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AI
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Artificial Intelligence in Agriculture
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Artificial Intelligence in Agriculture

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 45840

Special Issue Editors

Dr. Yiannis Ampatzidis

E-Mail Website
Guest Editor
Southwest Florida Research and Education Center (SWFREC), University of Florida, Gainesville, FL, USA
Interests: precision agriculture; automation; robotics; UAVs; machine vision; sensing; artificial intelligence; farm machinery
Special Issues, Collections and Topics in MDPI journals
Dr. Spyros Fountas

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Interests: precision agriculture; UAV; information systems; farm machinery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wonsuk (Daniel) Lee

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611-0570, USA
Interests: precision agriculture; artificial intelligence; sensor development; machine vision/image processing; GNSS/GIS; variable rate technology; yield mapping; machine systems design; instrumentation; remote sensing; NIR spectroscopy; farm automation
Special Issues, Collections and Topics in MDPI journals
Dr. Panos M. Pardalos

E-Mail Website
Guest Editor
Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA
Interests: network design problems; optimization in telecommunications; e-commerce; data mining; biomedical applications; massive computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decades, several emerging technologies and techniques have been developed for precision agriculture application. Artificial neural networks and deep learning algorithms are increasingly used in remote sensing and machine vision applications. Deep convolutional neural networks (CNNs) are the most widely used deep learning approach for image recognition. These methods have achieved dramatic improvements in many domains, and have attracted considerable interest from both academic and industrial communities.

This Special Issue focuses on the recent advances in artificial intelligence applications in agriculture and natural resources. For this purpose, we invite researchers to contribute original research papers in the areas of machine and computer vision, Internet of things, big data analytics, automation and robotics, machine learning, deep and transfer learning, reinforcement learning, logistics and optimization, and so on.

Potential Topics include, but are not limited to, the following:

  • Remote sensing
  • UAV applications in agriculture
  • Machine and computer vision
  • Automatic tools for disease and pest detection
  • High-throughput phenotyping tools
  • Yield prediction techniques
  • Big data analytics
  • Precision agriculture
  • Digital and smart agriculture and machinery
  • Decision support systems, crop modeling, and optimization
  • Agroclimatology.

Dr. Yiannis Ampatzidis
Dr. Spyros Fountas
Prof. Dr. Wonsuk (Daniel) Lee
Dr. Panos M. Pardalos
Guest Editors

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. AI is an international peer-reviewed open access quarterly 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 1600 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

18 pages, 1609 KiB  
Article
Using Machine Learning and Feature Selection for Alfalfa Yield Prediction
by Christopher D. Whitmire, Jonathan M. Vance, Hend K. Rasheed, Ali Missaoui, Khaled M. Rasheed and Frederick W. Maier
AI 2021, 2(1), 71-88; https://doi.org/10.3390/ai2010006 - 14 Feb 2021
Cited by 35 | Viewed by 6582
Abstract
Predicting alfalfa biomass and crop yield for livestock feed is important to the daily lives of virtually everyone, and many features of data from this domain combined with corresponding weather data can be used to train machine learning models for yield prediction. In [...] Read more.
Predicting alfalfa biomass and crop yield for livestock feed is important to the daily lives of virtually everyone, and many features of data from this domain combined with corresponding weather data can be used to train machine learning models for yield prediction. In this work, we used yield data of different alfalfa varieties from multiple years in Kentucky and Georgia, and we compared the impact of different feature selection methods on machine learning (ML) models trained to predict alfalfa yield. Linear regression, regression trees, support vector machines, neural networks, Bayesian regression, and nearest neighbors were all developed with cross validation. The features used included weather data, historical yield data, and the sown date. The feature selection methods that were compared included a correlation-based method, the ReliefF method, and a wrapper method. We found that the best method was the correlation-based method, and the feature set it found consisted of the Julian day of the harvest, the number of days between the sown and harvest dates, cumulative solar radiation since the previous harvest, and cumulative rainfall since the previous harvest. Using these features, the k-nearest neighbor and random forest methods achieved an average R value over 0.95, and average mean absolute error less than 200 lbs./acre. Our top R2 of 0.90 beats a previous work’s best R2 of 0.87. Our primary contribution is the demonstration that ML, with feature selection, shows promise in predicting crop yields even on simple datasets with a handful of features, and that reporting accuracies in R and R2 offers an intuitive way to compare results among various crops. Full article
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
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14 pages, 1454 KiB  
Article
Testing the Suitability of Automated Machine Learning for Weeds Identification
by Borja Espejo-Garcia, Ioannis Malounas, Eleanna Vali and Spyros Fountas
AI 2021, 2(1), 34-47; https://doi.org/10.3390/ai2010004 - 9 Feb 2021
Cited by 18 | Viewed by 5324
Abstract
In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively [...] Read more.
In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection. Full article
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
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17 pages, 506 KiB  
Article
Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review
by Jayme Garcia Arnal Barbedo
AI 2020, 1(2), 312-328; https://doi.org/10.3390/ai1020021 - 24 Jun 2020
Cited by 44 | Viewed by 12471
Abstract
Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A [...] Read more.
Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research. Full article
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
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13 pages, 3357 KiB  
Article
Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models
by Mohammad Ebrahim Banihabib, Reihaneh Bandari and Mohammad Valipour
AI 2020, 1(2), 263-275; https://doi.org/10.3390/ai1020017 - 7 Jun 2020
Cited by 14 | Viewed by 4221
Abstract
In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily [...] Read more.
In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon forecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive Integrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow time series. Second, a Recurrent Artificial Neural Network (RANN) model is utilized to forecast FSF-ARIMA model’s residuals. The hybrid model follows the detail of observed flow time variation and forecasted peak flow more accurately than previous models. The proposed model enhances the ability to forecast reservoir inflow, especially in peak flows, compared to previous linear and nonlinear auto-regressive models. The hybrid model has a potential to decrease maximum and average forecasting error by 81% and 80%, respectively. The results of this investigation are useful for stakeholders and water resources managers to schedule optimum operation of multi-purpose reservoirs in controlling floods and generating hydropower. Full article
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
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13 pages, 3749 KiB  
Article
Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning
by Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, Pedro Medeiros Netto Ottoni and José Paulo Molin
AI 2020, 1(2), 229-241; https://doi.org/10.3390/ai1020015 - 23 May 2020
Cited by 44 | Viewed by 8261
Abstract
Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) [...] Read more.
Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield. Full article
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
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11 pages, 4496 KiB  
Article
A Study on CNN-Based Detection of Psyllids in Sticky Traps Using Multiple Image Data Sources
by Jayme Garcia Arnal Barbedo and Guilherme Barros Castro
AI 2020, 1(2), 198-208; https://doi.org/10.3390/ai1020013 - 18 May 2020
Cited by 9 | Viewed by 4307
Abstract
Deep learning architectures like Convolutional Neural Networks (CNNs) are quickly becoming the standard for detecting and counting objects in digital images. However, most of the experiments found in the literature train and test the neural networks using data from a single image source, [...] Read more.
Deep learning architectures like Convolutional Neural Networks (CNNs) are quickly becoming the standard for detecting and counting objects in digital images. However, most of the experiments found in the literature train and test the neural networks using data from a single image source, making it difficult to infer how the trained models would perform under a more diverse context. The objective of this study was to assess the robustness of models trained using data from a varying number of sources. Nine different devices were used to acquire images of yellow sticky traps containing psyllids and a wide variety of other objects, with each model being trained and tested using different data combinations. The results from the experiments were used to draw several conclusions about how the training process should be conducted and how the robustness of the trained models is influenced by data quantity and variety. Full article
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
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