Digital Commons Network^™

Aicropcam: Deploying Classification, Segmentation, Detection, And Counting Deep-Learning Models For Crop Monitoring On The Edge, Nipuna Chamara, Geng (Frank) Bai, Yufeng Ge

Department of Biological Systems Engineering: Papers and Publications

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing …

Precision Spraying Using Variable Time Delays And Vision-Based Velocity Estimation, Paolo Rommel Sanchez, Hong Zhang

Henry M. Rowan College of Engineering Faculty Scholarship

Traditionally, precision farm equipment often relies on real-time kinematics and global positioning systems (RTK-GPS) for accurate position and velocity estimates. This approach proved effective and widely adopted in developed regions where RTK-GPS satellite and base station availability and visibility are not limited. However, RTK-GPS signal can be limited in farm areas due to topographic and economic constraints. Thus, this study developed a precision sprayer that estimated the travel velocity locally by tracking the relative motion of plants using a deep-learning-based machine vision system. Sprayer valves were then controlled by variable time delay (VTD) queuing and dynamic filtering. The proposed velocity …

A Fog Computing Framework For Intrusion Detection Of Energy-Based Attacks On Uav-Assisted Smart Farming, Junaid Sajid, Kadhim Hayawi, Asad Waqar Malik, Zahid Anwar, Zouheir Trabelsi

All Works

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. …

Cyber-Agricultural Systems For Crop Breeding And Sustainable Production, Soumik Sarkar, Baskar Ganapathysubramanian, Arti Singh, Fateme Fotouhi, Soumyashree Kar, Koushik Nagasubramanian, Girish Chowdhary, Sajal K. Das, George Kantor, Adarsh Krishnamurthy, Nirav Merchant, Asheesh K. Singh

Computer Science Faculty Research & Creative Works

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing …

A Methodology To Optimize Site-Specific Field Capacity And Irrigation Thresholds, Hemendra Kumar, Puneet Srivastava, Jasmeet Lamba, Bruno Lena, Efstathios Diamantopoulos, Brenda Ortiz, Bijoychandra Takhellambam, Guilherme Morata, Luca Bondesan

Department of Biological Systems Engineering: Papers and Publications

The determination of field capacity (FC), irrigation thresholds, and irrigation amounts is characterized by site-specific soil hydraulic properties (SHPs). This study, conducted in two zones (zone 1 and zone 2) delineated based on soil, topography, and historical crop yield in Alabama (USA), focused on determining zone-specific FC using negligible drainage flux (q_fc) criterion. The HYDRUS-1D model was used to optimize zone-specific SHPs using measured soil matric potential (h). The zone-specific FCs were determined using optimized and raw SHPs at 0.01 cm/day as q_fc. The results showed that the optimized FC at q_{fc …}

Identifying Early-Life Behavior To Predict Mothering Ability In Swine Utilizing Nutrack System, Savannah Millburn

Department of Animal Science: Dissertations, Theses, and Student Research

Early recognition of indicator traits for swine reproduction and longevity supports economical selection decision making. Gilt activity is a key variable impacting a sow’s herd life and productivity. The purpose of this study was to examine early- life behaviors contributing to farrowing traits including gestation length (GL), number born alive (NBA), number weaned (NW), and herd life (HL). Herd life was a binary trait representing if a gilt was culled after one parity. Beginning at approximately 20 weeks of age, video recordings were taken on 480 gilts for 7 consecutive days and processed using the NUtrack system. Activity traits include …

Ag-Iot For Crop And Environment Monitoring: Past, Present, And Future, Nipuna Chamara, Md Didarul Islam, Geng Bai, Yeyin Shi, Yufeng Ge

Department of Biological Systems Engineering: Papers and Publications

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction …

Detection Of Crop Diseases Using Enhanced Variability Imagery Data And Convolutional Neural Networks, Shai Kendler, Ran Aharoni, Sierra N. Young, Hanan Sela, Tamar Kis-Papo, Tzion Fahima, Barak Fishbain

Civil and Environmental Engineering Faculty Publications

The timely detection of crop diseases is critical for securing crop productivity, lowering production costs, and minimizing agrochemical use. This study presents a crop disease identification method that is based on Convolutional Neural Networks (CNN) trained on images taken with consumer-grade cameras. Specifically, this study addresses the early detection of wheat yellow rust, stem rust, powdery mildew, potato late blight, and wild barley net blotch. To facilitate this, pictures were taken in situ without modifying the scene, the background, or controlling the illumination. Each image was then split into several patches, thus retaining the original spatial resolution of the image …

Vision And Radar Steering Reduces Agricultural Sprayer Operator Stress Without Compromising Steering Performance, Travis A. Burgers, Kelly J. Vanderwerff

Mechanical Engineering Faculty Publications

Self-propelled agricultural sprayer operators work an average of 15 h d^-1 in peak season, and steering is the task that causes the operator the most stress because of the large number of stimuli involved. Automatic guidance systems help reduce stress and fatigue for operators by allowing them to focus on tasks other than steering. Physiological signals like skin conductance (electrodermal activity, EDA) change with stress and can be used to identify stressful events. The objective of this study was to determine if using a commercially available vision and radar guidance system (VSN®, Raven Industries) reduces agricultural sprayer operators’ stress …

A Deep Learning Model Compression And Ensemble Approach For Weed Detection, Martinson Ofori, Omar El-Gayar, Austin O'Brien

Research & Publications

Site-specific weed management is an important practice in precision agriculture. Current advances in artificial intelligence have resulted in the use of large deep convolutional neural networks for weed detection. In this paper, a transfer learning, model compression, and ensemble learning approach is introduced that is suitable for resource-limited hardware such as mobile and embedded devices. The resulting ensemble model achieves 91.2% classification accuracy which is comparable to the performance of state-of-the-art deep learning models (such as the vanilla VGG16, DenseNet, and ResNet) while being about 62.22% smaller in size than DenseNet (the smallest-sized full-sized model). The approach used in this …

Gis-Based Volunteer Cotton Habitat Prediction And Plant-Level Detection With Uav Remote Sensing, Tianyi Wang, Xiaohan Mei, J. Alex Thomasson, Chenghai Yang, Xiongzhe Han, Pappu Kumar Yadav, Yeyin Shi

Department of Biological Systems Engineering: Papers and Publications

Volunteer cotton plants germinate and grow at unwanted locations like transport routes and can serve as hosts for a harmful cotton pests called cotton boll weevils. The main objective of this study was to develop a geographic information system (GIS) framework to efficiently locate volunteer cotton plants in the cotton production regions in southern Texas, thus reducing time and economic cost for their removal. A GIS network analysis tool was applied to estimate the most likely routes for cotton transportation, and a GIS model was created to identify and visualize potential areas of volunteer cotton growth. The GIS model indicated …

How To Effectively Reach Farmers And Assist Them In Reaching Their Precision Management Goals, Courtney Nelson

Honors Theses

Precision and digital agriculture have been popular buzz words floating around the last several years. These broad terms cover a plethora of topics including GPS ear tags for livestock, soil moisture probes, and aerial imagery. With such a wide number of technological advances at their fingertips, it can be overwhelming for farmers to know where to start.

A study conducted by Purdue University in 2019 took a deeper look at data and software usage across 800 farms larger than 1000 acres (DeLay et al, 2020). Their research revealed that over half of farmers who don’t use farm data or software …

Essays On Agricultural Technology, Resource Allocation And The Value Of Information, Pedro Queiroz

Department of Agricultural Economics: Dissertations, Theses, and Student Research

This dissertation studied the adoption of agricultural technologies and the value of information for the allocation of resources in agriculture. Chapter 1 studied traditional (e.g., land and labor) and commercial (e.g., machinery and fertilizers) inputs in South American agriculture. Acemoglu’s directed technical change was used to estimate the process of induced innovation using deforestation as source of exogenous variation for the agricultural land supply. The results indicated that larger availability of land in intensive deforestation countries caused more land-complementary inputs (machinery) to be used relative to labor-complementary inputs (fertilizers). Technical change was biased towards land. Chapter 2 studied nitrogen fertilizer …

An Expected Value Of Sample Information (Evsi) Approach For Estimating The Payoff From A Variable Rate Technology., Pedro Vertino De Queiroz, Richard Perrin, Lilyan E. Fulginiti, David S. Bullock

Department of Agricultural Economics: Faculty Publications

This paper examines the payoff to variable rate technology (VRT) using a Bayesian approach following literature on the expected value of sample information (EVSI). In each cell within a field, we compare the expected payoff from an optimal variable rate conditioned on a signal from that cell, with the expected payoff from a uniform rate technology (URT) that is optimal for all cells in the field. This comparison, when evaluated across the theoretical distribution of signals, provides an estimate of the expected gross benefit from VRT relative to URT. Under plausible assumptions, a closed-form algebraic solution relates this expected benefit …

A Survey Of Public Datasets For Computer Vision Tasks In Precision Agriculture, Yuzhen Lu, Sierra N. Young

Civil and Environmental Engineering Faculty Publications

Computer vision technologies have attracted significant interest in precision agriculture in recent years. At the core of robotics and artificial intelligence, computer vision enables various tasks from planting to harvesting in the crop production cycle to be performed automatically and efficiently. However, the scarcity of public image datasets remains a crucial bottleneck for fast prototyping and evaluation of computer vision and machine learning algorithms for the targeted tasks. Since 2015, a number of image datasets have been established and made publicly available to alleviate this bottleneck. Despite this progress, a dedicated survey on these datasets is still lacking. To fill …

Effects Of Combined Conservation Practices On Soil And Water Quality In The Central Mississippi River Basin, C. Baffaut, F. Ghidey, R. N. Lerch, K. S. Veum, E. J. Sadler, K. A. Sudduth, N. R. Kitchen

United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: Faculty Publications

Conventional cultivation of claypan soils leads to soil and water quality degradation because of high runoff and associated soil erosion. The Goodwater Creek Experimental Watershed, which is part of the USDA Agricultural Research Service Benchmark Conservation Effects Assessment Project, Watershed Assessment Studies, was established to address these issues. Plot studies have highlighted trade-offs between erosion control and herbicide or nutrient runoff. There is a need for long-term field-scale evaluation of combined practices that reduce sediment, nutrient, and herbicide losses by runoff. A 36 ha field located in Missouri was under a conventional corn (Zea mays L.)-soybean (Glycine max L.) system …

Performances Of The Lbp Based Algorithm Over Cnn Models For Detecting Crops And Weeds With Similar Morphologies, Vi Nguyen Thanh Le, Selam Ahderom, Kamal Alameh

Research outputs 2014 to 2021

Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, …

A Novel Method For Detecting Morphologically Similar Crops And Weeds Based On The Combination Of Contour Masks And Filtered Local Binary Pattern Operators, Vi Nguyen Thanh Le, Selam Ahderom, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of …

Rapeseed Stand Count Estimation At Leaf Development Stages With Uav Imagery And Convolutional Neural Networks, Biquan Zhao, Chenghai Yang, Yeyin Shi, Qingxi Liao, Guangsheng Zhou, Chufeng Wang, Tianjin Xie, Zhao Jiang, Dongyan Zhang, Wanneng Yang, Chenglong Huang, Jing Xie, Jian Zhang

Department of Biological Systems Engineering: Papers and Publications

Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves …

Unmanned Aircraft System (Uas) Technology And Applications In Agriculture, Samuel C. Hassler, Fulya Baysal-Gurel

Agricultural and Environmental Sciences Faculty Research

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing …

Computational Contributions To The Automation Of Agriculture, Micah Nagel

Senior Honors Theses

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of …

Development Of A Nitrogen Recommendation Tool For Corn Considering Static And Dynamic Variables, Laila A. Puntel, Agustin Pagani, Sotirios V. Archontoulis

Department of Agronomy and Horticulture: Faculty Publications

Many soil and weather variables can affect the economical optimum nitrogen (N) rate (EONR) for maize. We classified 54 potential factors as dynamic (change rapidly over time, e.g. soil water) and static (change slowly over time, e.g. soil organic matter) and explored their relative importance on EONR and yield prediction by analyzing a dataset with 51 N trials from Central-West region of Argentina. Across trials, the average EONR was 113 ± 83 kg N ha⁻¹ and the average optimum yield was 12.3 ± 2.2 Mg ha⁻¹, which is roughly 50% higher than the current N rates used …

A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak

Faculty Publications

The realization of Internet of Underground Things (IOUT) relies on the establishment of reliable communication links, where the antenna becomes a major design component due to the significant impacts of soil. In this paper, a theoretical model is developed to capture the impacts of change of soil moisture on the return loss, resonant frequency, and bandwidth of a buried dipole antenna. Experiments are conducted in silty clay loam, sandy, and silt loam soil, to characterize the effects of soil, in an indoor testbed and field testbeds. It is shown that at subsurface burial depths (0.1-0.4m), change in soil moisture impacts …

Droplet Size Impact On Efficacy Of A Dicamba-Plus-Glyphosate Mixture, Thomas R. Butts, Chase A. Samples, Lucas X. Franca, Darrin M. Dodds, Daniel B. Reynolds, Jason W. Adams, Richard K. Zollinger, Kirk A. Howatt, Bradley K. Fritz, W. Clint Hoffmann, Joe D. Luck, Greg Kruger

West Central Research and Extension Center, North Platte

Chemical weed control remains a widely used component of integrated weed management strategies because of its cost-effectiveness and rapid removal of crop pests. Additionally, dicamba-plus-glyphosate mixtures are a commonly recommended herbicide combination to combat herbicide resistance, specifically in recently commercially released dicamba-tolerant soybean and cotton. However, increased spray drift concerns and antagonistic interactions require that the application process be optimized to maximize biological efficacy while minimizing environmental contamination potential. Field research was conducted in 2016, 2017, and 2018 across three locations (Mississippi, Nebraska, and North Dakota) for a total of six site-years. The objectives were to characterize the efficacy of …

Development And Preliminary Evaluation Of An Integrated Individual Nozzle Direct Injection And Carrier Flow Rate Control System For Pesticide Applications, Joe D. Luck, Scott A. Shearer, Michael P. Sama

Biosystems and Agricultural Engineering Faculty Publications

Direct injection systems for agricultural spray applications continue to present challenges in terms of commercialization and adoption by end users. Such systems have typically suffered from lag time and mixing uniformity issues, which have outweighed the potential benefits of keeping chemical and carrier separate or reducing improper tank-mixed concentration by eliminating operator measurements. The proposed system sought to combine high-pressure direct nozzle injection with an automated variable-flow nozzle to improve chemical mixing and response times. The specific objectives were to: (1) integrate a high-pressure direct nozzle injection system with variable-flow carrier control into a prototype for testing, (2) assess the …

Performance Validation Of A Multi-Channel Lidar Sensor: Assessing The Effects Of Target Height And Sensor Velocity On Measurement Error, Surya S. Dasika, Michael P. Sama, L. Felipe Pampolini, Christopher B. Good

Biosystems and Agricultural Engineering Faculty Publications

The objective of this study was to determine the effects of sensor velocity and target height above ground level on height measurement error when using a multi-channel LiDAR sensor. A linear motion system was developed to precisely control the dynamics of the LiDAR sensor in an effort to remove uncertainty in the LiDAR position and velocity while under motion. The linear motion system allowed the LiDAR to translate forward and backward in one direction parallel to the ground. A user control interface was developed to operate the system under different velocity profiles and to log LiDAR data synchronous to the …

Multispecies Fruit Flower Detection Using A Refined Semantic Segmentation Network, Philipe A. Dias, Amy Tabb, Henry P. Medeiros

Electrical and Computer Engineering Faculty Research and Publications

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance …

Apple Flower Detection Using Deep Convolutional Networks, Philipe A. Dias, Amy Tabb, Henry P. Medeiros

Electrical and Computer Engineering Faculty Research and Publications

To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network …

On The Use Of Unmanned Aerial Systems For Environmental Monitoring, Salvatore Manfreda, Matthew F. Mccabe, Pauline E. Miller, Richard Lucas, Victor Pajuelo Madrigal, Giorgos Mallinis, Eyal Ben Dor, David Helman, Lyndon Estes, Giuseppe Ciraolo, Jana Müllerová, Flavia Tauro, M. Isabel De Lima, João L.M.P. De Lima, Antonino Maltese, Felix Frances, Kelly Caylor, Marko Kohv, Matthew Perks, Guiomar Ruiz-Pérez, Zhongbo Su, Giulia Vico, Brigitta Toth

Geography

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of …

Weed Management In Cranberries: A Historical Perspective And A Look To The Future, Hilary A. Sandler

Cranberry Station Faculty Publications

Integrated weed management (IWM) has been part of cranberry cultivation since its inception in the early 19th century. Proper site and cultivar selection, good drainage, rapid vine establishment, and hand weeding are as important now for successful weed management as when the industry first started. In 1940, Extension publications listed eight herbicides (e.g., petroleum-based products, inorganic salts and sulfates) for weed control. Currently, 18 herbicides representing 11 different modes of action are registered for use on cranberries. Nonchemical methods, such as hand weeding, sanding, flooding, and proper fertilization, remain integral for managing weed populations; new tactics such as flame cultivation …