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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Precision Agriculture using Networks of Degradable Analytical Sensors (PANDAS) (Final Technical Report)

Precision agriculture, where sensing of soil, environment and crop conditions are used to precisely synchronize inputs (such as water and fertilizer) to crop needs enhances input use efficiency. This can improve yields and farm profitability while mitigating environmental losses, improving soil carbon content and substantially decreasing energy use for food, feed and fuel crops. Unfortunately, farmers are not yet able to harness the full potential of these management technologies as there is a lack of available management information, and there is therefore a need for sensors that are able to economically measure spatio-temporal variability in soil and crop properties of extremely heterogeneous farm fields precisely at high resolution and at low cost. Real-time, in-situ monitoring of agricultural soil conditions is today carried out using devices that limit the total number of nodes that can be used economically to typically one per acre or less. Higher spatio-temporal resolution sensing would enable more precise agricultural input optimization, with significant benefits to the farmer and the environment. In order to address this issue, this project focused on developing additively manufactured, biodegradable, soil sensors with predicted costs of < $\$$1 per unit to monitor crop inputs (such as water and fertilizer) that predictably, harmlessly degrade away into the soil when no longer needed. These sensor nodes should be easy to place, accurately and continuously monitor soil and crop conditions for an entire season, be read remotely using existing farm equipment, require no ongoing maintenance, not impede farm operations and produce no persistent waste. This approach could enable a >100× increase in information density over current solutions for precision farming of row and other crops, and lead to significant reductions in input energy use and provide increased yield for biofuel crops. Over the course of this project the team at the University of Colorado Boulder, University of California Berkeley, and Colorado State University/Kansas State University investigated a wide range of printable biodegradable electronic materials and sensor designs for determining soil moisture and soil nitrate concentration. These efforts expanded the available materials set for printed soil degradable electronic materials, particularly for conductors, enabling high conductivity and stability. Printed soil moisture and nitrate sensors with suitable sensitivity and selectivity were developed and characterized. Low power and passive wireless electronic systems were integrated with the soil sensors, and testing was carried out with completed sensors to understand their functionality under agricultural conditions. Additionally, other sensor types enabled by the biodegradable materials set created during this project, such as soil microbial activity sensors, were also developed and demonstrated. Project outputs include 10 peer reviewed publications, 4 patent applications, 21 technical presentations, 3 PhD thesis, 10 media reports, 8 additional grants worth over $\$$6M, and the formation of 3 start-up companies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2016

Safety and Certification Considerations for Expanding the Use of UAS in Precision Agriculture

The agricultural community is actively engaged in adopting new technologies such as unmanned aircraft systems (UAS) to help assess the condition of crops and develop appropriate treatment plans. In the United States, agricultural use of UAS has largely been limited to small UAS, generally weighing less than 55 lb and operating within the line of sight of a remote pilot. A variety of small UAS are being used to monitor and map crops, while only a few are being used to apply agricultural inputs based on the results of remote sensing. Larger UAS with substantial payload capacity could provide an option for site-specific application of agricultural inputs in a timely fashion, without substantive damage to the crops or soil. A recent study by the National Aeronautics and Space Administration (NASA) investigated certification requirements needed to enable the use of larger UAS to support the precision agriculture industry. This paper provides a brief introduction to aircraft certification relevant to agricultural UAS, an overview of and results from the NASA study, and a discussion of how those results might affect the precision agriculture community. Specific topics of interest include business model considerations for unmanned aerial applicators and a comparison with current means of variable rate application. The intent of the paper is to inform the precision agriculture community of evolving technologies that will enable broader use of unmanned vehicles to reduce costs, reduce environmental impacts, and enhance yield, especially for specialty crops that are grown on small to medium size farms.

Hayhurst, Kelly J.↗
NASA NTRS2015

Mock Certification Basis for an Unmanned Rotorcraft for Precision Agricultural Spraying

This technical report presents the results of a case study using a hazard-based approach to develop preliminary design and performance criteria for an unmanned agricultural rotorcraft requiring airworthiness certification. This case study is one of the first in the public domain to examine design and performance criteria for an unmanned aircraft system (UAS) in tandem with its concept of operations. The case study results are intended to support development of airworthiness standards that could form a minimum safety baseline for midsize unmanned rotorcraft performing precision agricultural spraying operations under beyond visual line-of-sight conditions in a rural environment. This study investigates the applicability of current methods, processes, and standards for assuring airworthiness of conventionally piloted (manned) aircraft to assuring the airworthiness of UAS. The study started with the development of a detailed concept of operations for precision agricultural spraying with an unmanned rotorcraft (pp. 5-18). The concept of operations in conjunction with a specimen unmanned rotorcraft were used to develop an operational context and a list of relevant hazards (p. 22). Minimum design and performance requirements necessary to mitigate the hazards provide the foundation of a proposed (or mock) type certification basis. A type certification basis specifies the applicable standards an applicant must show compliance with to receive regulatory approval. A detailed analysis of the current airworthiness regulations for normal-category rotorcraft (14 Code of Federal Regulations, Part 27) was performed. Each Part 27 regulation was evaluated to determine whether it mitigated one of the relevant hazards for the specimen UAS. Those regulations that did were included in the initial core of the type certification basis (pp. 26-31) as written or with some simple modifications. Those regulations that did not mitigate a recognized hazard were excluded from the certification basis. The remaining regulations were applicable in intent, but the text could not be easily tailored. Those regulations were addressed in separate issue papers. Exploiting established regulations avoids the difficult task of generating and interpreting novel requirements, through the use of acceptable, standardized language. The rationale for the disposition of the regulations was assessed and captured (pp. 58-115). The core basis was then augmented by generating additional requirements (pp. 38-47) to mitigate hazards for an unmanned sprayer that are not covered in Part 27.

Hayhurst, Kelly J.↗
NASA NTRS2003

Precision Agriculture: Changing the Face of Farming

To a large extent our work has grown out of the remote sensing technology and conceptual framework developed by geologists. For example the drive to look at the physics of reflectance and atmospheric corrections is rooted in work done in the early 1980s by the United States Geological Survey and NASA. Our work on emissivity and thermal behavior of plants pulls on research done using the Thermal Infrared Multispectral Scanner, an instrument originally conceived for geologic applications. Even our ability to geometrically map the airborne imagery onto the globe was explicitly developed because of need to map sediment flow patterns in along the coast of Louisiana. Growing from this base we have learned much in the last few years and believe our integration of geologic remote sensing with the other fields of expertise was a wise investment. Clearly none of the specialties alone could develop, let alone test, the basic approach we are now finding so powerful. This is the path that will ultimately give the information needed by the farmer. We also recognize how small a portion of the total problem has been solved. Having developed the basic logic, built proto-type tools and performed initial tests everything else remains to be done. And problems, scientific and practical, are everywhere. We have not established sensitivities. We have not robustly segregated the contributions of crop residue, soil moisture, shadows, plant and soil to the energy leaving the surface. What we do is extremely expensive and difficult. It is experimental in methodology and uses research oriented tools. We are constantly alive to the practicality of moving our results into commercial applications. We know another airborne instrument will have to be available. Atmospheric parameters will have to be measured automatically. The software will have to be re-written for speed. At times the list of problems seems endless. But the potential is also enormous. Agriculture is a huge portion of our economy. Just a one percent increase in efficiency is a $2,000,000 change. We all depend on farmers, literally for the bread we eat. No other activity of man even has an impact on the land that farming has. If application of precision agriculture can nelp farmers manage their land better, we all may benefit.

Rickman, D.↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1998

High Resolution Airborne Digital Imagery for Precision Agriculture

The Environmental Research Aircraft and Sensor Technology (ERAST) program is a NASA initiative that seeks to demonstrate the application of cost-effective aircraft and sensor technology to private commercial ventures. In 1997-98, a series of flight-demonstrations and image acquisition efforts were conducted over the Hawaiian Islands using a remotely-piloted solar- powered platform (Pathfinder) and a fixed-wing piloted aircraft (Navajo) equipped with a Kodak DCS450 CIR (color infrared) digital camera. As an ERAST Science Team Member, I defined a set of flight lines over the largest coffee plantation in Hawaii: the Kauai Coffee Company's 4,000 acre Koloa Estate. Past studies have demonstrated the applications of airborne digital imaging to agricultural management. Few studies have examined the usefulness of high resolution airborne multispectral imagery with 10 cm pixel sizes. The Kodak digital camera integrated with ERAST's Airborne Real Time Imaging System (ARTIS) which generated multiband CCD images consisting of 6 x 106 pixel elements. At the designated flight altitude of 1,000 feet over the coffee plantation, pixel size was 10 cm. The study involved the analysis of imagery acquired on 5 March 1998 for the detection of anomalous reflectance values and for the definition of spectral signatures as indicators of tree vigor and treatment effectiveness (e.g., drip irrigation; fertilizer application).

Herwitz, Stanley R.↗
NASA NTRS1999

The Design of a Remote Sensing Data Acquisition Campaign for Precision Agriculture and Some Early Results

In the 1970s NASA and the Department of Agriculture attempted to use the new Landsat MSS system for agricultural purposes. The program had relatively little success. With the advent of differential GPS, yield monitors on harvest equipment and higher spatial resolution remote sensing systems it seemed likely the situation should be reexamined. Therefore, a campaign of data acquisition involving remote sensing and other modalities with dependent research was assembled and funded by the Space Grant Consortia in Alabama and Georgia. The design of the remote sensing data acquisition was driven by the biology and physics of the crop system and limited by the available sensor platforms. Major parameters included crop stage, spatial resolution, seasonal and daily weather conditions, and which portion of the EM spectrum would actually capture the most discriminating information. Joint visible and Near IR with Thermal IR would permit use of existing indices, such as greenness, as well as phenomena driven by the plant' s evapotranspiration. Spatial resolution in the 2-5 meter range was chosen, avoiding many complexities caused by aliasing crop row spacing at, higher resolutions yet finer than the harvester's tightest recording rate. This dictates use of an airborne system. Use of an airborne system also makes scheduling around weather much simpler than use of satellite data. Active video calibration was recognized as essential if quantitative measures were ever to be obtained or reproduced. The system would also have to have onboard geoOF1 Based on these elements 3 data acquisitions have been flown. Seven flight lines were flown twice in 1998 and 16 lines flown in 1999. Total raw data is several GBytes. All of the data has now been geometrically corrected and some preliminary analysis accomplished. The thermal bands have an extremely high correlation with yield. For one@test case with corn, correlation in excess of 0.86 was obtained from a data acquisition two months prior to harvest! Soil images show significant within field variation in clay, soil brightness and emissivity. Light wind has been found to effect the reflectance and temperature of broad leaf crops, including soybeans, cotton and peanuts. Clearly, this work has already demonstrated some very important results. With continued development of the remote sensing technology there is good reason to believe this research will soon be able to help the individual farmer.

Rickmanl, D.↗
NASA NTRS2016

Expanding the Envelope of UAS Certification: What it Takes to Type Certify a UAS for Precision Agricultural Spraying

One of the key challenges to the development of a commercial Unmanned Air-craft System (UAS) market is the lack of explicit consideration of UAS in the current regulatory framework. Despite recent progress, additional steps are needed to enable broad UAS types and operational models. This paper discusses recent research that examines how a risk-based approach for safety might change the process and substance of airworthiness requirements for UAS. The project proposed risk-centric airworthiness requirements for a midsize un-manned rotorcraft used for agricultural spraying and also identified factors that may contribute to distinguishing safety risk among different UAS types and operational concepts. Lessons learned regarding how a risk-based approach can expand the envelope of UAS certification are discussed.

Maddalon, J. M.↗
NASA NTRS2023

Farming the Future: An Approach to Precision Agriculture through UAS-based Thermal Scanners

Food waste is a global issue, and it is extremely prevalent in the United States (U.S.) where one-third of the food produced goes to waste. The most common item found in landfills is food waste, which is why it is one of the most significant contributors to global warming with 8%. These negative environmental impacts also return to affect agriculture, as global warming changes climate patterns and adversely affects farming practices. In addition, the resources that were used to grow the produce are also wasted, including money, water, and time. To tackle this problem at its roots, this paper includes a design of an unmanned thermal drone that can be a farmer’s third eye. This drone will scan over farms and provide data to the farmers about the health, ripeness, and moisture content of their crops through mounted thermal cameras and artificial intelligence. The information provided via these drones will help farmers to make more informed decisions about their crops, decreasing the chances that the crops will go bad in the field. These drones will be one of the first steps in preventing food waste, which will, in turn, help to reduce global emissions that result from farming.

UAS supported agriculture↗
NASA NTRS2002

Remote Sensing and Information Technology for Large Farms

A method of applying of remote sensing (RS) and information management technology to help large farms produce at maximum efficiency is undergoing development. The novelty of the method does not lie in the concept of "precision agriculture," which involves variation of seeding, of application of chemicals, and of irrigation according to the spatially and temporally local variations in the growth stages and health of crops and in the chemical and physical conditions of soils. The novelty also does not lie in the use of RS data registered with other data in a geographic information system (GIS) to guide the use of precise agricultural techniques. Instead, the novelty lies in a systematic approach to overcoming obstacles that, heretofore, have impeded the timely distribution of reliable, relevant, and sufficient GIS data to support day-to-day, acre-to-acre decisions concerning the application of precise agricultural techniques to increase production and decrease cost. The development and promotion of the method are inspired in part by a vision of equipping farm machinery to accept GIS (including RS) data and using the data for automated or semiautomated implementation of precise agricultural techniques. Primary examples of relevant GIS data include information on plant stress, soil moisture, and effects of applied chemicals, all derived by automated computational analysis of measurements taken by one or more airborne spectroradiometers. Proper management and timeliness of the large amount of GIS information are of paramount concern in agriculture. Information on stresses and changes in crops is especially perishable and important to farmers. The need for timeliness and management of information is satisfied by use of computing hardware and software capable of (1) rapid georectification and other processing of RS data, (2) packaging the output data in the form of GIS plots, and (3) making the data available to farmers and other subscribers by Internet password access. It is a goal of this development program to make RS data available no later than the data after an aerial survey. In addition, data from prior surveys are kept in the data base. Farmers can, for example, use current and prior data to analyze changes.

Williams, John E.↗
NASA NTRS2003

Remote Sensing and Information Technology for Large Farms

A method of applying remote sensing (RS) and information-management technology to help large farms produce at maximum efficiency is undergoing development. The novelty of the method does not lie in the concept of precision agriculture, which involves variation of seeding, of application of chemicals, and of irrigation according to the spatially and temporally local variations in the growth stages and health of crops and in the chemical and physical conditions of soils. The novelty also does not lie in the use of RS data registered with other data in a geographic information system (GIS) to guide the use of precise agricultural techniques. Instead, the novelty lies in a systematic approach to overcoming obstacles that, heretofore, have impeded the timely distribution of reliable, relevant, and sufficient GIS data to support day-to-day, acre-to-acre decisions concerning the application of precise agricultural techniques to increase production and decrease cost. The development and promotion of the method are inspired in part by a vision of equipping farm machinery to accept GIS (including RS) data and using the data for automated or semi-automated implementation of precise agricultural techniques. Primary examples of relevant GIS data include information on plant stress, soil moisture, and effects of applied chemicals, all derived by automated computational analysis of measurements taken by one or more airborne spectroradiometers. Proper management and timeliness of the large amount of GIS information are of paramount concern in agriculture. Information on stresses and changes in crops is especially perishable and important to farmers. The need for timeliness and management of information is satisfied by use of computing hardware and software capable of (1) rapid geo-rectification and other processing of RS data, (2) packaging the output data in the form of GIS plots, and (3) making the data available to farmers and other subscribers by Internet password access. It is a goal of this development program to make RS data available no later than the data after an aerial survey. In addition, data from prior surveys are kept in the data base. Farmers can, for example, use current and prior data to analyze changes.

Williams, John E.↗
NASA NTRS2004

SAMZ: Satellite-Derived Management Zones

The term "satellite-derived management zones" (SAMZ) denotes agricultural management zones that are subdivisions of large fields and that are derived from images of the fields acquired by instruments aboard Earth orbiting satellites during approximately the past 15 years. "SAMZ" also denotes the methodology and the software that implements the methodology for creating such zones. The SAMZ approach is one of several products of continuing efforts to realize a concept of precision agriculture, which involves optimal variations in seeding, in application of chemicals, and in irrigation, plus decisions to farm or not to farm certain portions of fields, all in an effort to maximize profitability in view of spatial and temporal variations in the growth and health of crops and in the chemical and physical conditions of soils. As used here, "management zone" signifies, more precisely, a subdivision of a field within which the crop production behavior is regarded as homogeneous. From the perspective of precision agriculture, management zones are the smallest subdivisions between which the seeding, application of chemicals, and other management parameters are to be varied. In the SAMZ approach, the main sources of data are the archives of satellite imagery that have been collected over the years for diverse purposes. One of the main advantages afforded by the SAMZ approach is that the data in these archives can be reused for purposes of precision agriculture at low cost. De facto, these archives contain information on all sources of variability within a field, including weather, crop types, crop management, soil types, and water drainage patterns. The SAMZ methodology involves the establishment of a Web-based interface based on an algorithm that generates management zones automatically and quickly from archival satellite image data in response to requests from farmers. A farmer can make a request by either uploading data describing a field boundary to the Web site or else drawing the boundary on a reference image. Hence, a farmer can start to engage in precision farming shortly after gaining access to the Web site, without need for incurring the high costs of conventional precision-agriculture data-collection practices that include collecting soil samples, mapping electrical conductivity of soil, and compiling multi-year crop-yield data. Given the boundary of a field, a SAMZ server computes the zones within the field in a three-stage process. In the first stage, a vector-valued image of the field is constructed by assembling, from the archives, the equivalent of a stack of the available images of the field (see figure). In the second stage, the vector-valued image is analyzed by use of a wavelet transform that detects spatial variations considered significant for precision farming while suppressing small-scale heterogeneities that are regarded as insignificant. In the third stage, a segmentation algorithm assembles the zones from smaller regions that have been identified in the wavelet analysis.

Source record↗
NASA NTRS2005

Satellite-Derived Management Zones

The term "satellite-derived management zones" (SAMZ) denotes agricultural management zones that are subdivisions of large fields and that are derived from images of the fields acquired by instruments aboard Earth-orbiting satellites during approximately the past 15 years. "SAMZ" also denotes the methodology and the software that implements the methodology for creating such zones. The SAMZ approach is one of several products of continuing efforts to realize a concept of precision agriculture, which involves optimal variations in seeding, in application of chemicals, and in irrigation, plus decisions to farm or not to farm certain portions of fields, all in an effort to maximize profitability in view of spatial and temporal variations in the growth and health of crops, and in the chemical and physical conditions of soils. As used here, "management zone" signifies, more precisely, a subdivision of a field within which the crop-production behavior is regarded as homogeneous. From the perspective of precision agriculture, management zones are the smallest subdivisions between which the seeding, application of chemicals, and other management parameters are to be varied. In the SAMZ approach, the main sources of data are the archives of satellite imagery that have been collected over the years for diverse purposes. One of the main advantages afforded by the SAMZ approach is that the data in these archives can be reused for purposes of precision agriculture at low cost. De facto, these archives contain information on all sources of variability within a field, including weather, crop types, crop management, soil types, and water drainage patterns. The SAMZ methodology involves the establishment of a Web-based interface based on an algorithm that generates management zones automatically and quickly from archival satellite image data in response to requests from farmers. A farmer can make a request by either uploading data describing a field boundary to the Web site or else drawing the boundary on a reference image. Hence, a farmer can start to engage in precision farming shortly after gaining access to the Web site, without the need for incurring the high costs of conventional precision-agriculture data-collection practices that include collecting soil samples, mapping electrical conductivity of soil, and compiling multiyear crop-yield data. Given the boundary of a field, a SAMZ server computes the zones within the field in a three-stage process. In the first stage, a vector-valued image of the field is constructed by assembling, from the archives, the equivalent of a stack of the available images of the field (see figure). In the second stage, the vector-valued image is analyzed by use of a wavelet transform that detects spatial variations considered significant for precision farming while suppressing small-scale heterogeneities that are regarded as insignificant. In the third stage, a segmentation algorithm assembles the zones from smaller regions that have been identified in the wavelet analysis.

Lepoutre, Damien↗
NASA NTRS2005

2005 AG20/20 Annual Review

Topics covered include: Implementation and Validation of Sensor-Based Site-Specific Crop Management; Enhanced Management of Agricultural Perennial Systems (EMAPS) Using GIS and Remote Sensing; Validation and Application of Geospatial Information for Early Identification of Stress in Wheat; Adapting and Validating Precision Technologies for Cotton Production in the Mid-Southern United States - 2004 Progress Report; Development of a System to Automatically Geo-Rectify Images; Economics of Precision Agriculture Technologies in Cotton Production-AG 2020 Prescription Farming Automation Algorithms; Field Testing a Sensor-Based Applicator for Nitrogen and Phosphorus Application; Early Detection of Citrus Diseases Using Machine Vision and DGPS; Remote Sensing of Citrus Tree Stress Levels and Factors; Spectral-based Nitrogen Sensing for Citrus; Characterization of Tree Canopies; In-field Sensing of Shallow Water Tables and Hydromorphic Soils with an Electromagnetic Induction Profiler; Maintaining the Competitiveness of Tree Fruit Production Through Precision Agriculture; Modeling and Visualizing Terrain and Remote Sensing Data for Research and Education in Precision Agriculture; Thematic Soil Mapping and Crop-Based Strategies for Site-Specific Management; and Crop-Based Strategies for Site-Specific Management.

Ross, Kenton W.↗
DOE OSTI2025

Let’s Get Real: Are Wearable Plant Sensors Ready for Crop Monitoring?

In recent years, the number of publications describing new and exciting developments in wearable plant sensors (WPSs) has skyrocketed. These small, lightweight sensors hold promise to assist precision agriculture and may thus help reduce crop losses, increase resource use efficiency, and automate crop production. However, WPSs are often not adequately tested in environments relevant for crop growth, and the majority of experimental WPS studies reveal a glaring lack of basic knowledge of plant biology. This review aims to bridge the communication gap between WPS developers and the wider plant research community by (1) providing essential physiological and environmental background information for engineers in relation to WPS sensing capabilities, (2) offering a step-by-step guide to conduct sensor tests on plants correctly, and (3) highlighting potential challenges and suggesting WPS applications in the open field, greenhouses, and vertical farming systems. We hope this review facilitates the development of WPSs and guides them to be truly “ready for the world”.

crop monitoring↗
DOE OSTI2024

Integrated Urban Services: Program Impact and Business Plan Summary

The Integrated Urban Services (IUS) program, launched in 2021 and funded by the U.S. State Department under the United States-Association of Southeast Asian Nations (US-ASEAN) Smart Cities Partnership, aimed to bolster resilience in ASEAN cities by addressing challenges across food, energy, and water systems. Led by the National Renewable Energy Lab (NREL) with support from Regenerative Impact Ventures, the program focused on demonstrating the socio-economic benefits of integrated urban planning, educating stakeholders on circular economy principles, providing technical assistance to two ASEAN cities, and attracting private sector involvement. The program facilitated peer learning events, engaging public and private sector participants and leveraging knowledge from a group of global experts to inform approaches and best practices. Technical assistance was provided to two pilot cities, Iskandar Malaysia and Cagayan de Oro, Philippines, resulting in the development of market-driven business plans for resilient, circular, and regenerative energy-water-food system projects. The Iskandar Malaysia pilot focused on development of a state-of-the-art AgriTech Innovation Hub and Modern Farming Complex to enhance agricultural productivity and produce enough renewable energy to power the facilities. The Cagayan de Oro project aimed to enhance urban agricultural productivity and waste management through development of an Urban Precision Agricultural Complex featuring aeroponics, hydroponics, aquaponics, agrivoltaics, and a Black Solider Fly Facility for converting municipal solid waste into commodities. The success of the IUS program sets a precedent for replicating integrated urban service models globally, offering valuable insights for cities aiming to enhance their resilience and sustainability.

ASEAN↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗