This study proposes a deep learning-based real-time variable flow control system using the segmentation of fruit trees in a pear orchard. The real-time flow rate control, undesired pressure fluctuation and theoretical modeling may differ from those in the real world. Therefore, two types of preliminary experiments were conducted to examine the linear relationship of the flow rate modeling. Through

Quantifying certain physiological traits under heat-stress is crucial for maximizing genetic gain for wheat yield and yield-related components. In-season estimation of different physiological traits related to heat stress tolerance can ensure the finding of germplasm, which could help in making effective genetic gains in yield. However, estimation of those complex traits is time- and labor-intensive

Potato (Solanum tuberosum) stem density variation in the field can be used to inform harvest timing to improve tuber size distribution. Current methods for quantifying stem density are manual with low throughput. This study examined the use of Unmanned Aerial Vehicle imagery as a high-throughput alternative. A colour-based feature extraction technique and a deep convolutional neural network (CNN) were

Grapevine phenology and total soluble solids (TSS) accumulation are important information to plan agricultural operation in the vineyards over the season. The study aims at identifying the main environmental factors and related high spatial auxiliary information that could be used to define within-field zones of phenology and TSS accumulation. An experiment was carried out in the Maule Valley, Chile

Site-specific weed management (SSWM) can provide significant herbicide savings but requires the reliable detection of weed populations. In this work, the effect of sample size on sampling precision was studied in four sub-field areas of winter cereals infested with Galium aparine. Plots of 1170 m2, 4212 m2, 2592 m2 and 2880 m2 were marked and the positions of all G. aparine individuals were recorded

Yield mapping technologies can help to increase the quantity and quality of agricultural production. Current systems only focus on the quantification of the harvest, but the quality has equal or greater importance in some perennial crops and impacts directly on the financial profitability. Therefore, a system was developed to quantify and relate the quality obtained in the classification line with

There is considerable interest and value in identifying the gap between crop yields that have actually been achieved, and yields that could have potentially been achieved. A suite of methods currently exist to estimate the yield potential of a crop, but there are no approaches that predict the site- and season-specific yield potential using datasets that are readily available and easily accessible

Precision agriculture (PA) technologies offer a potential solution to food security and environmental challenges but, will only be successful if adopted by farmers. Adoption in China lags behind that in some developed agricultural economies despite scientifically proven benefits of PA technologies for Chinese agriculture. Adoption is dependent on farmer attitudes and perceptions towards PA technologies

The accurate detection of young fruits in complex scenes is of great significance for automatic fruit growth monitoring systems. The images obtained in the open orchard contain interference factors including strong illumination, blur and occlusion, and the image quality is low. To improve the detection accuracy of young apples in low-quality images, a novel young apple detection algorithm that fuses

To increase nitrogen (N) use efficiency and reduce water pollution from vegetable production, it is necessary to optimize N management. Fluorescence-based optical sensors are devices that can improve N fertilization through non-destructive field monitoring of crop variables. The aim of this work was to compare the performance of five fluorescence indices (SFR-R, SFR-G, FLAV, NBI-R, and NBI-G) to predict

The objective of this paper is to modify the linear geographically weighted regression (GWR) estimator to accommodate the discontinuous join point, or “knot”, of the linear response with plateau (LRP). This is the first application to estimate a LRP site-specific crop response function (SSCRF) model with GWR. The data used in this heuristic application are from a variable rate nitrogen (VRN) trial

Laborious fruit thinning is required for attaining high-quality Medjoul dates. Thinning automation can significantly reduce labor and improve efficiency. An image processing apparatus developed for robotic Medjoul thinning is presented. Instance segmentation based on Mask R-CNN was applied to identify the fruit bunch components: spikelets and rachis. Motion planning parameters were extracted using

Vegetation indexes (VIs) are a key variable for monitoring the crop growth and estimating crop productivity. The spectral traits of the tassels were significantly different from those of the canopy leaves, which had an effect on the accuracy of using vegetation indices of canopy features to monitor crop growth. Advances in deep learning in computer vision provide opportunities for rapid and nondestructive

Soil nutrients vary greatly and information regarding their spatial-temporal variability is critical for achieving site-specific nutrient management. However, the problem is that the high cost of soil sampling and laboratory analysis limits the number of samples current research budgets can afford. Besides, large sampling intervals sometimes fail to capture the variability of typical soil properties

Wheat and rapeseed are significant crops in Czech agriculture and remote sensing has huge potential for their management, given Sentinel-1 can overcome issues of cloudiness and monitor vegetation development via radar backscatter. This study compares radar and optical data characterizing the development of wheat and rapeseed in an agricultural cooperative in the Czech Republic. Radar Vegetation Index

The Spodoptera frugiperda (i.e., fall armyworm) causes irreversible damage in cotton cultivars, and its visual inspection on plants is a burdensome task for humans. A recent strategy to automatically do similar tasks is processing hyperspectral reflectance measurements with machine learning algorithms. Herein, its proposed a framework for modeling the spectral response of cotton plants under the fall

Using a literature pool spanning 23 years, this meta-analysis quantifies the effect of factors underlying the adoption of precision agriculture. Unlike statistical significance, which demonstrates how likely adoption is due to chance, effect size indicates the importance of a factor to adoption. This meta-analysis finds that perceived profitability, consultants and use of a computer factors have a

Fruit detection and segmentation will be essential for future agronomic management, with applications in yield estimation, growth monitoring, intelligent picking, disease detection and etc. In order to more accurately and efficiently realize the recognition and segmentation of apples in natural orchards, a robust segmentation net framework specially developed for fruit production is proposed. This

Hedgerow orchard is an olive growing system where trees are planted at a super high-density higher than 20-fold (i.e., 1200–2500 trees ha−1) compared to the traditional density of olive orchards (usually 50 to 160 trees ha−1). It is dominating a great proportion of new plantations because harvesting can be fully mechanized, it is early bearing and has a relatively constant high productivity. However

Changes in primary cover for agricultural crops in Amazonas region influence the phenomenon of spatial variability in soil properties. This phenomenon is still studied assuming that the spatial data is isotropic, but does not consider the anisotropic pattern of soil properties. Thus, the aim of this work was to characterize, identify and correct isotropic patterns of magnetic susceptibility (MS) measurements

Coffee beverage quality is highly correlated with the degree of fruit ripeness. In this sense, monitoring fruit ripeness is of utmost importance for harvest planning and, especially for obtaining high-quality beverages. Currently, this process is carried out through manual counts of unripe fruits, which is laborious and limited to a few plants within the field. This study aimed at evaluating the potential

The pulse width modulation (PWM) spray system is the most advanced technology to obtain variable rate spray application without varying the operative sprayer parameters (e.g. spray pressure, nozzle size). According to the precision agriculture principles, PWM is the prime technology that allows to spray the required amount where needed without varying the droplet size spectra which benefits both the

Unoccupied aerial system (UAS) imagery may serve as an additional tool towards management zone delineation. This is because UAS data collection is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to preexisting tools (e.g. apparent soil electrical conductivity or ECa). The purpose of this study, therefore, was to evaluate UAS imagery,

Soil acidification is a major and growing concern in many cropping regions globally. Whilst spatial variability in acidification is a common consideration in the management of soil health and fertility at sub-paddock scale, insufficient focus has been directed toward the identification of this variability. Suitability of portable visible near infrared reflectance (vis–NIR) spectroscopy was assessed

In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal

Three vigor zones, identified in a Barbera vineyard by remote sensing at full canopy, were carefully ground-truthed to determine, over 2 years, the relative weight of soil factors in affecting within-field variability, and to investigate vigor zone influence on dry matter (DM) and nutrient partitioning into different vine organs. Regardless of season, high vigor (HV) achieved stronger vine capacity

Liming agricultural fields is necessary for counteracting soil acidity and is one of the oldest operations in soil fertility management. However, the best management practice for liming in Germany only insufficiently considers within-field soil variability. Thus, a site-specific variable rate liming strategy was developed and tested on nine agricultural fields in a quaternary landscape of north-east

Terrestrial laser scanning technology is an advanced active remote sensing ranging method that is well suited for yielding high-resolution scans of the morphology of a tree, which is an indicator of the health of the plant. The Ganoderma boninense fungus causes basal stem rot (BSR), which threatens the oil palm industry in Malaysia. To date, the current practice of inspection in a plantation is conducted

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

By collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment

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