Highlights Agricultural recordkeeping was made easy by integrating artificial intelligence. Generative AI enhances data collection with real-time validation. Metadata recorded by dynamically asked questions to the user. User experience indicates the effectiveness of recordkeeping using MetaAg 2.0. ABSTRACT. Generative Artificial Intelligence (Gen-AI) enables adaptive and context-aware human–computer interaction for structured data collection. To address limitations of manual farm recordkeeping, MetaAg, an Android-based application, was developed to automatically capture spatial and temporal information of farm operations using a rule-based chatbot for metadata entry. In this work, the system was extended through a retrieval-augmented generative AI framework to improve the accuracy and completeness of metadata recording. The framework leverages Gen-AI through API-based interaction to dynamically generate context-aware queries and validate user inputs using a backend knowledge base. MetaAg 2.0 was implemented as a prototype system and evaluated using multiple LLMs, including GPT-4o-mini (GPT), lfm-2.5-1.2b-thinking (Liquid), and Deepseek-R1-0528 (Deepseek). Results show that the proposed system supports efficient recordkeeping with reduced user interaction load and improved interaction performance. Comparative analysis indicates that GPT-4o-mini consistently outperforms Liquid and Deepseek in terms of lower interaction time, reduced wait burden, and smoother user interaction. User experience evaluation yielded a usability index (UIX) of 86.2 ± 6.1, indicating high user acceptance. Qualitative feedback further highlights strengths in ease of use and efficient data capture, while identifying limitations in interaction rigidity, interface layout, and error correction flexibility. These findings demonstrate that the proposed framework enables accurate and efficient metadata collection while maintaining strong usability, providing a scalable approach for intelligent agricultural data management. Keywords: Chatbot, Database, Digital records, Farm metadata, Gen-AI, Human-AI interaction, Human-computer interaction.

Digital agriculture is emerging as the next green revolution, helping farmers to make data-informed decisions that increase productivity and optimize resource use. Understanding farmers' perceptions of current barriers and future opportunities for technology adoption is necessary for sustainable agriculture. This study aimed to identify trends and patterns in farmers' perceptions of digital agriculture technology adoption. A survey was distributed across most of the Midwest and adjacent region of the United States, collecting 247 responses. Results showed 93% used digital agricultural technology, with long-term (>10 years) adoption of auto-guidance (59%), yield mapping (56%), and variable rate technologies (36%). Perceived financial profitability was a key driver of technology adoption (36%), followed by input optimization (18%) and productivity (16%). The main barrier was high cost relative to perceived benefit (31%), followed by small farm size (16%) and equipment incompatibility (14%). Environmental benefits emerged as a tertiary motivator, and their lower prioritization suggests that farmers focus more on the economic dimension of sustainability when adopting new technologies. Fertilizer efficiency (27%), pest management (18%), and water management (13%) were top challenges to address. Findings suggest that digital agriculture adoption is primarily driven by economic considerations, with cost-benefit analysis and entry costs as key determinants in US Midwest and adjacent region farming systems.

One of the most intriguing 6G vertical markets is precision agriculture, where communications, sensing, control, and robotics technologies are used to improve agricultural outputs and decrease environmental impact. Ambient IoT (A-IoT), which uses a network of devices that harvest ambient energy to enable communications, is expected to play an important role in agricultural use cases due to its low costs, simplicity, and battery-free (or battery-assisted) operation. In this article, we review the use cases of precision agriculture and discuss the challenges. We discuss how A-IoT can be used for precision agriculture and compare it with other ambient energy source technologies. We also discuss research directions related to both A-IoT and precision agriculture.

Crop yield prediction is critical for agricultural insurance, better risk management, and efficient production strategies. In this study, we propose a novel machine-learning framework to predict county-level corn yield using daily weather data, weather-derived features based on zigzag topology persistence, and static soil information. Our approach integrates a Long Short-Term Memory (LSTM) network to capture sequential weather patterns and a shallow feed-forward network for yearly weather topology persistence. Two network outputs are concatenated with soil data to feed into a deep feed-forward network optimized to predict the corn yield at a county level for each year. The model was trained on datasets of 1391 county-year pairs and tested on 50 pairs. Performance was compared to a Convolutional Graph Neural Network (CGNN), a Transformer, Support Vector Regression (SVR) with a radial basis function (SVR-RBF), Extreme Gradient Boosting (XGBoost), and combined Vector Autoregression-SVR (VAR-SVR) models. Among the models, the proposed LSTM-DNN and CGNN models excelled. The goodness of fit (R2), root mean square error (RMSE, Mg/ha), mean absolute error (MAE, Mg/ha), and mean absolute percentage error (MAPE, %) were 0.79, 21.2, 15.5, and 5.49% for the LSTM-DNN model, whereas the nearest competitor, the CGNN model, performed those metrics as 0.65, 22.2, 17.4, and 7.09, respectively. The LSTM-DNN model’s accuracy and computational simplicity were slightly better than the CGNN model. The LSTM-DNN model explained around 79% of the county average yield variability from weather and soil data. The MAPE of 5.49% from observed yields reflects a reliable tool for estimating the yield. Future studies could fine-tune the model with more accurate and high-resolution data instead of county-level records.

Wireless Underground Sensor Networks (WUSNs) have potential applications in precision agriculture for monitoring variables such as soil moisture, temperature, and nutrient levels. These systems offer highly scalable sensor networks, batteryless operation, and minimal environmental disruption. Radio Frequency Identification (RFID) technology can be used to enable communication with passive sensing devices in various scenarios. This work utilizes the OATSMobile platform to enable RFID communications with WUSNs. The platform consists of a rough terrain vehicle, an interrogation device, and environmental sensing devices in a testbed setup. OATSMobile records and transmits sensor data streams to remote computing nodes over the internet via a cellular network connection. Using a data pipeline based on the Avena software framework, sensor reading events from passive buried transponders are captured to evaluate the feasibility of passive RFID communications with WUSNs. A measurement campaign was conducted with 194 buried transponders at depths of 2.5, 5, 7.5, 10, and 30 cm, resulting in the automated recording of 8,973 reading events at a remote computing device.

Modern agricultural systems produce high-resolution data from remote sensing platforms, in-field sensors, and augmented machinery. However, these datasets often lack contextual information which hinders their utility in decision support systems and limits their applicability for AI-based modeling capacity. Digital metadata—the who, what, where, when, and how of field operations—are essential to transform other “layers of” raw data into actionable and interoperable agricultural knowledge. This paper presents Meta Ag, a smartphone-based metadata collection framework designed to improve the accuracy, completeness, and contextual richness of agricultural field records. The developed Android app integrates automated geofence-based event detection, operator identification, temporal logging, and structured input via dynamic interface and data validation elements. Its modular architecture supports authentication, automatic context generation, real-time validation, and centralized cloud storage. Meta Ag facilitates interoperability by exporting records in CSV, JSON, and RDF (Resource Description Framework) formats. Field evaluations show that the duration captured by Meta Ag differed from the actual recorded duration with a Root Mean Squared Error (RMSE) of 24.7s (range of 0s to 61s) and Meta Ag consistently detected all field access events via geofence triggers. These results highlight its effectiveness as a deployable, efficient solution for agricultural metadata collection. By reducing human error and supporting standardized, high-integrity recordkeeping, the Meta Ag framework enables the production of AI-ready metadata critical for digital agriculture applications.

Weather is a principal driver of agricultural yield and exhibits both spatial and temporal heterogeneity with quasiperiodic behavior. This study integrates statistical summaries and topological features of weather time series to explain county-level corn yields in Indiana state of the US. Using persistent homology, we demonstrate that the geometric structure of weather data—capturing connected components and loops—varies across counties and years, and is highly sensitive to the temporal framing (e.g., full year vs. cropping season). Including daily extremes (maximum and minimum temperature) significantly enhances the topological signal compared to mean-based representations. Combining pattern descriptors–mean, standard deviation, entropy, low-frequency power, maximum jump–with topological invariants, i.e., connected components (H0 as distinct weather events) and loops (H1 as cyclical transitions in weather dynamics) counts, mean H1 persistence, and persistence gap, a machine learning model (CatBoost regression) achieved R2 = 0.59 and MAPE = 10.31% explaining 59% of the yield variability. Sensitivity analysis identified topological features—particularly the counts of H0, H1, and mean H1 persistence—as significant contributors to yield explanation, underscoring the value of topological structure in modeling agro-meteorological interactions.

Subsurface soil moisture is a primary determinant for root development and nutrient transportation in the soil and affects the tractability of agricultural vehicles. A statistical forecasting model, Vector AutoRegression (VAR), and a Long Short-Term Memory network (LSTM) were developed to forecast the subsurface soil moisture at a 20 cm depth using 9 years of historical weather data and subsurface soil moisture data from Fort Wayne, Indiana, USA. A time series analysis showed that the weather data and soil moisture have a stationary seasonal tendency and demonstrated that soil moisture can be forecasted from weather data. The VAR model estimates volumetric soil moisture of one-day ahead with an R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.698, 0.0561, 0.0046, and 0.0382 for 2021 corn cropping season, whereas the LSTM model using inputs of previous seven days yielded R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.998, 0.00237, 0.00002, and 0.00382, respectively as tested for cropping season of 2020 and 0.973, 0.00368, 0.00003 and 0.00577 as tested for the cropping season of 2021. The LSTM model presents a viable data-driven alternative to traditional statistical models for forecasting subsurface soil moisture.

In the present era of agricultural digitalization, documenting on-farm operations is critical. These records contextualize other layers of data and underpin economic analysis and informed decision-making. On-farm recordkeeping is rooted in an ancient tradition and has evolved from pen and paper to digital means integrating diverse tools and methods. These tools vary widely in mode of data recording and this presents challenges in achieving complete, accurate and interoperable data. Assessing this diversity of existing recordkeeping systems is a key step toward the improvement in recordkeeping systems that enhance data quality and interoperability. Despite the importance, as of present, comprehensive studies addressing this challenge are lacking. A systematic review of existing on-farm recordkeeping systems was carried out to address their advantages and weaknesses and to analyze their features and traits, focusing on interoperability and adherence to efficient and comprehensive on-farm recordkeeping. Paper-based recordkeeping, a longstanding and reliable method, is gradually being replaced by digital platforms. Many universities and agencies have released farm management spreadsheets and interactive database forms representing the initial step toward intuitive recordkeeping. Furthermore, farm management software, web apps, and user-friendly smartphone apps are increasingly crucial for handling agricultural big data. Notably, among the surveyed software packages and apps, most of them are not free and only a few support data interoperability. The survey also indicates a scope for further development in open-source tools with automation in recordkeeping. Adopting digital on-farm recordkeeping tools can positively impact both on and off the farm, fostering data interoperability, controlled yet flexible data access, completeness, and appropriate accuracy.

Accurate measurements of daily feed consumption for dairy cattle is an important metric for determining animal health and feed efficiency. Traditionally, manual measurements or average feed consumption for groups of animals have been employed which leads to human error and overall inconsistent measurements for the individual. Therefore, we developed a scalable non-invasive analytics system that leverages depth information derived from stereo cameras to consistently measure feed offered and report findings throughout the day. A top-down array of cameras faces the available feed, measures feed depth, projects depth to a 3-dimensional (3D) mesh, and finally estimates feed volume from the 3D projection. Our successful experiments at the Purdue University Dairy, that houses 230 cows, demonstrates its robustness and scalability for larger operations holding significant potential for optimizing feed management in dairy farms, thereby improving animal health and sustainability in the dairy industry.

Canola (Brassica napus L.) yield prediction using a combined application of small unmanned aerial system (sUAS) and vegetation indices (VIs) have gained significant attention. In recent years, major studies have demonstrated the potential of developing new VIs for predicting canola yield. However, such indices may perform optimally on the specific farms for which they have been designed and may fail to generalize to a new agronomic scenario. Therefore, this study aims to conduct a comprehensive analysis on the application of existing VIs that could be used to predict potential canola yield on a commercial farm, thereby eliminating the need to develop new indices from scratch. In this research study, over twenty-seven VIs were extracted from two sUAS imagery captured during peak flowering and seed development stages of canola. The extracted features were fed to four conventional machine learning (ML) classifiers with appropriate hyperparameter tuning approaches. Additionally, to perform a comparative test with neural network-based deep learning (DL) architectures, a convolutional neural network-1-dimensional (CNN-1D_Canola) model was developed to train data points and predict canola yield. These models were trained on the yield maps interpolated using three approaches, based on the ground truth yield data points obtained from a harvester. Results suggest that peak flowering is the best stage to predict canola yield. Additionally, a combination of kriging-based yield maps with three best features, canola ratio index (CRI), canola index (CI), and structure intensive vegetation index (SIPI) indices, trained using support vector machine (R2=0.68), multi-layer perceptron (R2=0.7), and CNN_1D_Canola (R2=0.66) have the potential to predict canola yield based on spectral image-based features. This study highlights the potential of predicting canola yield for a commercial farm using a combined application of sUAS imagery and VIs. The promising performance of all the models coupled with a comprehensive hyperparameter tuning approaches suggests its applicability in predicting canola yield in real field conditions.

As an on-ramp to databases, we offer several well-structured private database templates as open source resources for agriculturalists, particularly those with modest spreadsheet skills. These farmer-oriented Air table databases use simple data-validated forms, with the look and feel of a customized app, to yield operational data that is tidy, machine- and human-readable, editable, and exportable for analysis in other software. Such data can facilitate logistics, provide contextual metadata, and improve enterprise analysis. A recorded workshop explaining how to build a database for activity records is presented. These resources may facilitate infusion of digital agriculture principles through Extension and structured educational programming.

Agriculture faces many challenges to continue past trends of crop productivity improvement in the presence of increased losses due to adverse weather, diseases caused by pathogens and pests, limitations on fertilizers and fuel, and a general need to improve sustainability. At the same time, an increasing world population adds more pressure to increase yield productivity with limited land and water. Digital agriculture offers a broad list of technologies and solutions to increase yield productivity by granting farmers more control over their operations via precision agriculture and data-driven tools that support decision-making to optimize resource usage in all kinds of operations. However, rural areas often lack adequate communications infrastructure to sustain automated data collection pipelines. As a result, vast amounts of data are often lost, undermining the effectiveness of digital agriculture applications. Delay Tolerant Networks (DTNs) have been used in applications where mainstream communication channels are not available or work with low performance. In this paper, we propose and simulate a DTN that takes advantage of the movement of regular agricultural machinery operations, using a fleet of edge-computers capable of establishing peer-to-peer wireless links with in-field static edge-computers. The DTN was simulated using location data recorded from planting operations in multiple fields using the Opportunistic Network Environment (ONE) simulator with three routing algorithms. Message delivery rates between 0.31 and 0.7, with latencies varying from 3180 and 7040 seconds under different simulation scenarios were obtained. Lastly, we present pathways for developing a message propagation and routing scheme adaptable to the dynamic nature of agricultural operations.

Agricultural robots have the potential to increase productivity, save labor, and optimize resources. However, navigating these robots remains challenging due to the intricate and dynamic nature of agricultural landscapes. Traditional image processing techniques often fall short in large-scale applications due to varying environmental conditions. To address this challenge, our paper introduces a novel in-field RGB image processing methodology centered on row detection and navigation. Using the state-of-the-art foundation model, Segment Anything Model (SAM), as our backbone, we harness vegetation index values extracted from excess green segmentation as prompts. This assists the SAM model in generating refined masks tailored for robot navigation within rows. Evaluation conducted on 50 test images showcased a navigation angle error chiefly between −5.72° and 8.72°. However, 8% of the tests displayed errors spanning from 19° to 48°. The overall root mean square error (RMSE) of the algorithm was 9.78°. To counter these significant angular discrepancies, we advocate for the integration of finite-state machine algorithms. Our research offers a promising avenue for more robust row detection and navigation techniques in agricultural robotics.

The production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (nitrogen & phosphorus), farm machinery fuel costs and decreasing labor productivity leading to non-optimized usage of farm inputs. Literature describes how sustainable practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of precision agriculture technologies (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm machinery navigation). The literature review describes low adoption of Internet of Things (IoT)-based precision agriculture technologies, such as variable rate fertilizer (39%), variable rate pesticide (8%), variable rate irrigation (4%), cloud-based data analytics (21%) and telematics (10%) amongst Midwestern row crop producers. Barriers to the adoption of IoT-based precision agriculture technologies cited in the literature include cost effectiveness, power requirements, wireless communication range, data latency, data scalability, data storage, data processing and data interoperability. Therefore, this study focused on exploring and understanding decision-making variables related to barriers through three focus group interview sessions conducted with eighteen (n = 18) subject matter experts (SME) in IoT- based precision agriculture practices. Dependency relationships described between cost, data latency, data scalability, power consumption, communication range, type of wireless communication and precision agriculture application is one of the main findings. The results might inform precision agriculture practitioners, producers and other stakeholders about variables related to technical and operational barriers for the adoption of IoT-based precision agriculture practices.

As an on-ramp to databases, we offer several well-structured private database templates as open source resources for agriculturalists, particularly those with modest spreadsheet skills. These farmer-oriented Air table databases use simple data-validated forms, with the look and feel of a customized app, to yield operational data that is tidy, machine- and human-readable, editable, and exportable for analysis in other software. Such data can facilitate logistics, provide contextual metadata, and improve enterprise analysis. A recorded workshop explaining how to build a database for activity records is presented. These resources may facilitate infusion of digital agriculture principles through Extension and structured educational programming.