About

Dennis Buckmaster is a Professor in the Department of Agricultural & Biological Engineering at Purdue University and serves as the Dean's Fellow for Digital Agriculture. His research focuses on Machine Systems Engineering, Data Science, and Digital Agriculture. As a faculty member, he contributes to advancing the integration of digital technologies in agricultural systems, emphasizing innovative solutions for sustainable and efficient agricultural practices. His work supports the development of digital tools and systems that enhance productivity and resource management in agriculture.

Research topics

• Computer Science
• Economics
• Artificial Intelligence
• Data Mining
• Environmental planning
• Archaeology
• Telecommunications
• Environmental resource management
• Environmental science
• Database
• Environmental economics
• Business
• Geography
• Data science

Selected publications

• Image analysis as a tool for measuring total mixed ration uniformity on dairy farms

  Computers and Electronics in Agriculture · 2026-01-23

  articleOpen access
  PublisherDOI

• Meta Ag 2.0: A Framework for AI-Infused Contextual Agricultural Recordkeeping

  2025-01-01

  articleOpen access

  <b><sc>Abstract.</sc></b> Generative Artificial Intelligence (Gen-AI) can enhance human-computer interaction by enabling natural, adaptive, and personalized communication to significantly improve data integrity and quality through automation and flexibility. Meta Ag, an Android-based farm recordkeeping app was developed to address challenges of error-prone manual data entry and it automatically recorded spatial and temporal information of farm operations; it employed a rule-based chatbot with data validation mechanisms to document operational details. In this work, the capabilities of Meta Ag were extended by incorporating a retrieval augmented agentic-AI framework to facilitate precise and complete metadata recording. The designed framework utilizes Gen-AI capabilities through API to collect and validate metadata from a user maintaining cognitively aligned human-AI interaction. The framework uses the data storage backend as a retrievable knowledge base and creates contextually appropriate questions for the user about detailed farm activity, replacing the rule-based chatbot in Meta Ag app. Implementing this framework, an example web application, Meta Ag 2.0, was developed where ChatGPT (GPT-4o) serves as the agentic AI engine. The app automatically transmits recorded spatial and temporal data to the AI, which applies procedural memory to query a private Airtable database (knowledge base, backend) to access pre-defined metadata fields. The AI dynamically generates context-specific questions, validates user inputs against database options, and refines metadata accuracy through iterative interaction before writing data records. This AI-enhanced recordkeeping system minimizes errors, reduces complexity, and enables the seamless documentation of diverse data through robust, scalable and human-centric data management solutions. This approach enables richer contextual data to support digital agriculture advancements.

  PublisherOA PDFDOI

• Meta Ag: An automatic agricultural contextual metadata collection app

  Smart Agricultural Technology · 2025-06-04 · 2 citations

  articleOpen access

  • Meta Ag, an automated farm recordkeeping Android app was developed. • The app captures precise spatiotemporal data, reducing missing farm activity logs. • Rule-based chatbot collects validated activity data with minimal inconsistencies. • Data exported in CSV, RDF, and JSON formats supports semantic interoperability. 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.

  PublisherDOI

• Contextualized LSTM-DNN Model for County-Level Corn Yield Prediction from Weather and Soil Data

  2025-04-23

  articleSenior author

  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 (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), 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.

  PublisherDOI

• Design and Validation of a Stereo Vision System to Estimate Volume of Total Mixed Rations Offered to Dairy Cattle

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Design and validation of a stereo vision system to estimate volume of total mixed rations offered to dairy cattle

  Computers and Electronics in Agriculture · 2025-08-04

  article
  PublisherDOI

• Ambient IoT: Communications Enabling Precision Agriculture

  IEEE Communications Magazine · 2025-03-31 · 6 citations

  article

  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.

  PublisherDOI

• Understanding Corn Production Complexity: Causal Structure Learning and Variable Ranking from Agricultural Simulations

  AgriEngineering · 2025-11-03

  articleOpen accessCorresponding

  Corn (Zea mays L.) yield productivity is driven by a multitude of factors, specifically genetics, environment, and management practices, along with their corresponding interactions. Despite continuous monitoring through proximal or remote sensors and advanced predictive models, understanding these complex interactions remains challenging. While predictive models are improving with regard to accurate predictions, they often fail to explain causal relationships, rendering them less interpretable than desired. Process-based or biophysical models such as the Agricultural Production Systems sIMulator (APSIM) incorporate these causalities, but the multitude of interactions are difficult to tease apart and are largely sensitive to external drivers, which often include stochastic variations. To address this limitation, we developed a novel methodology that reveals these hidden causal structures. We simulated corn production under varied conditions, including different planting dates, nitrogen fertilizer amounts, irrigation rules, soil and environmental conditions, and climate change scenarios. We then used the simulation results to rank features having the largest impact on corn yield through Random Forest modeling. The Random Forest model identified nitrogen uptake and annual transpiration as the most influential variables on corn yield, similar to the existing research. However, this analysis alone provided limited insight into how or why these features ranked highest and how the features interact with each other. Building on these results, we deployed a Causal Bayesian model, using a hybrid approach of score-based (hill climb) and constraint-based (injecting domain knowledge) models. The causal analysis provides a deeper understanding by revealing that genetics, environment, and management factors had causal impacts on nitrogen uptake and annual transpiration, which ultimately affected yield. Our methodology allows researchers and practitioners to unpack the “black box” of crop production systems, enabling more targeted and effective model development and management recommendations for optimizing corn production.

  PublisherDOI

• A Web-Based Application Leveraging Geospatial Information to Automate On-Farm Trial Design

  ArXiv.org · 2025-02-24

  preprintOpen accessSenior author

  On-farm sensor data have allowed farmers to implement field management techniques and intensively track the corresponding responses. These data combined with historical records open the door for real-time field management improvements with the help of current advancements in computing power. However, despite these advances, the statistical design of experiments is rarely used to evaluate the performance of field management techniques accurately. Traditionally, randomized block design is prevalent in statistical designs of field trials, but in practice it is limited in dealing with large variations in soil classes, management practices, and crop varieties. More specifically, although this experimental design is suited for most trial types, it is not the optimal choice when multiple factors are tested over multifarious natural variations in farms, due to the economic constraints caused by the sheer number of variables involved. Experimental refinement is required to better estimate the effects of the primary factor in the presence of auxiliary factors. In this way, farmers can better understand the characteristics and limitations of the primary factor. This work presents a framework for automating the analysis of local field variations by fusing soil classification data and lidar topography data with historical yield. This framework will be leveraged to automate the designing of field experiments based on multiple topographic features

  PublisherOA PDFDOI

• Leveraging Aboveground to Underground Backscatter IoT Communication via Event-based Data Streams

  2025-04-23

  articleSenior author

  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.

  PublisherDOI

Frequent coauthors

• James V. Krogmeier

  Purdue University West Lafayette

  36 shared

• C. Alan Rotz

  26 shared

• Aaron Ault

  Purdue University West Lafayette

  19 shared

• Ajit K. Srivastava

  18 shared

• C. E. Goering

  18 shared

• Roger P. Rohrbach

  North Carolina State University

  17 shared

• Andrew Balmos

  16 shared

• Yaguang Zhang

  16 shared