A correction to this paper has been published: https://doi.org/10.1007/s11119-021-09824-9

Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without

Defining the ideal soybean stand is essential for obtaining maximum grain yield. As a hypothesis of the work, it was questioned whether it is possible to vary seeding population of soybean based on the physical–chemical attributes of soil related to grain yield. The objectives of this work were (i) to identify soil attributes that help obtain the maximum grain yield grown in the Cerrado region of Brazil;

Soil acidification is a growing problem in semi-arid agroecosystems. In the state of Montana, USA, soil pH levels below 5.5 have been documented in nearly half of the counties. Acidic soils have the potential to reduce crop yield, but methods to identify and remediate acidic soils are costly and time-intensive. This study tests a relatively new approach for identifying areas of acidic soils using imagery

Precision agriculture provides detailed information on the spatial variability of soil properties, including nutrient content, allowing for local-specific decision making. Recently, proximal sensors have been used to accurately predict soil properties, contributing to reduce costs of conventional wet-chemistry analyses for soil characterization. However, further investigations on this approach in tropical

Granular blended fertilizers are commonly applied using spinner disc spreaders in the U.S. with recent advances including wider spreads. Today, uniformity of spread is important as growers and custom applicators attempt to implement site-specific management and utilize global navigation satellite systems (GNSS)-based guidance systems which permit equipment to follow the same field traverses over time

Understanding yield potential and yield-limiting factors is essential for improving profitability and grain yields while avoiding adverse environmental effects. In the USA, grain yield monitors are widely used but the information they provide is rarely used to understand within-field yield variations and associated yield constraints. The objectives of this research were to understand the influence

The use of Artificial Intelligence (AI) in agriculture is expected to yield advantages such as savings in production resources, labor costs, and working hours as well as a reduction in soil compaction. However, the economic and ecological benefits of AI systems for agriculture can only be realized if farmers are willing to use them. This study applies the technology acceptance model (TAM) of Davis

Weed maps created late in the growing season are potentially useful in regions where late maturing weeds are problematic and need to be controlled before they produce seed. The objectives of this study were to (1) spatially characterize the population dynamics of predominant weed species and apply this information into quantifying the effect of treated and untreated weed infestations on wheat (Triticum

This study evaluated the agronomic and economic prospects of Site-Specific Seeding (SSS) for consumption and seed potato production based on Management Zone (MZ) maps delineated with the fusion of multiple soil and crop attributes at four experimental sites in Belgium. Soil pH, organic carbon, P, K, Mg, Ca, Na, moisture content, cation exchange capacity, apparent electrical conductivity and crop normalized

Precision nitrogen fertilizer application depends on accurate estimation of plant nitrogen content. However, the assessment of plant nitrogen content at early growth stages of paddy rice through remote sensed images is complicated by the compound effects of backgrounds (e.g. flood water, bare soil, algae, etc.) on the band reflectance. The rapid changing of plant nitrogen content during the vegetative

Unmanned aerial vehicle (UAV) system is an emerging remote sensing tool for profiling crop phenotypic characteristics, as it distinctly captures crop real-time information on field scales. For optimizing UAV agro-monitoring schemes, this study investigated the performance of single-source and multi-source UAV data on maize phenotyping (leaf area index, above-ground biomass, crop height, leaf chlorophyll

Drones are one of the latest tools to have been added to farmers’ precision agriculture technology tool kit. Despite the proclaimed benefits, adoption rates of drones are low and literature regarding the adoption of drones in agriculture is scarce. Therefore, this study investigates whether an extended Technology Acceptance Model (TAM) can contribute to the understanding of latent factors influencing

Enthusiasm regarding the “digital agriculture” revolution is widespread, yet objective research on how commercial farms actually use data and data services remains limited. The purpose of this research is to better understand the current positioning of U.S. commercial corn and soybean farms within the farm data lifecycle, including the collection, use, and impact of farm data. Using survey data from

Controlling weed infestation through chemicals (herbicides & pesticides) is essential for crop yield. However, excessive use of these chemicals has caused severe agronomic and environmental problems. According to accurate weed detection, an appropriate dose of herbicides is recommended in site-specific weed management (SSWM) applications, which may ultimately promote chemical saving while enhancing

Recently, agriculture has gained much attention regarding automation by artificial intelligence techniques and robotic systems. Particularly, with the advancements in machine learning (ML) concepts, significant improvements have been observed in agricultural tasks. The ability of automatic feature extraction creates an adaptive nature in deep learning (DL), specifically convolutional neural networks

The targeted treatment of weeds is an expanding part of precision farming in many countries. Targeted weed treatments, using precision spray maps, reduce herbicide consumption, whilst still maintaining long term weed control. Assembling accurate spray maps is a vital part of this process. However, acceptable accuracy in spray maps is difficult to quantify, due in part to rapid technological advances

The upcoming technological breakthrough in the cropping system will offer a more detailed insight into soil-to-plant, man-to-soil, and man-to-plant impacts, thus improving the forecasting and ensuring more efficient in-field management. This study presents various on-the-go sensing procedures which were conducted in order to evaluate the quality of spatial estimations of soil physical properties such

Real-time non-destructive monitoring of nitrogen accumulation by hyperspectral remote sensing is important for crop nitrogen management. In this study, winter wheat field experiment incorporating several varieties and exogenous nitrogen treatments was performed at multiple sites. Using hyperspectral readings of the experimental crops, the continuum removal method was used to expand the chlorophyll

This paper examines the prospects and challenges of developing and implementing precision agricultural technologies (PATs) in cocoa production in Ghana. A census of cocoa research scientists and a survey of cocoa extension agents (CEAs) in Ghana were taken. Five major challenges they perceived to pose serious challenges to the development and implementation of future PATs were: (a) farmer-demographic

Crop discrimination at the plant or patch level is vital for modern technology-enabled agriculture. Multispectral and hyperspectral remote sensing data have been widely used for crop classification. Even though spectral data are successful in classifying row-crops and orchards, they are limited in discriminating vegetable and cereal crops at plant or patch level. Terrestrial laser scanning is a potential

On-farm experiments can provide farmers with information on more efficient crop management in their own fields. Developments in precision agricultural technologies, such as yield monitoring and variable-rate application technology, allow farmers to implement on-farm experiments. Research frameworks including the experimental design and the statistical analysis method strongly influences the precision

This study investigated the potential of in-season airborne hyperspectral imaging for the calibration of robust forage yield and quality estimation models. An unmanned aerial vehicle (UAV) and a hyperspectral imager were used to capture canopy reflections of a grass-legume mixture in the range of 450 nm to 800 nm. Measurements were performed over two years at two locations in Southeast and Central

Observed variations in the ground electrical conductivity (σb*) measurements obtained with EMI instruments depend on several soil proxies that influence crop growth and development such as salinity (σe), contents of water (θw), clay (wc) and organic matter (wom) and bulk density (ρb). However, the relative contributions of all these to σb* are unknown. This knowledge is needed to improve the planning

One of the main problems in the peanut production process is to identify the pod maturity stage. Peanut plants have indeterminate growth, which leads to a high pod maturity variability within the same plant. Moreover, the actual method of determining maturity is destructive and highly subjectivity, which does not represent the overall variability in the field. Hence, the main goal of this study was

Harvester-mounted yield monitor systems are increasingly used to document corn (Zea mays L.) yield. The three most commonly used spatial estimation methods to convert point data gathered with yield monitors to regular, grid-based, raster maps include nearest neighbor (NN), inverse distance weighting (IDW) and kriging. Seven spatial estimation methods (NN, IDW using 10, 20, 30 and all data points and

Monitoring vine water status is a major issue for vineyard management because water constraints impact both the quality and the quantity of the harvest. Existing methods are often costly and complex to implement. ApeX-Vigne is a free mobile application developed to facilitate the collection and geolocation of 50 vine apex observations to characterise vine shoot growth and classify it into 3 growth

Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management

A correction to this paper has been published: https://doi.org/10.1007/s11119-021-09796-w

Studies of crop coefficients as a function of vegetation indices are reported less often for sugarcane than other crops because of the variation in crop evapotranspiration (ETc) and spectral response over the long growth period. In this study, the possibility of correlating crop coefficient (Kc) and ground based normalized difference vegetation index (NDVI) of a sugarcane crop was investigated based

Soil total nitrogen (TN) concentration is an important soil nutrient parameter. However, the high-precision measurement of TN concentration in real time remains a challenge. Accordingly, an on-the-go TN concentration detector was developed on the basis of near-infrared spectroscopy. The modular concept and the extreme learning machine (ELM) algorithm were applied to design and model the detector, respectively

Unmanned aerial vehicles (UAVs) present themselves as an alternative to overcome the limitations of satellite sensors in monitoring agricultural crops, motivating many studies with UAVs. They can carry sensors, which need studies for better understanding. The present study aimed to vicariously calibrate a Red-Green-Near infrared (RG-NIR) low-cost sensor on board a UAV, and to develop predictive models

The purpose of this work was to: (i) propose a methodology to infer local estimates of the available water capacity (AWC) at a plot from a few measurements in laboratory of AWC carried out on horizons of a pit on the same plot, (ii) examine the effect of measurement errors on spatial estimates of AWC and the associated uncertainties. For each horizon identified in the pit, the water content was determined

Due to the low efficiency of nitrogen fertilizers in flooded rice paddies, there is a rising demand for tools able to detect crop nitrogen status in space and time to allow farmers to use the technical novelties of precision agriculture to improve fertilizer management in extensive fields. This work sets up an operational approach to increase nitrogen use efficiency of top-dressing fertilization by

In this study, site-specific N balances were calculated for a 13.1 ha heterogeneous field. Yields and N uptake as input data for N balances were determined with data from a combine harvester, reflectance measurements from satellites and tractor-mounted sensors. The correlations between the measured grain yields and yields determined by digital methods were moderate. The calculated values for the N

Proximal sensing is being integrated into vineyard management as it provides rapid assessments of spatial variability of soils’ and plants’ features. The electromagnetic induction (EMI) technology is used to measure soil apparent electrical conductivity (ECa) with proximal sensing and enables to appraise soil characteristics and their possible effects on plant physiological responses. This study was

Working within a vineyard in the Pessac Léognan Appellation of Bordeaux, France, this study documents the potential of using simple statistical methods with spatially-resolved and increasingly available electromagnetic induction (EMI) geophysical and normalized difference vegetation index (NDVI) datasets to accurately estimate Bordeaux vineyard soil classes and to quantitatively explore the relationship

As farming practices become increasingly automated, the quantity of high resolution on-farm production information grows exponentially and so does the need for high-throughput computing solutions to aid management. High resolution (5 m) wheat yield forecasting is presented here using two machine learning approaches: (a) Bootstrapped Regression Trees (BRR) where predictions are pixel-wise and (b) Convolutional

Studying weed spatial distribution patterns and implementing precise herbicide applications requires accurate weed mapping. In this study, a simple unmanned aerial vehicle (UAV) was utilized to survey 11 dry onion (Allium cepa L.) commercial fields to examine late-season weed classification and investigate weeds spatial pattern. In addition, orthomosaics were resampled to a coarser spatial resolution

Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld

Precision agriculture (PA) has a huge potential for growth in sub-Saharan Africa (SSA), but it faces a number of social-economic and technological challenges. This study sought to map existing PA research and application in SSA countries following the methodology for systematic mapping in environmental sciences. After screening for relevance, the initial about 7715 articles was reduced to 128. Results

Tea leaf blight (TLB) is a common tea disease seriously affecting the quality and yield of tea. An accurate estimation of TLB severity can be used to guide tea farmers to reasonably spray pesticides. This study proposes an estimation method for TLB severity in natural scene images and consists of four main steps: segmentation of the diseased leaves, area fitting of the diseased leaves, segmentation

The paper describes a geostatistical approach for combining multi-source data with different support for field delineation into homogeneous soil zones. It takes into account change of support explicitly given the critical influence of spatial resolution on the statistical characteristics of estimates. Geophysical and hyperspectral data were used in combination with soil chemical properties measured

To evaluate the impact of weeds on crops, precise identification and early prediction are required. This paper presents two new non-destructive indicators deduced from visible images: weed pressure (WP) and wheat growth status (WGS). They are based on the fractional vegetation cover (FVC) obtained from digital vegetation maps (crop vs. weeds) in a wheat field. FVC was determined for both plants with

Accurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation fields from the remaining objects in a multispectral scene is a difficult task for traditional algorithms. In this connection, deep learning methods that perform semantic

This paper considers the problem of autonomous navigation in agricultural fields. It proposes a localization and mapping framework based on semantic place classification and key location estimation, which together build a hybrid topological map. This map benefits from generic partitioning of the field, which contains a finite set of well-differentiated workspaces and, through a semantic analysis, it

Soil water content (θ) measurement is vital for accurate irrigation scheduling. Electromagnetic induction surveys can be used to map spatial variability of θ when other soil properties are uniform. However, depth-specific θ variations, essential for precision irrigation management, have been less investigated using this method. A quasi-2-dimensional inversion model, capable of inverting apparent soil

Uniform plant stand, herein understood as an outcome of plant spacing and seeding-depth uniformity, requires proper selection of downforce control across varying field conditions, especially when planting at faster ground speeds. Thus, objectives of this study were (1) to assess the effect of ground speed and downforce settings on plant spacing and seeding depth, and (2) evaluate the relationship of

Efficient grass-based livestock production depends on precise allocation of pasture to the herd in the form of herbage mass (HM). Accurate measurement of HM results in increased utilisation of grass in the herd’s diet and consequently reductions in whole-farm feed inputs, emissions and costs. The rising plate meter (RPM) is an established method of estimating HM, but there is scope to improve its accuracy

Farm management information system (FMIS) is an important element of precision agriculture to support the decision making process in the agricultural business. Developing FMIS is not trivial and requires the proper design and implementation models for supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders. To cope with

To improve the monitoring accuracy of grain loss sensors, an optimised structure for grain loss sensors was designed. First, stainless steel 304, copper plate and aluminum T6 were selected as potential materials for sensitive plates, and the collision signal characteristics with these different materials were studied in detail. Then, the relationship between the damping ratio and collision response

Identifying and quantifying the number and size of stomata on leaf surfaces is useful for a wide range of plant ecophysiological studies, specifically those related to water-use efficiency of different plant species or agricultural crops. The time-consuming nature of manually counting and measuring stomata have limited the utility of manual methods for large-scale precision agriculture applications

Soil acidification is caused by natural paedogenetic processes and anthropogenic impacts but can be counteracted by regular lime application. Although sensors and applicators for variable-rate liming (VRL) exist, there are no established strategies for using these tools or helping to implement VRL in practice. Therefore, this study aimed to provide guidelines for site-specific liming based on proximal

This paper presents a novel methodology for identifying homogeneous areas within high-frequency drip-irrigated orchards and for defining the most sensitive and resistant areas of the field to water stress. The methodology proposed here is based on the assessment of water status at the tree level during mild water stress using remote sensing derived indicators which provide valuable information about

Real-time monitoring of leaf nitrogen (N) content by remote sensing can accurately diagnose crop nutrient status and facilitate precision N management. However, the methods used to estimate of vertically integrated leaf N content do not consider different cropping systems, in which the maize growth stages are not synchronized, resulting in decreased practical value of the results. The purpose of this

Accurate estimation of herbage mass (HM) is essential for optimising grass utilisation and increasing profit for pasture-based livestock agriculture. The rising plate meter (RPM) is used for predicting HM based on average compressed sward height (CSH). Sampling resolution and distribution are primary parameters in determining spatial heterogeneity of HM. There is no definitive sampling protocol for

Most non-destructive methods for plant stress detection do not measure the primary stress response but reactions of processes downstream of primary events. For instance, the chlorophyll fluorescence ratio Fv/Fm, which indicates the maximum quantum yield of photosystem II, can be employed to monitor stress originating elsewhere in the plant cell. This article describes the properties of a sensor to

Agriculture is both the site of development of important new technologies and a key area of application of technologies developed elsewhere. It is little wonder, then, that many thinkers believe that progress in the science and engineering of robotics may soon change the face of farming. This paper surveys the prospects for agricultural robotics, discusses its likely impacts, and examines the ethical

Estimating aboveground biomass is important for monitoring crop growth in agronomic field experiments. Often this estimation is done manually, destructively (mowing) or not (counting) on a relatively limited number of sub-plots within an experiment. In the presence of spatial heterogeneity in experiment fields, sensors developed for precision agriculture, have shown great potential to automate this

Mapping the within-field variability of crop status is of great importance in precision agriculture, which seeks to balance agronomic inputs with spatial crop demands. Satellite imagery and the delineation of management zones based on remote sensing plays a key role. However, satellite imagery is dependent on a cloud-free view, which is especially challenging in temperate regions such as Northern Europe