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Agronomy
Special Issues
Applications of Deep Learning Techniques in Agronomy
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Applications of Deep Learning Techniques in Agronomy

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

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 4204

Special Issue Editor

Dr. Peng Fu

E-Mail Website
Guest Editor
Center for Environment, Energy, and Economy, Harrisburg University of Science and Technology, Harrisburg, PA, USA
Interests: remote sensing; plant physiology; urban climate; soil science; machine learning; digital agriculture; ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to the United Nations predictions, the world population will increase by 2 billion by 2050, requiring more than 50% increase in food productivity to sustain the rising demands for food, fuel, and clothing. However, our current rates of improvements in food production fall far behind the population growth, and in some regions the rates for major crops are already stagnant and even reversing. This is of huge concern particularly as the looing climate change will add additional, tremendous pressure on food productivity. To this end, new revolutionized techniques such as deep/machine learning are necessitated as one of the potential solutions to help close the gap in expected improvements in food productivity to feed additional 2 billion people by 2050. 

In this special issue, we call for contributions that focus on leveraging artificial intelligence, machine learning, IOT sensors, remote/proximal sensing, and/or other new/emerging techniques to improve crop yields, increase agricultural efficiencies, and reduce food production costs. Potential topics include, but are not limited to, the following:

  1. Deep/machine learning in high-throughput phenotyping
  2. Crop yield prediction through deep and/or machine learning and various data streams
  3. Drones and deep learning in agriculture monitoring
  4. Effective irrigation through deep and machine learning
  5. AI-based soil chemical analysis and fertilization
  6. Crop disease mapping and management
  7. Data analytics for decision support
  8. The use of deep/machine learning in crop optimization and modeling
  9. Automation and robotics for reducing manual work in fields

Dr. Peng Fu
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

  • deep learning
  • remote sensing
  • crop yield
  • irrigation
  • drones
  • soil
  • disease
  • crop modeling

Published Papers (2 papers)

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Research

15 pages, 445 KiB  
Article
Deep Learning-Based Method for Classification of Sugarcane Varieties
by Priscila Marques Kai, Bruna Mendes de Oliveira and Ronaldo Martins da Costa
Agronomy 2022, 12(11), 2722; https://doi.org/10.3390/agronomy12112722 - 02 Nov 2022
Cited by 7 | Viewed by 1974
Abstract
The classification of sugarcane varieties using products derived from remote sensing allows for the monitoring of plants with different profiles without necessarily having physical contact with the study objects. However, differentiating between varieties can be challenging due to the similarity of the spectral [...] Read more.
The classification of sugarcane varieties using products derived from remote sensing allows for the monitoring of plants with different profiles without necessarily having physical contact with the study objects. However, differentiating between varieties can be challenging due to the similarity of the spectral characteristics of each crop. Thus, this study aimed to classify four sugarcane varieties through deep neural networks, subsequently comparing the results with traditional machine learning techniques. In order to provide more data as input for the classification models, along with the multi-band values of the pixels and vegetation indices, other information can be obtained from the sensor bands through RGB combinations by reconciling different bands so as to yield the characteristics of crop varieties. The methodology created to discriminate sugarcane varieties consisted of a dense neural network, with the number of hidden layers determined by the greedy layer-wise method and multiples of four neurons in each layer; additionally, a 5-fold evaluation in the training data was composed of Sentinel-2 band data, vegetation indices, and RGB combinations. Comparing the results acquired from each model with the hyperparameters selected by Bayesian optimisation, except for the neural network with manually defined parameters, it was possible to observe a greater precision of 99.55% in the SVM model, followed by the neural network developed by the study, random forests, and kNN. However, the final neural network model prediction resulted in the 99.48% accuracy of a six-hidden-layers network, demonstrating the potential of using neural networks in classification. Among the characteristics that contributed the most to the classification, the chlorophyll-sensitive bands, especially B6, B7, B11, and some RGB combinations, had the most impact on the correct classification of samples by the neural network model. Thus, the regions encompassing the near-infrared and shortwave infrared regions proved to be suitable for the discrimination of sugarcane varieties. Full article
(This article belongs to the Special Issue Applications of Deep Learning Techniques in Agronomy)
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16 pages, 2768 KiB  
Article
Estimation of Fusarium Head Blight Severity Based on Transfer Learning
by Chunfeng Gao, Zheng Gong, Xingjie Ji, Mengjia Dang, Qiang He, Heguang Sun and Wei Guo
Agronomy 2022, 12(8), 1876; https://doi.org/10.3390/agronomy12081876 - 10 Aug 2022
Cited by 10 | Viewed by 1594
Abstract
The recognition accuracy of traditional image recognition methods is heavily dependent on the design of complicated and tedious hand-crafted features. In view of the problems of poor accuracy and complicated feature extraction, this study presents a methodology for the estimation of the severity [...] Read more.
The recognition accuracy of traditional image recognition methods is heavily dependent on the design of complicated and tedious hand-crafted features. In view of the problems of poor accuracy and complicated feature extraction, this study presents a methodology for the estimation of the severity of wheat Fusarium head blight (FHB) with a small sample dataset based on transfer learning technology and convolutional neural networks (CNNs). Firstly, we utilized the potent feature learning and feature expression capabilities of CNNs to realize the automatic learning of FHB characteristics. Using transfer learning technology, VGG16, ResNet50, and MobileNetV1 models were pre-trained on the ImageNet. The knowledge was transferred to the estimation of FHB severity, and the fully connected (FC) layer of the models was modified. Secondly, acquiring the wheat images at the peak of the outbreak of FHB as the research object, after preprocessing for size filling on the wheat images, the image dataset was expanded with operations such as mirror flip, rotation transformation, and superimposed noise to improve the performance of the model and reduce the overfitting of models. Finally, under the Tensorflow deep learning framework, the VGG16, ResNet50, and MobileNetV1 models were subjected to transfer learning. The results showed that in the case of transfer learning and data augmentation, the ResNet50 model in Accuracy, Precision, Recall, and F1 score was better than the other two models, giving the highest accuracy of 98.42% and F1 score of 97.86%. The ResNet50 model had the highest recognition accuracy, providing technical support and reference for the accurate recognition of FHB. Full article
(This article belongs to the Special Issue Applications of Deep Learning Techniques in Agronomy)
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