This study aimed to evaluate the use of multiple covariates in robust geostatistical modeling of soil chemical properties characterized by the presence of outliers. Different spatial prediction methods were compared using data from two agricultural areas located in Brazil´s Southeast: one with rotational grazing and one cultivated with sugarcane. Considering the variable-rate fertilizer prescription

Soil is one of the most important factors for agricultural production. In tropical regions, soil variability is considerable, with the most diverse combinations of physical and chemical characteristics, an influence factor in crop growth and productivity. In this research, the main objective was to identify how soil characteristics and parent material can influence sugarcane development over time using

Under toxic aluminum (Al) levels in the soil, wheat (Triticum aestivum L.) suffers stress and plant growth is affected. A method for diagnosis of plants is proposed that includes the following as a strategy: to analyze total Al in the soil, employ satellite radar imagery and calculate a vegetation index. The objective of this research, conducted at the field scale, was to explore how radar backscattering

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that

Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded

Wide assimilation of precision agriculture among farmers is currently dependent on the ability to demonstrate its efficiency at the field-scale. Yet, most experiments that compare variable-rate vs uniform application (VRA and UA) are performed in strips, concentrated in a small portion of the field with limited extrapolation to the field scale. A spatiotemporal normalized ratio (STNR) methodology is

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological

Digital technologies are a promising means to tackle the increasing global challenges (e.g., climate change, water pollution, soil degradation) and revolutionising agricultural production. The current research used a two-stage Delphi study with 34 experts from various domains, including production, advisory and research, to identify the key drivers and barriers, the most promising technologies and

This research study focuses on the design of variable rate liquid fertilizer applicator which can measure the NPK status of soil and applies N, P and K nutrients simultaneously at separate variable rates around the oil palm trees in the form of aqueous solutions of straight fertilizers having single nutrient. A fertilizer flow control and spray system was designed to apply liquid fertilizer around

A growing and promising sector of precision agriculture is the site-specific application of pesticides, having a high potential for reductions in pesticide use. Within the research project ‘AssSys’, site-specific pesticide applications with a direct injection sprayer system and an automatic application assistant were evaluated economically with respect to herbicide and fungicide applications. The application

Citrus fruits do not ripen at the same time in natural environments and exhibit different maturity stages on trees, hence it is necessary to realize selective harvesting of citrus picking robots. The visual attention mechanism reveals a physiological phenomenon that human eyes usually focus on a region that is salient from its surround. The degree to which a region contrasts with its surround is called

Federal conservation programs can target conservation assistance to the most vulnerable lands by stimulating the adoption of innovative technology including precision agriculture. Productivity and vulnerability vary within fields suggesting conservation programs could be targeted to marginal field regions potentially increasing the whole-field net revenue. Decision-support tools (DST) have been proposed

The nutritional and productive attributes of Arabica coffee (Coffea arabica L.) can vary spatially within cultivated areas. Precision farming techniques applied to coffee plantations can diagnose this spatial variability and propose solutions to correct this unevenness. The objective of this study was to characterize the distribution and spatial dependence of leaf macronutrient concentration and fruit

Crop models are useful tools because they can help understand many complex processes by simulating them. They are mainly designed at a specific spatial scale, the field. But with the new spatial data being made available in modern agriculture, they are being more and more applied at multiple and changing scales. These applications range from typically at broader scales, to perform regional or national

Traditional pest control approaches rely mostly on the experience of farmers, which may not be effective due to lack of scientific information regarding the environment where crops grow. Farmers can initiate a more effective integrated pest management program when precise and quantified results of forecasting pest population outbreaks are provided. Previous studies generally utilize long-term data

Quantification of interactions of soil conditions, plant available water and weather conditions on crop development and production is the key for optimizing field management to achieve optimal production. The goal of this study was to quantify the effects of soil and weather conditions on cotton development and production using temporal aerial imagery data, weather and soil apparent electrical conductivity

Kriging has been shown to be the best interpolator to interpolate maps in precision agriculture. However, Kriging requires a high number of sampling points to generate accurate maps. Recently, machine learning (ML) techniques have shown the potential to produce maps with a lower number of sampling points. In addition, using ML map generation can be automated and use much more feature information to

Ratoon rice production has been an emerging cropping system to increase food quality and productivity worldwide. Efficient monitoring of ratoon rice aboveground biomass (AGB) over large areas is valuable for precision agriculture, as AGB is closely related to crop grain yield and quality. Unmanned aerial vehicle (UAV) remote sensing has opened an unprecedented opportunity to efficiently monitor crop

With the rapid advances in precision agriculture technology, machine vision is emerging as a means to obtain accurate spatial information for a control system at relatively attractive cost. Besides being affordable and data rich, vision sensors offer precise local or relative information, which can complement global sensors such as global navigation satellite systems (GNSS). The research reported on

Reducing the decision-making unit to classes within fields can improve yields, efficiency in the use of nutrients and profitability of crops. The objectives were to compare methods for class delimitation in wheat (Triticum aestivum L.) crops based on apparent productivity levels and establish similarities among them in terms of spatial overlapping, productive attributes and the use of nitrogen. In

To ensure litchi fruit yield and quality, reasonable blooming period management such as flower thinning is required during the early flowering period. A combination of the number of litchi flowers and their density map can provide a reference for blooming period management decisions during the flowering period. Flowering intensity is currently largely estimated manually by humans observing the trees

Grain cleaning loss rate is an important performance index of combine harvesters which needs to be measured in real time during the harvesting operation. To improve the measurement accuracy and range, a grain loss sensor based on piezoelectric effect and adaptive neuro-fuzzy inference system (ANFIS) was proposed. A piezoelectric ceramic was fixed on the bottom of a thin sensitive plate to detect grain

Changes in land use in riverside ecosystems added to the flood and ebb regimes of rivers modify the spatial variability of the physical–mechanical attributes of soil, making it more susceptible to degradation processes, especially during the rainy season. The aim of this study was to evaluate the impact of the use of a Eutric Cambisol, which was cultivated with banana trees, on the pattern of spatial

Remote sensing imagery has been a key data source for precision agriculture. However, high-resolution and/or hyperspectral imagery have typically been favored for their greater information content. This study aims to demonstrate the capability of medium-resolution imagery in precision agriculture by developing an example of canola yield mapping using Sentinel-2 data in central Alberta. Two simple empirical

Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for

Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea mays L.) N status indicators across growth stages; (2)

This work proposes an approach for establishing coffee management classes mainly supported by pedological information (soil survey) and land parcels, taking into account peculiarities of Brazilian coffee crops (land parcels already implemented with different crop ages, cultivars and density) and inspired by some management zone concepts. Two initial datasets were used based on soil survey and/or coffee

Understanding the spatial patterns of soil microbial communities and influencing factors is a prerequisite for soil health assessments and site-specific management to improve crop production. However, soil microbial community structure at the field scale is complicated by the interactions among topography and soil properties. The objectives of this study were to (1) characterize the spatial variability

Determining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized

Crop cultivation provides ecosystem services on increasingly large fields. However, the effects of in-field spatial heterogeneity on crop yields, in particular triticale, have rarely been considered. The study assess the effects of in-field soil heterogeneity and elevation on triticale grown in an intensively cropped hummocky landscape. The field was classified into three soil classes: C1, C2, and

Nutrient assessment of plants, a key aspect of agricultural crop management and varietal development programs, traditionally is time demanding and labor-intensive. This study proposes a novel methodology to determine leaf nutrient concentrations of citrus trees by using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence (AI). The study was conducted in four different citrus