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Agronomy
Special Issues
Data-Driven Agricultural Innovations
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Data-Driven Agricultural Innovations

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 October 2021) | Viewed by 36342

Special Issue Editor

Dr. Paul Kwan

E-Mail Website
Guest Editor
School of Engineering and Technology, CQUniversity Brisbane, 160 Ann St., Brisbane City, QLD 4000, Australia
Interests: artificial intelligence; pattern recognition; computer vision; machine learning; computational science; data science; digital agriculture; agroinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agricultural sector has a rich history of innovations and adoption of novel technologies for productivity increase, risk reduction, and sustainability improvement. There is a growing trend of digital and communications technology uptake by farmers and policy makers alike to address issues that have arisen due to climate change, scarcity of resources, rising global population, disruption of supply chains by the pandemic and by natural disasters, etc., which can hamper efficiency and significantly impact business models in the agricultural sector. The opportunities are numerous, but they also come with new challenges.

In both industry and academia, new research and development projects have been proposed and undertaken to address some of these issues. Innovations including monitoring and controlling crop irrigation systems via smartphone, crop sensors, livestock farming technology, farm automation, indoor vertical farming, modern greenhouses, precision agriculture, blockchain, and artificial intelligence have all benefited the agricultural sector. One characteristic that these technologies have in common is the importance of “data” in driving success in achieving their proposed benefits. 

This Special Issue will cover “Data-Driven Agricultural Innovations” across different data scales and resolutions. Contributions are welcome to address key issues in data collection, (big) data analysis, data storage, data management, and data transfer and sharing through applying scientific approaches and methodologies from multiple disciplines, including Artificial Intelligence, machine learning, computer vision, data science, geographical information system, remote sensing, crop science, horticulture, and animal science, to name a few.

Prof. Dr. Paul Kwan
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

  • agriculture
  • agronomy
  • artificial intelligence
  • data science
  • information systems
  • robotics

Published Papers (4 papers)

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Research

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16 pages, 5127 KiB  
Article
Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle
by Ali Shojaeipour, Greg Falzon, Paul Kwan, Nooshin Hadavi, Frances C. Cowley and David Paul
Agronomy 2021, 11(11), 2365; https://doi.org/10.3390/agronomy11112365 - 22 Nov 2021
Cited by 21 | Viewed by 8322
Abstract
Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these [...] Read more.
Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these initial findings into a practical precision livestock monitoring system, which can be deployed at scale for large herds. The objective of this study was to investigate and address key limitations to the autonomous biometric identification of cattle. The contributions of this work are fourfold: (1) provision of a large publicly-available dataset of cattle face images (300 individual cattle) to facilitate further research in this field, (2) development of a two-stage YOLOv3-ResNet50 algorithm that first detects and extracts the cattle muzzle region in images and then applies deep transfer learning for biometric identification, (3) evaluation of model performance across a range of cattle breeds, and (4) utilizing few-shot learning (five images per individual) to greatly reduce both the data collection requirements and duration of model training. Results indicated excellent model performance. Muzzle detection accuracy was 99.13% (1024 × 1024 image resolution) and biometric identification achieved 99.11% testing accuracy. Overall, the two-stage YOLOv3-ResNet50 algorithm proposed has substantial potential to form the foundation of a highly accurate automated cattle biometric identification system, which is applicable in livestock farming systems. The obtained results indicate that utilizing livestock biometric monitoring in an advanced manner for resource management at multiple scales of production is possible for future agriculture decision support systems, including providing useful information to forecast acceptable stocking rates of pastures. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations)
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16 pages, 965 KiB  
Article
The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics
by Edmund J. Sadgrove, Greg Falzon, David Miron and David W. Lamb
Agronomy 2021, 11(11), 2290; https://doi.org/10.3390/agronomy11112290 - 12 Nov 2021
Cited by 5 | Viewed by 1956
Abstract
This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. [...] Read more.
This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature Extreme Learning Machine (CF-ELM) is used in this study due to its additional ability to extract colour image features. The SCF-ELM is an ensemble learner that utilizes feature mapping via k-means clustering, a decision matrix and majority voting. It has been evaluated on a range of challenging agricultural object classification scenarios including weed, livestock and machinery detection. SCF-ELM model performance results were excellent both in terms of detection, 90 to 99% accuracy, and also inference times, around 0.01(s) per image. The SCF-ELM was able to compete or improve upon established algorithms in its class, indicating its potential for remote computing applications in agriculture. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations)
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14 pages, 4142 KiB  
Article
Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture
by Maria Teresa Linaza, Jorge Posada, Jürgen Bund, Peter Eisert, Marco Quartulli, Jürgen Döllner, Alain Pagani, Igor G. Olaizola, Andre Barriguinha, Theocharis Moysiadis and Laurent Lucat
Agronomy 2021, 11(6), 1227; https://doi.org/10.3390/agronomy11061227 - 17 Jun 2021
Cited by 63 | Viewed by 11494
Abstract
One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding difficulty in systematic data gathering, as no two fields are exactly alike. Therefore, the comparison of several pilot experiments in different fields, [...] Read more.
One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding difficulty in systematic data gathering, as no two fields are exactly alike. Therefore, the comparison of several pilot experiments in different fields, weather conditions and farming techniques enhances the collective knowledge. Thus, this work provides a summary of the most recent research activities in the form of research projects implemented and validated by the authors in several European countries, with the objective of presenting the already achieved results, the current investigations and the still open technical challenges. As an overall conclusion, it can be mentioned that even though in their primary stages in some cases, AI technologies improve decision support at farm level, monitoring conditions and optimizing production to allow farmers to apply the optimal number of inputs for each crop, thereby boosting yields and reducing water use and greenhouse gas emissions. Future extensions of this work will include new concepts based on autonomous and intelligent robots for plant and soil sample retrieval, and effective livestock management. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations)
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Review

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21 pages, 3724 KiB  
Review
A Revisit of Internet of Things Technologies for Monitoring and Control Strategies in Smart Agriculture
by Amjad Rehman, Tanzila Saba, Muhammad Kashif, Suliman Mohamed Fati, Saeed Ali Bahaj and Huma Chaudhry
Agronomy 2022, 12(1), 127; https://doi.org/10.3390/agronomy12010127 - 05 Jan 2022
Cited by 92 | Viewed by 13229
Abstract
With the rise of new technologies, such as the Internet of Things, raising the productivity of agricultural and farming activities is critical to improving yields and cost-effectiveness. IoT, in particular, can improve the efficiency of agriculture and farming processes by eliminating human intervention [...] Read more.
With the rise of new technologies, such as the Internet of Things, raising the productivity of agricultural and farming activities is critical to improving yields and cost-effectiveness. IoT, in particular, can improve the efficiency of agriculture and farming processes by eliminating human intervention through automation. The fast rise of Internet of Things (IoT)-based tools has changed nearly all life sectors, including business, agriculture, surveillance, etc. These radical developments are upending traditional agricultural practices and presenting new options in the face of various obstacles. IoT aids in collecting data that is useful in the farming sector, such as changes in climatic conditions, soil fertility, amount of water required for crops, irrigation, insect and pest detection, bug location disruption of creatures to the sphere, and horticulture. IoT enables farmers to effectively use technology to monitor their forms remotely round the clock. Several sensors, including distributed WSNs (wireless sensor networks), are utilized for agricultural inspection and control, which is very important due to their exact output and utilization. In addition, cameras are utilized to keep an eye on the field from afar. The goal of this research is to evaluate smart agriculture using IoT approaches in depth. The paper demonstrates IoT applications, benefits, current obstacles, and potential solutions in smart agriculture. This smart agricultural system aims to find existing techniques that may be used to boost crop yield and save time, such as water, pesticides, irrigation, crop, and fertilizer management. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations)
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