Precision Agriculture Data Research Articles

Drone-Assisted Climate Smart Agriculture (DACSA): The design of the groundwork flow data for drone operations

The success of precision farming hinges on effective ground support and workflow. In pursuit of this, we undertook a thorough requirement study of the system necessary for precision farming and developed a precision farming data flow model in ground support. The prototype hardware ground support and conceptual data flow provided valuable guidance in the successful realization of Drone-Assisted Climate Smart Agriculture (DACSA). Using open-source software to accommodate a range of data processing algorithms becomes crucial in operationalizing ground support for precision farming. This study has culminated in a comprehensive prototype model for precision farming operations that can be executed with confidence. The management system of flow data for precision farming has been drawn, this platform is specifically crafted to streamline agriculture operations by transforming diverse inputs into useful spatial data. To maintain the growth of the database, it is necessary to incorporate it in the entire crop cycle. The integration of this database can significantly enhance the precision of predicting plant performance. While this innovative approach is still in progress, it has already demonstrated its potential in supporting informed decision-making. For the next, it is imperative that we prioritize research aimed at creating decision-support algorithms that can effectively gather and blend information pertaining to soil, crops, and weather into actionable maps. These maps must incorporate location-specific data and be utilized by agricultural professionals for on-site decision-making. Moreover, they must be well-suited for drone usage in tasks such as monitoring, mapping, or spraying.

UAV hyperspectral image acquisition and processing, an application for nutrient estimation of rice in Vietnam

Hyperspectral imagery obtained from Unmanned Aerial Vehicles (UAVs) is increasingly employed to investigate nutrient concentrations in vegetation. The deployment of a hyperspectral camera on a UAV, flight planning, image acquisition, preprocessing of hyperspectral data, and the subsequent estimation of nutrient concentrations in vegetation are facing challenges. These challenges manifest as geometric, spectral distortions, and the abundance of numerous spectral bands. This study seeks to guide on mitigating the impact of issues encountered during an experiment to estimate nutrient concentrations in rice leaves using UAV hyperspectral images. An industrial hexagonal drone equipped with a push-broom hyperspectral camera featuring 122 bands within the Visible to Near-Infrared (VIS-NIR) wavelength range (400-960 nm) is employed to collect data over a 1-hectare testing rice field. Models for estimating Leaf Phosphorus Concentration (LPC) and Leaf Potassium Concentration (LKC) are developed based on the correlation between hyperspectral images, characterized by a 3 cm spatial resolution, and 162 LPC and 162 LKC reference data points. Utilizing various vegetation indices for LPC and LKC estimation, the outcomes reveal that a combination of band wavelengths at 838 nm and 734 nm is effective for LPC estimation, yielding a Root Mean Square Error (RMSE) of 27.1%. Conversely, LKC estimation exhibits an RMSE of 38.8% with an insignificant correlation between LKC and the current wavelength ranges. Above all, this study is a primary example of the utilization of UAV hyperspectral data in precision agriculture in Vietnam.

Smart Sensors and Smart Data for Precision Agriculture: A Review.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.

The role of big data in advancing precision agriculture and ensuring food security

The advent of big data analytics is revolutionizing the agro-industrial complex, particularly in the realm of precision agriculture. This article explores how big data technologies are advancing precision farming practices and contributing to global food security. By integrating data from diverse sources such as satellite imagery, weather stations, soil sensors, and farm management systems, big data analytics enables farmers to make informed decisions that optimize resource use, enhance crop yields, and reduce environmental impact. This study examines key applications of big data in precision agriculture, including predictive modeling for crop health, real-time monitoring of soil conditions, and data-driven pest management strategies. Furthermore, it discusses the socio-economic benefits of big data adoption, such as increased efficiency, cost savings, and improved resilience against climate variability. Case studies from various regions highlight successful implementations and the resulting improvements in agricultural productivity and sustainability. The findings underscore the critical role of big data in transforming agricultural practices and ensuring food security in the face of growing global challenges. By leveraging the power of big data, the agro-industrial complex can achieve a more sustainable and secure future, addressing the needs of a rapidly expanding population.

Retrieving decametric-resolution leaf chlorophyll content from GF-6 WFV by assessing the applicability of red-edge vegetation indices

Accurate and timely retrieval of leaf chlorophyll content (LCC) at fine spatial scales is crucial for monitoring plants' photosynthetic capacity and nutritional status. The wide field-of-view camera (WFV) onboard Gaofen-6 (GF-6) satellite has two red-edge bands at 16-meter spatial resolution, providing an opportunity to derive decametric-resolution LCC. However, the potential of GF-6 WFV data in LCC estimation has yet to be studied. Vegetation indices (VIs) are widely used in LCC estimation due to their simplicity and efficiency, and the accuracy of LCC inversion can be improved with red-edge VIs. This study focused on exploring the retrieval of LCC with GF-6 WFV using the VI-based method by assessing the applicability of nine red-edge VIs derived from GF-6 WFV. Our results revealed that (1) the newly developed canopy structure-insensitive CSI (chlorophyll sensitive index) achieved comparable accuracy to the physically-based method and reduced the limitation of the VI-based method being species- and time-specific. Validation results showed that the root-mean-square error (RMSE) of the estimated LCC from the CSI-based model was 7.91–11.38 μg/cm2 for four plant functional types (similar accuracy to Croft et al. (2020) based on the lookup table method with RMSE of 9.25–13.18 μg/cm2). The accuracy of the CSI-based model remained stable across species and growth conditions, indicating its feasibility for large-scale applications. (2) GF-6 WFV provided accurate LCC estimates and well-captured the spatial and seasonal variations in LCC. GF-6 LCC maps showed comparable accuracy (R2 = 0.53, RMSE = 9.78 μg/cm2) and similar spatiotemporal distribution to the 30-m Sentinel-2 LCC products. Additionally, GF-6 LCC maps outperformed Sentinel-2 LCC products regarding observation frequency and spatial continuity. Our study highlights the potential of GF-6 WFV in retrieving decametric-resolution LCC and provides further support for applications of GF-6 WFV data in precision agriculture and forestry.

Cyborg farmers: Embodied understandings of precision agriculture

AbstractPrecision agriculture is often seen as disembodied and placeless, promised to either bring about a fourth agricultural revolution or as the start of dystopian rural futures, where farmers and their knowledge will be replaced by machines. A growing body of literature shows more nuanced ways of working with precision agriculture. This illustrates the need to investigate how, and under which conditions, precision agriculture is integrated and internalised by farmers. Based on 25 in‐depth interviews with male Dutch crop farmers, contractors, researchers, and ag‐tech developers, we develop the concept of the cyborg farmer, who embodies the use of precision technology while maintaining an intimate relationship with agro‐ecological context. This approach challenges the mind–body dichotomy in that the cognitive is not understood as primary, as emotions and the materiality of the body are taken seriously. This perspective emphasises the importance of embodied knowledge in how farmers interpret precision agriculture data. Our findings highlight the productive tension between data‐driven elements of precision agriculture and the embodied and intuitive understanding of agro‐ecological context. This productive tension consists of the successful integration of different forms of knowledge by the cyborg farmer, where embodied sense‐making and precision agriculture data are integrated into the formation of useable knowledge. The cyborg farmer maintains farmer agency and resists the dominance of algorithmic rationality over other forms of knowledge .

Exploring drivers of within‐field crop yield variation using a national precision yield network

Abstract While abiotic drivers of yields represent important limiting factors to crop productivity, the role of biotic drivers that could be directly managed by farmers (e.g. agri‐environment schemes supporting key ecosystem services) remains poorly understood. Precision yield mapping provides an opportunity to understand the factors that limit agricultural yield through the interpretation of high‐resolution cropping data. This has the potential to inform future precision agricultural management, such as the targeted application of agrochemicals, promoting increased sustainability in modern agricultural systems. We used precision yield measurements from a network of 1174 fields in England (2006–2020) to identify drivers of within‐field yield variation in winter wheat and oilseed rape. Potential drivers included climate, topography and landscape composition and configuration. We then explored relationships between in‐field yield patterns and local landscape context, including the presence of features associated with ecosystem benefits. Proximity to the field edge was associated with reduced yields in 85% of wheat and 87% of oilseed fields. This translating to an approximate reduction of 10% in wheat and 18% in oilseed yields lost due to field edge effects. We found evidence that reduced yields at the field edges were associated with biotic features of the surrounding landscape, including the occurrence of semi‐natural habitats. Specifically, agri‐environment scheme (AES) presence increased the rate at which yields at field edges approach those of the field centres. This suggests that AES occurrence within a landscape (rather than field adjacent) may increase edge effects. However, these trends are unclear and suggest interactions between drivers and the spatial and temporal scale of investigation. Synthesis and applications. While we found evidence of landscape context mitigating against field edge effects, these were counterintuitive. For example, AES at a landscape scale appeared to increase the severity of edge effects. This study highlights a lack of environmental data at sufficiently high spatiotemporal resolution to match that of precision agriculture data. This mismatch is hindering the effective integration of precision agriculture data in an environmental policy and/or management context and potentially leading to unnecessarily poorly informed decisions related to AES deployment. This may limit environmental and economic benefits.

The need for streamlining precision agriculture data in Africa

The need for streamlining precision agriculture data in Africa

Evaluating Management Practices in Precision Agriculture for Maize Yield with Spatial Econometrics

Precision agriculture (PA) aims to provide data on soil, nutrient use, irrigation, and crops, to guide management strategic decisions towards an efficient use of inputs, increasing production and avoiding environmental problems due to excessive accumulation of fertilizers. In this paper, PA data from a large Portuguese farm producing maize were used to assess the effectiveness of agronomic management decisions concerning fertilizer and nutrient use, seed choice, and water content, in terms of crop productivity. The maize yield in 2017 and 2018 was modelled as a function of manageable inputs and unmanageable factors introduced as control variables. Panel spatial econometric methods were used for specification and estimation, to control for spatial dependence and spatial heterogeneity. The model proved to fit the data remarkably well and could be a good reference for specifying models to explain maize production; thus, helping researchers who need to deal with the huge amount of data that normally originates from PA. Additionally, it can be considered another tool for farm managers, helping in the design and evaluation of their agronomic management decisions.

Potential of Gpr Data Fusion with Hyperspectral Data for Precision Agriculture of the Future

Potential of Gpr Data Fusion with Hyperspectral Data for Precision Agriculture of the Future

Sentinel-2 Data for Precision Agriculture?-A UAV-Based Assessment.

An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R2 = 0.82, mean absolute error = 0.52 m2/m2). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture.

Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard

In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales.

Recent Trends of Big Data in Precision Agriculture: a Review

Recent developments in the field of technology have led to a renewed interest in the field of smart agriculture. The current smart agricultural system produces and depends on large amounts of data, yet, it is hard to process the vast amounts of data using traditional data analysis systems. Big Data technologies have attracted much attention among researchers due to their potential to handle large amounts of data. Thus, for numerous possibilities and powerful data processing capabilities, Big Data continues to become a hot topic of research in the agricultural field. This is indicated by the existing several pilot projects as well as various investigations that have been performed in the Big Data area. Therefore, this study attempts to review the recent trends of Big Data technology in the field of precision agriculture. The research focuses on the latest applications of Big Data technology in multi-disciplinary agri-area domains. A total of 25 recently published studies about Big Data in agri-areas were selected for the review process after proper screening. The selected papers were categorized according to their fields of study, purposes, methods applied, as well as their contributions in the agri-field. The findings indicated that Big Data application areas are expanding, and the impacts are significant in precision agriculture.

Unmanned Aerial Vehicle-Based Multispectral Remote Sensing for Commercially Important Aromatic Crops in India for Its Efficient Monitoring and Management

Aromatic plants cultivation, processing and marketing is an upcoming agro-industry. The yields from these plants are generally governed by its good management practices of timely, suitable and precise actions against damaging factors. Remote sensing in agriculture is not a new phenomenon anymore, but using unmanned aerial vehicle (UAV, commonly known as drones) for the same is a pertinent topic these days, especially in India. Therefore, the study seeks to perform UAV-based airborne data acquisition, processing and analysis for modernised agricultural practices, finding of which may lead to generate rapid and on-demand real-time remotely sensed data for precision agriculture of commercial crops, which require more care and timely inputs as compared to conventional crops. The UAV high-resolution (1.5 cm/pixel) data were acquired from Mica Sense Altum, a 6 bands multispectral sensor, mounted over an indigenous Quad-copter (< 5 kg). With the help of processed orthoimage, the 22 plots of Rosa damascena (Damask Rose) were precisely (95% accuracy) classified into 03 categories, i.e., rose canopy, weed and open soil areas. We have also estimated digital plant count, plant height derived from canopy height model (CHM), canopy temperature and the topographic conditions of the crop plots. The digital plant counting for R. damascena planted in 4323 m2 area took 1.2 h as compared to manual 5.94 h counting. Average plant height values derived from CHM ranged from 23–68 cm as compared to 28–71 cm manually measured heights. Results were compared with ground sampling data, with which high correlation was found in digital plant count (R2 = 0.99) and plant height (96.69% accuracy). The derived average moderate slopes and northeast aspect suggested suitable topographic conditions required for R. damascena cultivation. The image-derived canopy temperature was compared to the relative ground-based measurements, obtaining accuracy percent of 98.54%. The outcomes are encouraging and have potential to be applied for future UAV grounded applications by farmhands.

Big Data and AI Revolution in Precision Agriculture: Survey and Challenges

Sustainable agricultural development is a significant solution with fast population development through the use of information and communication (ICT) in precision agriculture, which produced new methods for making cultivation further productive, proficient, well-regulated while preserving the climate. Big data (machine learning, deep learning, etc.) is amongst the vital technologies of ICT employed in precision agriculture for their huge data analytical capabilities to abstract significant information and to assist agricultural practitioners to comprehend well farming practices and take precise decisions. The main goal of this article is to acquire an awareness of the Big Data latest applications in smart agriculture and be acquainted with related social and financial challenges to be concentrated on. This article features data creation methods, accessibility of technology, accessibility of devices, software tools, and data analytic methods, and appropriate applications of big data in precision agriculture. Besides, there are still a few challenges that come across the widespread implementation of big data technology in agriculture.

A cloud-based prototype for the monitoring and predicting of data in precision agriculture based on internet of everything

To empower knowledgeable resolution and to gratify informational needs, farmers obligate agricultural information and appropriate knowledge. In India, day by day, the agriculture sector is diminishing, and it devastates the mass production ability of the ecosystem. It is a crucial need to resolve the complication in the sector to reimpose vibrancy and place it back on elevated progress. With the aim of to offer superior agricultural development, the Internet of Everything (IoE) has been developed and accomplished that recognize agricultural habitat data gatherings like soil moisture, humidity, light and temperature. Every sensor node can transform tracking information into the cloud. Data mining principles are utilized with takes cause of discovering communicative systems in the atmospheric constraints recorded by the sensor network. The implementation of this exploration was carried as a use case within the farmhouse in Chennai, India. This paper, we are presenting a formal provision of Peer-to-Peer Central- Registry biased Internet of Everything Protocol (P2PRioEP) which is an application medium registry for hybrid peer-to-peer IoT networks. The results are obtained over a duration and discussed.

Dry biomass estimation of paddy rice with Sentinel-1A satellite data using machine learning regression algorithms

Despite the growing use of Sentinel-1 in retrieving crop growth information, its potential for obtaining improved estimates of rice biophysical parameters has not been fully investigated. This study therefore assesses the capabilities of Sentinel-1A temporal datasets, of the interferometric wide-swath (IW) mode that include the vertical transmitted and horizontal received (VH), and vertical transmitted and vertical received (VV) polarizations, and the linear combination of VH and VV (VHVV), to estimate rice dry biomass over a test site located in southeast China. To this end, four machine learning regression algorithms; Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), were applied to assess the aforementioned datasets. The results indicated that for the estimation of rice dry biomass over the entire growing season, VHVV data with RF produced the most accurate estimates with an R2 of 0.73 and an RMSE of 462.4 g/m2. However, this study suggests that a consideration of the rice growth phases could produce more accurate estimates of dry biomass as evident by an R2 of 0.72 and an RMSE of 362.4 g/m2 recorded at the reproductive phase (elongation to milking) with VH data and k-NN. Hence research works that would investigate the estimation of rice dry biomass at the vegetative (transplanting to elongation) and maturity (milking to ripening) phases are required to ascertain whether a growth-phase approach could obtain more accurate rice biomass estimates that better meet precision agriculture data needs.

FastMapping: Software to create field maps and identify management zones in precision agriculture

Diverse on-farm agronomic data, gathered via precision agriculture (PA), require depuration and joint analysis of multiple georeferenced field characteristics such as crop yields, and soil and topographic aspects. Multivariate analysis of spatial variability has been recommended to understand variation of data within fields and classify sites into zones of broad similarity to support crop management. However, most of the statistical techniques available for spatial data require advanced skills. Cleaning and statistical analysis of big spatial data can only be effectively implemented if there are easy-to-use computer programs integrating the analytical steps. This paper explains the development and implementation of FastMapping, an interactive web application to automatically clean PA raw data, generate spatial variability field maps, and delineate multivariate management zones. The application uses an interface developed in R language that automatically depurates datafiles; in addition, through automatic spatial interpolation of each variable, it allows us to merge data layers on the same spatial grid in such a way that each site within the agricultural field has values of all the measured variables. On this grid, FastMapping identifies homogeneous zones, in a multivariate and spatial way, and provides data reports including validation of the delineated zones. FastMapping outputs for zone delineation were compared with results from Management Zone Analyst (MZA) software in several fields. The new software yielded a similar zoning to that of MZA and added graphs and statistical results that are lacking in most PA software tools. The flexibility of FastMapping to import, clean and visualize PA data in a single computer environment is highly encouraging. FastMapping can be used by the PA community to support site-specific agricultural management.

UAV-based Multispectral &amp; Thermal dataset for exploring the diurnal variability, radiometric &amp; geometric accuracy for precision agriculture

To explore the diurnal variations, radiometric and geometric accuracy of UAV-based data for precision agriculture, a comprehensive dataset was created in a one-day field campaign (21 June 2017). The multi-sensor data set covers wheat, barley &amp; potato experimental fields, located in Wageningen University and Research (WUR) farm maintained by Unifarm. UAV-based images were collected with several sensors over the experimental area, starting from 7:25am and ending at 20:00pm local solar time. The dataset consists of images collected by 9 flights with senseFly MSP4C, 9 with Parrot Sequoia, 2 with Slant Range P3, 5 with DJI Zenmuse X3 NIR, 4 with the senseFly Thermo-map and 1 with the RGB Sony WX-220. Additionally, validation measurements at radiometric calibration plates and plant sample locations were taken with a Cropscan handheld spectrometer and a tec5 Handyspec spectrometer. The dataset consists of the validation measurements, the raw images and the processed orthomosaics (both with and without geometric correction).

Big Data Challenges and Opportunities in Agriculture

This article reviews various aspects of research concerning the background and state-of-the-art of big data in agriculture. This article focuses on data generation, storage, analysis and visualization in big data. In every phase, technical challenges and the latest advancement are discussed, and these discussions aim to provide a comprehensive overview and complete picture of this exciting area. This survey is concluded with a discussion on the application of big data in precision agriculture and its future directions.