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Robotics and Automation in Agriculture

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A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Design, Modeling and Computing Methods".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 42168

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Special Issue Editor

Prof. Dr. Ratnesh Kumar

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Department of Electrical & Computer Engineering, Department of Computer Science, Iowa State University, Ames, IA 50011-3060, USA
Interests: agri/env/bio sensors; sensor systems; sensor data modeling and fusion; sensor security and anomaly detection; energy harvesting; internet of things; cyber physical systems; decision-making and controls; formal methods

Special Issue Information

Dear Colleagues,

Farming is facing many challenges in terms of productivity and cost-effectiveness. The introduction of automation, including intelligent robots, automated sensing, modeling, decision-making and actuation, and the role of genotypes versus the environment, to agricultural operations will allow for enhanced efficiency and reduced environmental impact. There are genetics-sensors-machinery-robotics-clouds-aided automated systems for various aspects of agricultural functions, from crop genetics composition to monitoring for resource availability, stresses and diseases, and grafting to seeding and planting; and from the application of water/nutrients/agrochemicals to harvesting to sorting, packaging, boxing, and livestock management. Automated ground as well as aerial devices and robotics can gather operational data as well as affect the operations on a broader basis than manual practices. Precision farming using genetics, automation, and robotics technologies applied to existing systems can lead to enhanced resource-efficient and environmentally-friendly agricultural production that is needed to feed the growing populace, projected to be 9 Billion by 2050, with no proportionate growth in cultivable land and climate adversity.

This Special Issue will focus on automation and robotics in agriculture for precision and sustainable farming, such as the impact of genotype and environment on phenotypes; ground/aerial sensors for in-field monitoring and data-modeling in clouds; agricultural modeling and visualization for yields, agrochemical and water cycling, and decision-making; mechanized seeding, agrochemical application, harvesting, targeted weed control, and environmentally-friendly fertilization; and/or livestock management, all based on more effective planning, runtime decision-making and targeted interventions, optimizing the entire agricultural process, sensor fusion from multiple agricultural robotic platforms, internet of things and agricultural robotics.

Harpole Prof. Dr. Ratnesh Kumar
Guest Editor

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Keywords

Precision and sustainable agriculture
In-situ sensors and information acquisition
Ground and aerial agricultural robotics and devices
Data-driven modeling of agricultural processes
Data-driven agricultural decision making and optimization
Understanding the impact of genotypes and the environment on phenotypes
Internet of things in agricultural robotics
Sensor fusion in agricultural robotics

Published Papers (9 papers)

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Research

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13 pages, 912 KiB

Open AccessArticle

Fused Graphical Lasso Recovers Flowering Time Mutation Genes in Arabidopsis thaliana

by Rajan Kapoor, Aniruddha Datta and Michael Thomson

Inventions 2021, 6(3), 52; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions6030052 - 20 Jul 2021

Cited by 1 | Viewed by 2129

Abstract

Conventional breeding approaches that focus on yield under highly favorable nutrient conditions have resulted in reduced genetic and trait diversity in crops. Under the growing threat from climate change, the mining of novel genes in more resilient varieties can help dramatically improve trait improvement efforts. In this work, we propose the use of the joint graphical lasso for discovering genes responsible for desired phenotypic traits. We prove its efficiency by using gene expression data for wild type and delayed flowering mutants for the model plant. Arabidopsis thaliana shows that it recovers the mutation causing genes LNK1 and LNK2. Some novel interactions of these genes were also predicted. Observing the network level changes between two phenotypes can also help develop meaningful biological hypotheses regarding the novel functions of these genes. Now that this data analysis strategy has been validated in a model plant, it can be extended to crop plants to help identify the key genes for beneficial traits for crop improvement. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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14 pages, 4562 KiB

Open AccessArticle

Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

by Worasit Sangjan, Arron H. Carter, Michael O. Pumphrey, Vadim Jitkov and Sindhuja Sankaran

Inventions 2021, 6(2), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions6020042 - 10 Jun 2021

Cited by 17 | Viewed by 8853

Abstract

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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20 pages, 2134 KiB

Open AccessArticle

Bayesian Network Analysis of Lysine Biosynthesis Pathway in Rice

by Aditya Lahiri, Khushboo Rastogi, Aniruddha Datta and Endang M. Septiningsih

Inventions 2021, 6(2), 37; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions6020037 - 24 May 2021

Cited by 5 | Viewed by 3736

Abstract

Lysine is the first limiting essential amino acid in rice because it is present in the lowest quantity compared to all the other amino acids. Amino acids are the building block of proteins and play an essential role in maintaining the human body’s healthy functioning. Rice is a staple food for more than half of the global population; thus, increasing the lysine content in rice will help improve global health. In this paper, we studied the lysine biosynthesis pathway in rice (Oryza sativa) to identify the regulators of the lysine reporter gene LYSA (LOC_Os02g24354). Genetically intervening at the regulators has the potential to increase the overall lysine content in rice. We modeled the lysine biosynthesis pathway in rice seedlings under normal and saline (NaCl) stress conditions using Bayesian networks. We estimated the model parameters using experimental data and identified the gene DAPF(LOC_Os12g37960) as a positive regulator of the lysine reporter gene LYSA under both normal and saline stress conditions. Based on this analysis, we conclude that the gene DAPF is a potent candidate for genetic intervention. Upregulating DAPF using methods such as CRISPR-Cas9 gene editing strategy has the potential to upregulate the lysine reporter gene LYSA and increase the overall lysine content in rice. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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47 pages, 1648 KiB

Open AccessArticle

Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests

by Bhuwan Kashyap and Ratnesh Kumar

Inventions 2021, 6(2), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions6020029 - 22 Apr 2021

Cited by 18 | Viewed by 6316

Abstract

Reducing agricultural losses is an effective way to sustainably increase agricultural output efficiency to meet our present and future needs for food, fiber, fodder, and fuel. Our ever-improving understanding of the ways in which plants respond to stress, biotic and abiotic, has led to the development of innovative sensing technologies for detecting crop stresses/stressors and deploying efficient measures. This article aims to present the current state of the methodologies applied in the field of agriculture towards the detection of biotic stress in crops. Key sensing methodologies for plant pathogen (or phytopathogen), as well as herbivorous insects/pests are presented, where the working principles are described, and key recent works discussed. The detection methods overviewed for phytopathogen-related stress identification include nucleic acid-based methods, immunological methods, imaging-based techniques, spectroscopic methods, phytohormone biosensing methods, monitoring methods for plant volatiles, and active remote sensing technologies. Whereas the pest-related sensing techniques include machine-vision-based methods, pest acoustic-emission sensors, and volatile organic compound-based stress monitoring methods. Additionally, Comparisons have been made between different sensing techniques as well as recently reported works, where the strengths and limitations are identified. Finally, the prospective future directions for monitoring biotic stress in crops are discussed. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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24 pages, 9379 KiB

Open AccessArticle

Agriculture Model Comparison Framework and MyGeoHub Hosting: Case of Soil Nitrogen

by Anupam Bhar, Benjamin Feddersen, Robert Malone and Ratnesh Kumar

Inventions 2021, 6(2), 25; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions6020025 - 29 Mar 2021

Cited by 6 | Viewed by 3149

Abstract

To be able to compare many agricultural models, a general framework for model comparison when field data may limit direct comparison of models is proposed, developed, and also demonstrated. The framework first calibrates the benchmark model against the field data, and next it calibrates the test model against the data generated by the calibrated benchmark model. The framework is validated for the modeling of the soil nutrient nitrogen (N), a critical component in the overall agriculture system modeling effort. The nitrogen dynamics and related carbon (C) dynamics, as captured in advanced agricultural modeling such as RZWQM, are highly complex, involving numerous states (pools) and parameters. Calibrating many parameters requires more time and data to avoid underfitting. The execution time of a complex model is higher as well. A study of tradeoff among modeling complexities vs. speed-up, and the corresponding impact on modeling accuracy, is desirable. This paper surveys soil nitrogen models and lists those by their complexity in terms of the number of parameters, and C-N pools. This paper also examines a lean soil N and C dynamics model and compares it with an advanced model, RZWQM. Since nitrate and ammonia are not directly measured in this study, we first calibrate RZWQM using the available data from an experimental field in Greeley, CO, and next use the daily nitrate and ammonia data generated from RZWQM as ground truth, against which the lean model’s N dynamics parameters are calibrated. In both cases, the crop growth was removed to zero out the plant uptake, to compare only the soil N-dynamics. The comparison results showed good accuracy with a coefficient of determination (R2) match of 0.99 and 0.62 for nitrate and ammonia, respectively, while affording significant speed-up in simulation time. The lean model is also hosted in MyGeoHub cyberinfrastructure for universal online access. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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20 pages, 522 KiB

Open AccessArticle

Sensor Localization Using Time of Arrival Measurements in a Multi-Media and Multi-Path Application of In-Situ Wireless Soil Sensing

by Herman Sahota and Ratnesh Kumar

Inventions 2021, 6(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions6010016 - 27 Feb 2021

Cited by 3 | Viewed by 2006

Abstract

The problem of localization of nodes of a wireless sensor network placed in different physical media (anchor nodes above ground and sensor nodes underground) is addressed in this article. We use time of arrival of signals transmitted between neighboring sensor nodes and between satellite nodes and sensor nodes as the ranging measurement. The localization problem is formulated as a parameter estimation of the joint distribution of the time of arrival values. The probability distribution of the time of arrival of a signal is derived based on rigorous statistical analysis and its parameters are expressed in terms of the location coordinates of the sensor nodes. Maximum likelihood estimates of the nodes’ location coordinates as parameters of the joint distribution of the various time of arrival variables in the network are computed. Sensitivity analysis to study the variation in the estimates with respect to error in measured soil complex permittivity and magnetic permeability is presented to validate the model and methodology. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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11 pages, 11687 KiB

Open AccessArticle

Grading of Scots Pine Seeds by the Seed Coat Color: How to Optimize the Engineering Parameters of the Mobile Optoelectronic Device

by Arthur I. Novikov, Vladimir K. Zolnikov and Tatyana P. Novikova

Inventions 2021, 6(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions6010007 - 15 Jan 2021

Cited by 40 | Viewed by 2262

Abstract

Research Highlights: There is a problem of forest seeds quality assessment and grading afield in minimal costs. The grading quality of each seed coat color class is determined by the degree of its separation with a mobile optoelectronic grader. Background and Objectives: Traditionally, pine seeds are graded in size, but this can lead to a loss of genetic diversity. Seed coat color is individual for each forest seed and is caused to a low error in identifying the genetic features of seedling obtained from it. The principle on which the mobile optoelectronic grader operates is based on the optical signal detection reflected from the single seed. The grader can operate in scientific (spectral band analysis) mode and production (spectral feature grading) mode. When operating in production mode, it is important to determine the optimal engineering parameters of the grader that provide the maximum value of the separation degree of seed-color classes. For this purpose, a run of experiments was conducted on the forest seeds separation using a mobile optoelectronic grader and regression models of the output from factors were obtained. Materials and Methods: Scots pine (Pinus sylvestris L.) seed samples were obtained from cones of the 2019 harvest collected in a natural stand. The study is based on the Design of Experiments theory (DOE) using the Microsoft Excel platform. In each of three replications of each run from the experiment matrix, a mixture of 100 seeds of light, dark and light-dark fraction (n = 300) was used. Results: Interpretation of the obtained regression model of seed separation in the visible wavelength range (650–715 nm) shows that the maximum influence on the output—separation degree—is exerted by the angle of incidence of the detecting optical beam. Next in terms of the influence power on the output are paired interactions: combinations of the wavelength with the angle of incidence and the wavelength with the grader’s seed pipe height. The minimum effect on the output is the wavelength of the detecting optical beam. Conclusions: The use of a mobile optoelectronic grader will eliminate the cost of transporting seeds to and from forest seed centers. To achieve a value of 0.97–1.0 separation degree of Scots pine seeds colored fractions, it is necessary to provide the following optimal engineering parameters of the mobile optoelectronic grader: the wavelength of optical radiation is 700 nm, the angle of incidence of the detecting optical beam is 45° and the grader’s seed pipe height is 0.2 m. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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18 pages, 3343 KiB

Open AccessArticle

Autonomous Mobile Ground Control Point Improves Accuracy of Agricultural Remote Sensing through Collaboration with UAV

by Xiongzhe Han, J. Alex Thomasson, Tianyi Wang and Vaishali Swaminathan

Inventions 2020, 5(1), 12; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions5010012 - 02 Mar 2020

Cited by 11 | Viewed by 5429

Abstract

Ground control points (GCPs) are critical for agricultural remote sensing that require georeferencing and calibration of images collected from an unmanned aerial vehicles (UAV) at different times. However, the conventional stationary GCPs are time-consuming and labor-intensive to measure, distribute, and collect their information in a large field setup. An autonomous mobile GCP and a collaboration strategy to communicate with the UAV were developed to improve the efficiency and accuracy of the UAV-based data collection process. Prior to actual field testing, preliminary tests were conducted using the system to show the capability of automatic path tracking by reducing the root mean square error (RMSE) for lateral deviation from 34.3 cm to 15.6 cm based on the proposed look-ahead tracking method. The tests also indicated the feasibility of moving reflectance reference panels successively along all the waypoints without having detrimental effects on pixel values in the mosaicked images, with the percentage errors in digital number values ranging from −1.1% to 0.1%. In the actual field testing, the autonomous mobile GCP was able to successfully cooperate with the UAV in real-time without any interruption, showing superior performances for georeferencing, radiometric calibration, height calibration, and temperature calibration, compared to the conventional calibration method that has stationary GCPs. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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Review

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17 pages, 1490 KiB

Open AccessReview

Applications of Vegetative Indices from Remote Sensing to Agriculture: Past and Future

by Jerry L. Hatfield, John H. Prueger, Thomas J. Sauer, Christian Dold, Peter O’Brien and Ken Wacha

Inventions 2019, 4(4), 71; https://0-doi-org.brum.beds.ac.uk/10.3390/inventions4040071 - 06 Dec 2019

Cited by 29 | Viewed by 6937

Abstract

Remote sensing offers the capability of observing an object without being in contact with the object. Throughout the recent history of agriculture, researchers have observed that different wavelengths of light are reflected differently by plant leaves or canopies and that these differences could be used to determine plant biophysical characteristics, e.g., leaf chlorophyll, plant biomass, leaf area, phenological development, type of plant, photosynthetic activity, or amount of ground cover. These reflectance differences could also extend to the soil to determine topsoil properties. The objective of this review is to evaluate how past research can prepare us to utilize remote sensing more effectively in future applications. To estimate plant characteristics, combinations of wavebands may be placed into a vegetative index (VI), i.e., combinations of wavebands related to a specific biophysical characteristic. These VIs can express differences in plant response to their soil, meteorological, or management environment and could then be used to determine how the crop could be managed to enhance its productivity. In the past decade, there has been an expanded use of machine learning to determine how remote sensing can be used more effectively in decision-making. The application of artificial intelligence into the dynamics of agriculture will provide new opportunities for how we can utilize the information we have available more effectively. This can lead to linkages with robotic systems capable of being directed to specific areas of a field, an orchard, a pasture, or a vineyard to correct a problem. Our challenge will be to develop and evaluate these relationships so they will provide a benefit to our food security and environmental quality. Full article

(This article belongs to the Special Issue Robotics and Automation in Agriculture)

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