Deep Learning Techniques for Agronomy Applications

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: closed (30 December 2018) | Viewed by 41801

Special Issue Editors

Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
Interests: machine learning; agronomy applications; mobile communications
Special Issues, Collections and Topics in MDPI journals
School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: scheduling; linear programming; integer programming; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the techniques of deep learning have become more and more popular for various applications in agronomy. These techniques can be used to support the prediction and prevention of pest disasters, drought disasters, flooding disasters, typhoon disasters, cold damages, and other agricultural disasters. Furthermore, crop growth models can be also built using these techniques. For instance, supervised learning techniques (e.g., neural network (NN), convolutional neural network (CNN), recurrent neural network (RNN), and ensemble neural networks (ENN)) can be used to forecast weather information and crop growth for improving crop quantities and reducing disaster damages. Furthermore, unsupervised learning techniques (e.g., auto-encoder (AE), de-noise auto-encoder (DAE), restricted Boltzmann machine (RBM), deep belief network (DBN), and deep Boltzmann machine (RBM)) can be used to represent data and reduce dimensions for regulation and overfitting prevention. Therefore, the combination of supervised learning and unsupervised learning techniques can provide a precise estimation and prediction for agronomy applications.

This Special Issue, named “Deep Learning Techniques for Agronomy Applications”, in Agronomy will solicit papers on various disciplines of agronomy applications, but are not limited to:

  • The Prediction of Crop Growth
  • The Prediction and Prevention of Pest Disasters
  • The Prediction and Prevention of Drought Disasters
  • The Prediction and Prevention of Flooding Disasters
  • The Prediction and Prevention of Typhoon Disasters
  • The Prediction and Prevention of Cold Damages
  • The Prediction and Prevention of Agricultural Disasters
  • The Prediction of Crop Quantities
  • Agronomy Applications Based on Deep Learning
  • Agronomy Applications Based on Machine Learning

Best regards,

Dr. Chi-Hua Chen
Dr. Hsu-Yang Kung
Dr. Feng-Jang Hwang
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. 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

  • deep learning for agronomy applications
  • crop growth prediction; pest disaster prediction
  • drought disaster prediction
  • flooding disaster prediction
  • typhoon disaster prediction
  • cold damage prediction

Published Papers (6 papers)

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Editorial

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5 pages, 199 KiB  
Editorial
Deep Learning Techniques for Agronomy Applications
by Chi-Hua Chen, Hsu-Yang Kung and Feng-Jang Hwang
Agronomy 2019, 9(3), 142; https://doi.org/10.3390/agronomy9030142 - 20 Mar 2019
Cited by 26 | Viewed by 6328
Abstract
This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, [...] Read more.
This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)

Research

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18 pages, 2199 KiB  
Article
Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques
by Juan Rodríguez Alvarez, Mauricio Arroqui, Pablo Mangudo, Juan Toloza, Daniel Jatip, Juan M. Rodriguez, Alfredo Teyseyre, Carlos Sanz, Alejandro Zunino, Claudio Machado and Cristian Mateos
Agronomy 2019, 9(2), 90; https://doi.org/10.3390/agronomy9020090 - 16 Feb 2019
Cited by 45 | Viewed by 6572
Abstract
BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are [...] Read more.
BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associated with milk production, reproduction, and health of cows. However, in practice, obtaining BCS values is a time-consuming and subjective task performed visually by expert scorers. There have been several efforts to automate BCS of dairy cows by using image analysis and machine learning techniques. In a previous work, an automatic system to estimate BCS values was proposed, which is based on Convolutional Neural Networks (CNNs). In this paper we significantly extend the techniques exploited by that system via using transfer learning and ensemble modeling techniques to further improve BCS estimation accuracy. The improved system has achieved good estimations results in comparison with the base system. Overall accuracy of BCS estimations within 0.25 units of difference from true values has increased 4% (up to 82%), while overall accuracy within 0.50 units has increased 3% (up to 97%). Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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16 pages, 1867 KiB  
Article
Research into the E-Learning Model of Agriculture Technology Companies: Analysis by Deep Learning
by Chi-Hsuan Lin, Wei-Chuan Wang, Chun-Yung Liu, Po-Nien Pan and Hou-Ru Pan
Agronomy 2019, 9(2), 83; https://doi.org/10.3390/agronomy9020083 - 13 Feb 2019
Cited by 10 | Viewed by 4452
Abstract
With the advancement of technology, the traditional e-learning model may expand the realm of knowledge and differentiate learning by means of deep learning (DL) and augmented reality (AR) scenarios. These scenarios make use of interactive interfaces that incorporate various operating methods, angles, perceptions, [...] Read more.
With the advancement of technology, the traditional e-learning model may expand the realm of knowledge and differentiate learning by means of deep learning (DL) and augmented reality (AR) scenarios. These scenarios make use of interactive interfaces that incorporate various operating methods, angles, perceptions, and experiences, and also draw on multimedia content and active interactive models. Modern education emphasizes that learning should occur in the process of constructing knowledge scenarios and should proceed through learning scenarios and activities. Compared to traditional “spoon-feeding” education, the model learning scenario is initiated with the learner at the center, allowing the person involved in the learning activity to solve problems and further develop their individual capabilities through exploring, thinking and a series of interactions and feedback. This study examined how students in the agriculture technological industry make use of AR digital learning to develop their industry-related knowledge and techniques to become stronger and more mature so that they unconsciously apply these techniques as employees, as well as encouraging innovative thought and methods to create new value for the enterprise. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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12 pages, 558 KiB  
Article
LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan
by Sajjad Ali Haider, Syed Rameez Naqvi, Tallha Akram, Gulfam Ahmad Umar, Aamir Shahzad, Muhammad Rafiq Sial, Shoaib Khaliq and Muhammad Kamran
Agronomy 2019, 9(2), 72; https://doi.org/10.3390/agronomy9020072 - 08 Feb 2019
Cited by 69 | Viewed by 8182
Abstract
Pakistan’s economy is largely driven by agriculture, and wheat, mostly, stands out as its second most produced crop every year. On the other hand, the average consumption of wheat is steadily increasing as well, due to which its exports are not proportionally growing, [...] Read more.
Pakistan’s economy is largely driven by agriculture, and wheat, mostly, stands out as its second most produced crop every year. On the other hand, the average consumption of wheat is steadily increasing as well, due to which its exports are not proportionally growing, thereby, threatening the country’s economy in the years to come. This work focuses on developing an accurate wheat production forecasting model using the Long Short Term Memory (LSTM) neural networks, which are considered to be highly accurate for time series prediction. A data pre-processing smoothing mechanism, in conjunction with the LSTM based model, is used to further improve the prediction accuracy. A comparison of the proposed mechanism with a few existing models in literature is also given. The results verify that the proposed model achieves better performance in terms of forecasting, and reveal that while the wheat production will gradually increase in the next ten years, the production to consumption ratio will continue to fall and pose threats to the overall economy. Our proposed framework, therefore, may be used as guidelines for wheat production in particular, and is amenable to other crops as well, leading to sustainable agriculture development in general. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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21 pages, 8702 KiB  
Article
Development of a Mushroom Growth Measurement System Applying Deep Learning for Image Recognition
by Chuan-Pin Lu, Jiun-Jian Liaw, Tzu-Ching Wu and Tsung-Fu Hung
Agronomy 2019, 9(1), 32; https://doi.org/10.3390/agronomy9010032 - 14 Jan 2019
Cited by 39 | Viewed by 9756
Abstract
In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several [...] Read more.
In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several complex parameters. If we can reduce the complexity such greenhouses and improve the efficiency of their production management using intelligent schemes, technological greenhouses could become the expert assistants of farmers. In this paper, the main goal of the developed system is to measure the mushroom size and to count the amount of mushrooms. According to the results of each measurement, the growth rate of the mushrooms can be estimated. The proposed system also records the data of the mushrooms and broadcasts them to the mobile phone of the farmer. This improves the effectiveness of the production management. The proposed system is based on the convolutional neural network of deep learning, which is used to localize the mushrooms in the image. A positioning correction method is also proposed to modify the localization result. The experiments show that the proposed system has a good performance concerning the image measurement of mushrooms. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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12 pages, 4357 KiB  
Article
Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks
by Jian Chen, Yangyang Fan, Tao Wang, Chu Zhang, Zhengjun Qiu and Yong He
Agronomy 2018, 8(8), 129; https://doi.org/10.3390/agronomy8080129 - 25 Jul 2018
Cited by 31 | Viewed by 4977
Abstract
The presence of pests is one of the main problems in crop production, and obtaining reliable statistics of pest infestation is essential for pest management. Detection of pests should be automated because human monitoring of pests is time-consuming and error-prone. Aphids are among [...] Read more.
The presence of pests is one of the main problems in crop production, and obtaining reliable statistics of pest infestation is essential for pest management. Detection of pests should be automated because human monitoring of pests is time-consuming and error-prone. Aphids are among the most destructive pests in greenhouses and they reproduce quickly. Automatic detection of aphid nymphs on leaves (especially on the lower surface) using image analysis is a challenging problem due to color similarity and complicated background. In this study, we propose a method for segmentation and counting of aphid nymphs on leaves using convolutional neural networks. Digital images of pakchoi leaves at different aphid infestation stages were obtained, and corresponding pixel-level binary mask annotated. In the test, segmentation results by the proposed method achieved high overlap with annotation by human experts (Dice coefficient of 0.8207). Automatic counting based on segmentation showed high precision (0.9563) and recall (0.9650). The correlation between aphid nymph count by the proposed method and manual counting was high (R2 = 0.99). The proposed method is generic and can be applied for other species of pests. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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