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Agronomy
Special Issues
AI and Agricultural Robots
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AI and Agricultural Robots

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 5859

Special Issue Editor

Dr. Siddhartha S. Mehta

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Guest Editor
Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA
Interests: visual servo control adaptive and robust control agricultural robotics; human-robotic systems

Special Issue Information

Dear Colleagues,

Food security and global market competition are the major driving forces stimulating research on robotics in agriculture. Additionally, agricultural sectors in many developed countries are functioning with reduced manpower due to the continuous decline in seasonal labor availability, high insurance costs, and rising wages. The labor-intensive and injury-prone working conditions are further decaying the already shrinking labor pool. Collectively, these circumstances along with low commodity prices and low labor productivity result in noncompetitive production costs that negatively impact the sustainability of agriculture in global markets. Robotics and automation in agriculture can help to mitigate labor shortages by reducing reliance on manpower and improve agricultural productivity to support sustainable economic development and growth. Robotic solutions have been studied by numerous researchers around the world to replace traditionally labor-intensive agricultural practices. However, the marketability and adoption of robotic systems in agriculture is currently limited by economic and technology barriers that prevent highly efficient autonomous operations at a cost that justifies the low commodity values.

Artificial Intelligence (AI) holds promise in overcoming several technology barriers to improve the performance of agricultural robotic systems. Recent advances have led to growth in the use of AI in a variety of agricultural applications, including disease detection, crop monitoring and yield estimation, crop and land cover classification, plant phenotyping, fruit detection, weed detection, data analytics, and animal production and management. Beyond the current state of knowledge, further research in efficient architectures along with scalable and fast training methods is necessary to expand AI toolboxes to meet performance requirements in agriculture while considering the restricted computational capacity. Furthermore, the potential of AI in agriculture can be realized through solution approaches that are robust with respect to uncertain, unstructured, and varying agricultural environments.  

These and more challenges related to the application of AI in agricultural robots and precision agriculture are expected to be covered by research and review manuscripts submitted to this Special Issue.

Dr. Siddhartha S. Mehta
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Robotics in pre-harvest and post-harvest operations
  • Smart irrigation and fertigation systems
  • Plant phenotyping
  • Fruit detection
  • Crop monitoring and health assessment
  • Disease detection
  • Yield estimation
  • Weed detection and control
  • Data analytics and decision support systems

Published Papers (2 papers)

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Research

21 pages, 8449 KiB  
Article
DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets
by Murtaza Rangwala, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar and Ryan K. Williams
Agronomy 2021, 11(11), 2245; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11112245 - 05 Nov 2021
Cited by 4 | Viewed by 2014
Abstract
Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular [...] Read more.
Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements. Full article
(This article belongs to the Special Issue AI and Agricultural Robots)
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15 pages, 4737 KiB  
Article
Design of and Experiment with Seedling Selection System for Automatic Transplanter for Vegetable Plug Seedlings
by Yongshuang Wen, Leian Zhang, Xuemei Huang, Ting Yuan, Junxiong Zhang, Yuzhi Tan and Zhongbin Feng
Agronomy 2021, 11(10), 2031; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11102031 - 09 Oct 2021
Cited by 24 | Viewed by 2989
Abstract
In the process of vegetable plug seedling cultivation, packaging, and transportation, there may be missing, unhealthy or injured seedlings in the tray, which results in a missed planting or a low seedling survival rate after automatic transplanting. In this study, a seedling selection [...] Read more.
In the process of vegetable plug seedling cultivation, packaging, and transportation, there may be missing, unhealthy or injured seedlings in the tray, which results in a missed planting or a low seedling survival rate after automatic transplanting. In this study, a seedling selection system with the function of seedlings identification, week seedlings elimination, and missing seedlings supplement was developed for an automatic transplanter. A plug seedling identification system based on a machine vision was used to detect vegetable plug seedlings based on the area characteristics of plug seedlings, stem leaves and plug bodies. The identification results were transmitted to a programmable logic controller (PLC), which controlled a nozzle to eliminate the unqualified seedlings from the conveyor belt lattice. When the empty conveyor belt lattice reaches the seedling throwing funnel, the rear conveyor belt lattice with the plug seedling is accelerated to ensure the continuity of seedlings supply. The adaptive fuzzy PID control algorithm was used to control the stepper motor of the conveyor belt to realize accurate seedling conveying and a seedling supplement. Using 30 days pepper plug seedlings as experimental seedlings, a comparative field experiment was carried out to evaluate the performance of the seedling selection system. The results showed that when the seedling selection system was turned on and the seedling extracting frequencies were 60, 80, and 100 plants/min, the success rates of plug seedling identification were 98.84%, 98.38%, and 96.99%, and the robust seedling rates were 98.05%, 97.78%, and 95.83%. The robust seedling rates were increased by 15.64%, 16.07%, and 13.89%, respectively, in contrast to turning off the seedling selection system. Full article
(This article belongs to the Special Issue AI and Agricultural Robots)
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