Precision Agriculture (PA) adoption by farmers is limited by costs and technological complexity. Google Earth Engine (GEE) is used in large-scale crop research but remains underutilized for PA applications. Crop yield variability is widely studied, yet research advancements increasingly widen the gap to practical use. To address this, a GEE platform was established, harnessing Sentinel-2 and AgERA5

The mechanical harvesting of tart cherries has been a standard practice since the 1960’s but no technology has been proposed to map orchard yields. This study addresses this gap by introducing a low-cost yield monitor that tracks the distance between fruit bin changes using an ultrasonic proximity sensor, Global Navigation Satellite System (GNSS) positioning, and a microcomputer to collect and store

Purpose Nitrogen (N) fertilisation is one of the main factors contributing to crop yield. Nevertheless, only a limited number of studies have addressed the consequences of spatial variability on the N balance (Nb). While the spatial variability of soil properties has been widely investigated, its influence on Nb has been analysed in only a few studies. Therefore, the objectives of this study were to

Assessment of spatial variability of soil health indicators (SHI) from the root zone, not just the topsoil, is crucial for precise farm management decisions. We predicted the spatial distribution of soil organic carbon (SOC), inorganic carbon (SIC), total nitrogen (total-N), nitrate nitrogen (NO 3 -N), C: N ratio, phosphorus (PO 4 ), soil pH, and soil moisture (SM) from the root zone using soil samples

Purpose Spatial variability within fields and unpredictable rainfall patterns make nitrogen (N) management challenging, with up to 65% of applied N being lost to the environment. Post-emergence sidedress applications of N fertilizer can improve plant uptake and reduce N losses, making it critical to efficiently identify corn ( Zea mays L.) N status at early growth stages. We hypothesized that indicators

Context Accurate and regular estimation of above-ground biomass (AGB) in grassland ecosystems is essential for sustainable grazing management, feed planning, and carbon accounting. However, AGB mapping in heterogeneous grasslands remains challenging due to the spatial and temporal variability of vegetation and management practices. Aims This study explores the potential of Gaussian Process Regression

Purpose EU policies aim to reduce pesticide use, yet the on-farm competitiveness of site-specific weed management (SSWM) technologies remains unclear. This study evaluates the economic performance of three SSWM technologies in Western Pomerania, Germany, at both crop and whole-farm levels, integrating soil compaction risk and workability assessments resulting from practice changes. Methods A typical

PurposeThis study evaluates the effectiveness of various anaerobic digestate distribution methods, including liquid digestate with a nitrification inhibitor, liquid digestate applied with variable rate application (VRA), and solid digestate, compared to mineral fertilizer. The objective was to assess their agronomic performance, nitrogen use efficiency (NUE), and environmental impact in winter wheat

PurposeSpatial estimates of crop water use during the growing season are crucial for precision irrigation management, especially under conditions of water scarcity and climate change. The on-farm trial detailed in this paper focuses on a processing tomato field in the Sacramento Valley of California. MethodologyDifferent meteorological parameters, including temperature, precipitation, relative humidity

PurposeHyperspectral sensing (remote or proximal) has emerged as a pivotal tool to classify plant materials (seeds, leaves, and whole plants), pharmaceutical products, food items, and many other objects. Thus, hyperspectral sensing is one of the most frequently used technologies in research articles published by this journal, and it was therefore found relevant to address two methodological issues

PurposePrecision agriculture requires detailed knowledge of the within-field variation of yield forming factors and the productivity potential of each area of the field. The goal of this work was to use a case study to test all the steps of the process of creating a crop model-based yield map from soil mobile soil sensors and determine the impact of uncertainties and inaccuracies on the results.MethodsSoil

PurposeGrain protein content (GPC) is a key determinant of the prices that grain growers receive, but there is considerable variability within and between fields, farms, and seasons. Despite growing interest in measuring and mapping within-field GPC variability, the uptake of grain protein sensors has been slow, resulting in considerable knowledge gaps. Building a predictive model to map GPC in areas

PurposeSmartphone color imaging offers a promising, field-ready alternative to laboratory or hyperspectral techniques for free iron oxide (Fed), a key control on soil carbon sequestration, nutrient buffering and site-specific agronomic decisions.MethodsWe imaged 150 B-horizon samples from 70 soil profiles across subtropical Jiangxi, China, with five flagship smartphones (Samsung Galaxy Note 10+ 5G

PurposeEffectively transferring knowledge of digital innovations, such as precision agriculture, is critical for farmers to make adoption decisions to meet current and future farming challenges. Stronger knowledge flows from Extension systems to stakeholders benefit adoption of precision agriculture. MethodsThis study used a three-round Delphi technique with a panel consisting of 15 precision agriculture

The brown planthoppers (BPHs) are serious pests of rice in Southeast Asia which often cause a heavy loss of rice. Monitoring BPH populations and prediction of their damages to rice are more important for the precious control of this pest. Nowadays, highly efficient monitoring and predicting methods for BPHs are still rare. Here, the canopy temperatures of rice damaged by different number of BPHs were

PurposeIn Australia, pulse crops are underutilised relative to cereals, with a consensus that this is largely attributed to pulses exhibiting greater yield variability than cereals. However, the variability indicators used have typically not accounted for the spatial structure of within-field variation. A total of 762 yield maps across Australia were used to 1) compare the within-field variability

PurposeEfficient orchard management requires high-throughput phenotyping technologies to assist growers in crop monitoring and decision-making. This study presents Agrosense, an advanced artificial intelligence (AI) powered sensing system designed for real-time phenotypic data collection in orchards, addressing the limitations of traditional manual methods.MethodsAgrosense integrates four RGB-D cameras

PurposeUnmanned aerial vehicle (UAV)-based hyperspectral (HS) imaging enables precise monitoring of crop growth parameters. Machine learning (ML) has recently gained significant attention in precision agriculture as a powerful statistical learning technique for processing complex, multi-dimensional remote sensing data. This study aims to evaluate the integration of UAV-based HS imaging and ground measurements

PurposeDigital technologies have significantly improved nitrogen (N) fertilizer optimization in precision agriculture. A limitation of existing crop N detection approaches is that they primarily focus on above-canopy spectral measurements, overlooking the potential insights from lower canopy levels, which may more accurately reflect N stress through spectral reflectance associated with differential

PurposeCoffee growing in Brazil faces significant climate risks, with frost being one of the main threats to coffee productivity. However, the evaluation and monitoring of coffee plants after the occurrence of frost is complex. In this context, the use of remotely piloted aircraft (RPA) in assessments of coffee trees damaged by frost can offer a fast and accurate alternative to obtain qualitative and

Increasing problems with pesticide resistance and the adverse environmental side effects of pesticide use have increased the demand for developing alternative methods to control pests. Site-specific pest management can reduce the negative impact of pest management in horticulture and agriculture. In recent years, there has been an increasing focus on using laser beams to control pests by directing

PurposeFields characterised by large within-field variation stand to potentially gain from the differential application of inputs via Site-Specific Crop Management (SSCM). Maps generated from wheat grain yield and protein sensors are revealing considerable spatiotemporal variability within and between fields, farms, and seasons. Yet one of the biggest barriers to the successful adoption of SSCM is

In precision agriculture, the delineation of management zone (MZ) fundamentally relies on identifying spatial variabilities across agricultural fields and applying resources differentially. The key to precision agricultural strategies lies in the integration of heterogeneous data sets. This is crucial for establishing MZs, which are essentially uniform agricultural blocks distinguished by unique soil

Low-cost multispectral sensors have recently been commercialized, paving the way for decision support in a wide variety of agricultural applications (fertilization, grass cover management, etc.). Such sensors have seldom been tested for agricultural applications taking into account practical constraints (external environment, expected accuracy, etc.). This study proposes to investigate the measurement

The investment in soil sampling is often disproportionate to the level of investment for soil amelioration. While advanced geostatistical Digital Soil Mapping (DSM) methods exist to map spatially variable soils, they are rarely used in precision agriculture, due to the increased soil sampling requirement, the cost of which is perceived as prohibitive. Consequently, soil constraints are regularly managed

PurposeZones for spatial irrigation management are often assumed to be static, dictated by non- or slowly-changing parameters, including elevation and slope, soil depth, texture and hydraulic properties, and other landscape properties. Sensing allows for the integration of static management zones (MZ) with a dynamic characterization of water status. Temporal remote sensing data, indicating crop status

PurposeYield mapping in viticulture is crucial for optimizing vineyard management. However, it remains constrained by limited yield monitoring sensors on grape harvesters and traditional remote sensing methods, which provide deterministic predictions without explicit uncertainty quantification. This study develops and evaluates a Bayesian hierarchical approach for vineyard yield mapping.MethodsThe

PurposeBacterial blight poses a significant threat to red kidney bean growth, often leading to substantial yield losses. Real-time monitoring of this disease is crucial for effective prevention and control, ensuring optimal yield. This study proposes a hyperspectral-based approach to assess the the bacterial blight disease in red kidney beans.MethodsIn this study, canopy hyperspectral data from two

Modern agriculture faces the challenges of food security with increasing world population and the need to protect natural resources. Organic farming is known as more environmentally friendly, but has a deficit of around 80% yield compared to conventional farming due to lower availability of nitrogen (N) fertilizer. Site-specific management based on subfields can improve the use efficiency of nitrogen

PurposeVariable Rate Nitrogen (VRN) fertilization can improve yields, quality and/or N use efficiency (NUE) of several crops. The objective of this study was to evaluate how pre-emergence VRN zones vs. uniform N management impacts potato (Solanum tuberosum L.) yield, quality and NUE.MethodsLow, average and high N zones were created and evaluated for 10 site-years at fields near Grace, Idaho, United

BackgroundIn recent decades, technological advances have significantly transformed various sectors of the economy, including agriculture. Precision agriculture, as a multidisciplinary and integrative approach, relies on the convergence of diverse technologies to optimize decision-making and enhance productivity, efficiency, and sustainability. In these evolving and complex technological landscapes

The normalised difference vegetation index (NDVI) is widely used for crop yield prediction. Several studies have shown that there is a positive correlation between NDVI and crop yield, with higher NDVI values indicating healthier and more productive crops. However, various factors can influence the accuracy of the NDVI-crop yield relationship. A systematic review, meta-analysis, and topic modelling

PurposePrecise segmentation of blueberry maturity is critical for optimizing harvestschedules and maintaining product quality. Traditional methods, which rely on manualinspection, are not only labor-intensive but also cost-inefficient. This study presents a novelframework that integrates deep learning-based super-resolution reconstruction (SRR) withsemantic segmentation to provide a fast and accurate

PurposeAs sensing technology and spatial data analysis become more accessible and advanced, nitrogen management is shifting from reliance on traditional soil sampling to the use of remotely sensed imagery and yield maps. While studies often compare variable rate nitrogen (VRN) fertilization to uniform rates, the profitability of information sources guiding VRN recommendations remains unclear. This

Soil compaction caused by heavy agricultural machinery poses a significant challenge to sustainable farming by degrading soil health, reducing crop productivity, and disrupting environmental dynamics. Field traffic optimization can help abate compaction, yet conventional algorithms have mostly focused on minimizing route length while overlooking soil compaction dynamics in their cost function. This

PurposeThis review synthesizes advancements in proximal spectral sensing devices—including portable, vehicle-based, UAV-based, and IoT-based—for monitoring field crop growth traits. By evaluating their technical capabilities, applications, and limitations, it addresses critical challenges in scalability, data integration, and environmental adaptability to advance precision agriculture (PA) practices

PurposeAutomated plot extraction in agronomic research field trials is essential for high-throughput phenotyping and precision agriculture. Accurate delineation of plot boundaries enables reliable crop type classification, yield estimation, and crop health monitoring. However, traditional plot extraction methods rely heavily on manual digitization, which is time-consuming, labor-intensive, and prone

The majority of newly developed sprayers now feature advanced capabilities, allowing herbicide application with centimeter-level precision, potentially reducing herbicide use by up to 90%. However, accurately identifying the precise locations to spray, known as the application map, remains a significant research challenge. Recently, both commercial providers and research institutions have proposed

Spot spraying represents an efficient and sustainable method for reducing herbicide use in agriculture. Reliable differentiation between crops and weeds, including species-level classification, is essential for real-time application. This study compares state-of-the-art object detection models-YOLOv8, YOLOv9, YOLOv10, and RT-DETR-using 5611 images from 16 plant species. Two datasets were created, dataset

Timely and accurate monitoring of plant nitrogen concentration (PNC) is vital for optimizing field N management. Hyperspectral indices are commonly used as a predictor for monitoring the PNC of crops, but individual spectral indices are often susceptible to cultivars and growth stages. Machine learning (ML) is a promising method for mining more spectral variables to assess the PNC of crops. To monitor

Current approaches for monitoring soil organic matter (SOM) exhibit limitations in long-term predictive accuracy and data efficiency. This study aims to develop a remote sensing framework that integrating Landsat imagery and three modeling algorithms (PLSR, RF, Cubist) to address these challenges, reduce sampling workload, and enable large scale soil fertility assessments. Feature selection via Boruta

The Małopolskie and Podkarpackie provinces in Poland are characterized by many small farms with many small, scattered fields. This farm structure is labeled “agrarian fragmentation”. Using digital technologies in such small farm areas is usually a challenge. However, there are several digital technologies that, with minimal financial investment, can yield results in the form of improved resource management

Efficient water management is critical for sustainable agriculture in Mediterranean climates, particularly in super-high-density (SHD) olive orchards where water scarcity poses significant challenges. This study assessed the potential of UAV-based thermal and multispectral imagery to monitor crop water status and predict yield under different regulated deficit irrigation (RDI) strategies. Conducted

The deep integration of artificial intelligence technology and agriculture has significantly propelled the rapid development of smart agriculture. However, the field still faces numerous challenges, including high algorithm complexity and limited detection speed in farmland environments. To address the challenges encountered by corn spraying robots in navigating and identifying lines, we have proposed

PurposeIn contemporary agriculture, achieving sustainable food production while preserving the environment is crucial. Traditional uniform rate nitrogen fertilization (URNF) often leads to over- or under-applications of N in fields with negative economic, agronomic and environmental issues. Variable rate nitrogen fertilization (VRNF) has shown promise in optimizing N application by accounting for soil

Assessing spatial and temporal variations in crop water stress is vital for precision irrigation. This study utilized Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MSS) and thermal band (TB) sensors to map the crop water stress index (CWSI) in wheat. A water deficit experiment was conducted on winter wheat under varying irrigation levels during late vegetative, reproductive, and maturation

Purpose The hull scrape and vegetation indices are widely used for predicting peanut maturation, but they are time-consuming, subjective, labor-intensive, and fail to account for climate variables, reducing their accuracy.Thus, the objective was to verify the potential of using artificial intelligence associating IV and climate variables to predict the variability of peanut pod maturity in the fieldMethods

IntroductionUp till now, digitalisation in agriculture has almost only been discussed in the context of large farms. However, sooner or later, ongoing digitalisation will reach the agricultural sector as a whole. Indeed, even smaller farms can also benefit from the opportunity and make profitable use of digital agricultural technology by adopting inter-farm organisational forms e.g. collaboration between

PurposePrecision agriculture technologies play an important role in optimising practices to increase yields and reduce costs, contributing to socio-economic progress and environmental well-being, and playing a key role in addressing climate change. Viticulture is a strategic, input-intensive agricultural sector where precision technologies can make the use of resources more efficient without compromising

Although unmanned aerial vehicle (UAV) remote sensing is widely used for high-throughput crop monitoring, few attempts have been made to assess nitrogen content (NC) at the organ level and its association with nitrogen use efficiency (NUE). Also, little is known about the performance of UAV-based image texture features of different spectral bands in monitoring crop nitrogen and NUE. In this study,

Through targeted spray applications, precision agriculture can provide not only environmental benefits but also lower production costs, improving farm competitiveness. Nevertheless, few studies have focused on the cost-effectiveness of precision agriculture sprayers in vineyards, which are among the most widespread specialty crops. Therefore, this is the first study that aims to evaluate the cost-effectiveness

PurposeCreating spatial weed distribution maps as the basis for site-specific weed management (SSWM) requires determining the occurrence and densities of weeds at georeferenced grid points. To achieve a field-wide distribution map, the weed distribution between the sampling points needs to be predicted. The aim of this study was to determine the best combination of grid sampling design and spatial

Spring dead spot is a disease of bermudagrass (Cynodon dactylon L. Pers) caused by Ophiosphaerella spp., of fungi which infect the below ground structures of plants, causing damage to the turf canopy. Previous research suggests that precision management strategies based on manually identified disease within unmanned aerial vehicle (UAV) imagery using GIS software and global navigation satellite systems

Counting soybean plants is a crucial strategy for assessing sowing quality and supporting high production. Despite its importance, the laborious nature of traditional assessment methods makes them unreliable and not scalable. Additionally, innovative image-based solutions have demonstrated limitations in detecting dense crops such as soybeans. Therefore, in this study, we developed neural network models

Precision Agriculture relies significantly on yield data obtained from combine harvesters, which constitutes a pivotal tool for optimizing crop productivity. Despite its potential, challenges in data accuracy persist, necessitating the development of novel automated postprocessing protocols for yield data refinement. In this paper, different automatic postprocessing protocols were evaluated using remote