standard deviation of relative accelerations, with higher values in the case of lighter vehicles

Spatial Variation of NO2 and Its Impact Factors in China: An Application of Sentinel-5P Products

As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due to the rapid economic growth. Compared with the traditional air pollution monitoring technology, the rapid development of the remote sensing monitoring method of atmospheric satellite has gradually become the critical technical means of global atmospheric environmental monitoring. To reveal the NO2 pollution situation in China, based on the latest NO2 products from Sentinel-5P TROPOMI, the spatial\u2013temporal characteristics and impact factors of troposphere NO2 column concentration of mainland China in the past year (February 2018 to January 2019) were analyzed on two administrative levels for the first time. Results show that the monthly fluctuation of tropospheric NO2 column concentration has obvious characteristics of \u201chigh in winter and low in summer\u201d, while the spatial distribution forms a \u201chigh in East and low in west\u201d pattern, bounded by Hu Line. The comparison of Coefficient of Variation (CV) and spatial autocorrelation models at two kinds of administrative scales indicates that although the spatial heterogeneity of NO2 column concentration is less affected by the observed scale, there is a \u201cdelayed effect\u201d of about one month in the process of NO2 column concentration fluctuation. Besides, the impact factors analysis based on Spatial Lag Model (SLM) and Geographic Weighted Regression (GWR) reveals that there is a positive correlation between nighttime light intensity, the secondary and tertiary industries proportion and NO2 column concentration. Furthermore, for regions with serious NO2 pollution in North China Plain, the whole society electricity consumption and vehicle ownership also play a positive role in increasing the NO2 column concentration. This study will enlighten the government and policy makers to formulate policies tailored to local conditions, to more effectively implement NO2 emission reduction and air pollution prevention