Purpose This 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

Purpose Automated 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

Purpose In 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

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 field

Introduction Up 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

Purpose Precision 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

Purpose Creating 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

Accurate estimation and spatial allocation of economic optimum nitrogen (N) rates (EONR) can support sustainable crop production systems by reducing chemical compounds to be applied to the ground while preserving the optimum yield and profitability Smart Farming (SF) techniques such as historical precision agriculture (PA) machinery data, satellite multispectral imagery, and on-machine nitrogen adjustment

The ability to reduce sampling distance or time is crucial for growers to monitor vineyard nutrients more frequently. Extension specialists often recommend collecting large random samples, but this is frequently overlooked, leading to inaccurate fertilizer recommendations. A novel, one-location square grid area-based sampling method called “box” sampling was developed to capture the overall nutrient

Nitrogen (N) is a key factor affecting corn yield. Remote sensing of spectral reflectance from plant canopies offers an efficient way to assess N status. High spatial and temporal resolution imagery from unmanned aerial vehicles (UAVs) provides additional advantages. This study aimed to (1) develop and validate a model to predict top-dressing N requirements at the V5 stage using vegetation indices

Evaluated Sentinel-2, SuperDove, and Dove-R consistency for wheat field monitoring. Hierarchical evaluation on surface reflectance, VIs, and LAI. VI and LAI consistencies of Sentinel-2 and PlanetScope exceed surface reflectance. Sentinel-2 and PlanetScope’s optimal synergy interval at VI or LAI is 2 days.

Mechanical weeding is an important part of integrated weed management. It destroys weeds between (interrow) and in (intrarow) crop rows. Preventing crop damage requires precise detection and tracking of the plants. In this work, a detection and tracking algorithm was developed and integrated on an intrarow hoeing prototype. The algorithm was developed and validated on 12 rows of 950 cauliflower plants

Optimizing the timing of rice paddy drainage and harvest is crucial for maximizing yield and quality. These decisions are guided by rice grain moisture content (GMC), which is typically determined by destructive plant samples taken at point locations. Providing rice farmers with predictions of GMC will reduce the time burden of gathering, threshing and testing samples. Additionally, it will reduce

Purpose Extracting the extent of wheat lodging is essential for post-disaster emergency response, disaster assessment, and accurate agricultural insurance claims. However, traditional methods for identifying lodged crops often lack flexibility, exhibit low levels of automation, and suffer from inefficiency. Methods This study proposes a rapid identification algorithm for wheat lodging, utilizing adaptive

The use of thermal-based crop water stress index (CWSI) has been studied in many crops in semi-arid regions and found as an effective method in detecting real-time crop water status of commercial fields remotely and non-destructively. However, to our knowledge, no previous studies have validated the usefulness of CWSI in a temperate crop like wild blueberries. Additionally, the temporal changes of

Durum wheat (Triticum durum Desf.) yield should be maximized to meet the growing global demand for pasta production. Precision agriculture (PA) could play a pivotal role in reaching this goal by correctly defining management zones (MZ) and optimizing the use of energy inputs. The aim of the work was to understand the relationship between MZ generated from observed yield data and those generated using

In recent years, there has been a significant progress in technologies used in 3D crop spraying. The inherent goal of applying these technologies has been to reduce drift, improve efficacy in the use of Plant Protection Products (PPP) and, consequently, reduce the amount of chemicals in fruit production, thus minimizing environmental impact and enhancing human health. In order to assess the study of

Rapid and accurate detection of fruits is crucial for estimating yields and making scientific decisions in litchi orchards. However, litchis grow in complex natural environments, characterized by variable lighting, severe occlusion from branches and leaves, small fruit sizes, and dense overlapping, all of which pose significant challenges for accurate detection. This paper addressed this problem by

Context Serrated tussock (Nassella trichotoma) is a weed of national significance in Australia which offers little to no nutritional value to livestock, and has the potential to reduce carrying capacity and agricultural return of infested pastures. Aims The aim of this study was to adapt existing Convolutional Neural Networks (CNNs) for plant segmentation and spraypoint detection in the challenging

Purpose Management zones (MZs) are the subdivision of a field into a few contiguous homogeneous zones to guide variable-rate application. Delineating MZs can be based on geostatistical or clustering approaches, however, the joint use of these approaches is not usual. Here, we show a joint use of both techniques. The objective of this manuscript is twofold: (1) compare different procedures for creating

Assessing the grain yield of wheat remains a great challenge in field breeding trials. Multispectral and RGB images acquired by UAVs offer a promising tool for in-season prediction yet with varying results during the growing season. Therefore, enhancing prediction accuracy through optimizing multi-date models seems necessary but needs to be weighted with time and costs. Multi-date models outperform

Few strategies have been developed to effectively delineate management zones (MZs) in crop-pasture rotation (CPR) systems that accommodate site-specific management for multiple crops using a single map. This study aimed to propose and evaluate several feature selection approaches that account for multiple crops in CPR systems and propose a framework for MZ delineation in CPR systems that results in

The provision of ecosystem services from Mediterranean permanent grasslands is threatened due to shifting management practices and environmental pressures. This observational study tested the hypothesis that Land Surface Phenology (LSP) parameters from high-resolution satellite data can characterize various permanent grasslands to support conservation and improvement practices. The potential of LSP

The article defines the key determinants of adopting precision agriculture technologies and digitalisation. The research objectives are fulfilled by the systematic review and meta-analysis of relevant studies, identified and selected in accordance with the PRISMA protocol in the Web of Science and Scopus databases. The findings emphasize the importance of socio-economic factors, such as education,

Purpose The use of UAVs (Unmanned Aerial Vehicles) equipped with sensors such as laser scanners offers an alternative to conventional, labor-intensive manual measurements in agriculture, as they enable precise and non-destructive field surveys. Methods This paper evaluates the use of UAV-based laser scanning (RIEGL miniVUX-SYS) for estimating the crop height and the plant area index (PAI) of winter

Introduction Optimizing fruit quality and yield in agriculture requires accurately monitoring leaf nitrogen (N) status spatially and temporally throughout the growing season. Standard remote sensing approaches for assessing leaf N rely on proxies like vegetation indices or leaf chlorophyll a + b (Cab) content. However, limitations exist due to the Cab-N relationship’s saturation and early nutrient

The first stage in the precision agriculture cycle has been a vital study area in recent years because it allows soil testing followed by data analysis. In this stage, a strategic delineation of site-specific management zones acquires a particular interest because it enables site-specific treatment to improve crop yield by efficiently using the input of resources. The delineation of site-specific management

The global attention to the utilization of unmanned aerial vehicle remote sensing drones in crop disease-wide detection has led to the urgent need to find an adapted model for different environmental conditions. Therefore, the current study has focused on spatiotemporal usage of different multispectral cameras in acquiring spectral reflectance models of in-field rice bacterial blight stresses. Where

Spatial and temporal variability of the soil’s apparent electrical conductivity (ECa) and other soil attributes can be analyzed using specific digital platforms for precision agriculture, contributing to agricultural management decision-making. Understanding these variations enables more efficient and sustainable management practices tailored to each area’s characteristics, leading to higher crop yields

On-farm experiments (OFE) typically do not account for limitations of grain yield monitors such as the dynamics of grain flow through a large combine. A common question asked within OFE is how ground speed impacts yield estimates from grain yield monitors. Therefore, the objective of this study was to determine if combine ground speed influences the ability of grain yield monitors to report yield differences

Nitrogen (N) is vital for plant growth, but its imbalance can negatively affect crop yields, the environment, and water quality. This is especially crucial for California’s almond orchards, which are the most N-hungry nut crop and require substantial N for high productivity. The current practices of uniform and extensive N application lead to N leaching into the groundwater, creating environmental

Soil sampling is crucial for capturing soil variability and obtaining comprehensive soil information for agricultural planning. This article evaluates the potential of MaxVol, an optimal design method for soil sampling based on selecting locations with significant dissimilarities. We compared MaxVol with conditional Latin hypercube sampling (cLHS), simple random sampling (SRS) and Kennard-Stone algorithm

There has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), as well as the abundance of machine learning modelling approaches. However, there are several common

Efficient mechanical harvesting of wild blueberries across uneven topographies calls for precise header height adjustments to optimize fruit picking. Conventionally, an operator requires manual adjustment of the harvester header to accommodate the spatial variations in plant height, fruit zone, and field terrain. This can result in inadequate header positioning, which leads to berry losses and increased

Crop mapping or crop recognition specifies the types of agricultural crops that grow in a selected region. Hyperspectral imaging (HSI) acquires spectral reflectance profiles of materials in hundreds of narrow and continuous spectral bands in the optical electromagnetic spectrum. The emerging compact HSI sensors mountable on ground-based platforms and drones are promising data sources for crop classification

The design and analysis of on-farm experimentation (OFE) have received growing attention because of the availability of precision machinery that promotes data collection. Even though replicated trials are the most recommended designs, on-farm trials with no replication are used in scenarios where variable rate technology is not available. Despite the abundance of georeferenced data within each plot

Crop models can improve our understanding of crop responses to environmental conditions and farming practices. However, uncertainties in model inputs can notably impact the quality of the outputs. This study aimed at quantifying the uncertainty in soil information and analyse how it propagates through the Quantitative Evaluation of Fertility of Tropical Soils model to affect yield and fertilizer recommendation

Investigating soil properties and yield variability in farming systems is crucial for delineating Management Zones (MZs). The objectives of study were to investigate the spatiotemporal variability of soil properties, identify spatial and temporal yield-limiting factors of soil and delineate MZs based on these factors. This study was conducted at the Xinghua Rice Smart Farm (33.08°E, 119.98°N) in Jiangsu

Rapid rangeland monitoring is critical for implementing management actions effectively and therefore, various remote sensing methods are used for rangeland monitoring. Prices of high-resolution imagery and cloud problems could avoid practicing satellite based-methods. UAV- or ground-based high resolution RGB imagery suggested as an alternative to monitor rangelands. In this study, the performance of

Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSIuni) calculation assumes N being the sole yield-limiting factor, neglecting