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Agronomy
Special Issues
Remote Sensing, GIS, and AI in Agriculture
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Remote Sensing, GIS, and AI in Agriculture

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 12707

Special Issue Editors

Dr. Eraldo Aparecido Trondoli Matricardi

E-Mail Website
Guest Editor
Department of Forestry, College of Technology, University of Brasilia, Brasília, Brazil
Interests: biomass modeling; remote sensing; tropical forests; artificial neural networks; forest fragmentation
Dr. Osmar Abílio De Carvalho Júnior

E-Mail Website
Guest Editor
Department of Geography, University of Brasilia, Brasilia 70910-900, Brazil
Interests: deep learning; digital image processing; change detection; crop mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agricultural systems are crucial for ensuring food security and sustainability, and the development of new methods that can enhance productivity and decrease the environmental impact is of great interest all around the globe. In this sense, agriculture is undergoing a significant transformation with the advent of emerging technologies, integrating high-resolution remote sensing images (spatial, spectral, and temporal), geographic information systems (GIS), global positioning systems (GPS), artificial intelligence (AI), and Big Data analysis in cloud computing systems. Therefore, this Special Issue aims to address these technological innovations in the various topics of agronomy: agricultural monitoring, yield prediction, irrigation management, methods to optimize yield (automated and accurate application of water, fertilizers, herbicides, and insecticides), precision agriculture, pest management, and agriculture-focused ecosystem services. In this challenging field, this Special Issue seeks to collect studies and discuss solutions for advancing methodologies in reliable agricultural management, monitoring, and forecasting systems from imagery information and geospatial data considering new approaches to data processing and AI.

Dr. Eraldo Matricardi
Dr. Osmar Abilio De Carvalho Junior
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
  • machine learning
  • artificial intelligence
  • agriculture
  • GIS
  • remote sensing

Published Papers (4 papers)

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Research

13 pages, 1493 KiB  
Article
Named Entity Recognition of Chinese Crop Diseases and Pests Based on RoBERTa-wwm with Adversarial Training
by Jianqin Liang, Daichao Li, Yiting Lin, Sheng Wu and Zongcai Huang
Agronomy 2023, 13(3), 941; https://doi.org/10.3390/agronomy13030941 - 22 Mar 2023
Cited by 6 | Viewed by 1528
Abstract
This paper proposes a novel model for named entity recognition of Chinese crop diseases and pests. The model is intended to solve the problems of uneven entity distribution, incomplete recognition of complex terms, and unclear entity boundaries. First, a robustly optimized BERT pre-training [...] Read more.
This paper proposes a novel model for named entity recognition of Chinese crop diseases and pests. The model is intended to solve the problems of uneven entity distribution, incomplete recognition of complex terms, and unclear entity boundaries. First, a robustly optimized BERT pre-training approach-whole word masking (RoBERTa-wwm) model is used to extract diseases and pests’ text semantics, acquiring dynamic word vectors to solve the problem of incomplete word recognition. Adversarial training is then introduced to address unclear boundaries of diseases and pest entities and to improve the generalization ability of models in an effective manner. The context features are obtained by the bi-directional gated recurrent unit (BiGRU) neural network. Finally, the optimal tag sequence is obtained by conditional random fields (CRF) decoding. A focal loss function is introduced to optimize conditional random fields (CRF) and thus solve the problem of unbalanced label classification in the sequence. The experimental results show that the model’s precision, recall, and F1 values on the crop diseases and pests corpus reached 89.23%, 90.90%, and 90.04%, respectively, demonstrating effectiveness at improving the accuracy of named entity recognition for Chinese crop diseases and pests. The named entity recognition model proposed in this study can provide a high-quality technical basis for downstream tasks such as crop diseases and pests knowledge graphs and question-answering systems. Full article
(This article belongs to the Special Issue Remote Sensing, GIS, and AI in Agriculture)
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16 pages, 12084 KiB  
Article
Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy
by Mo Wang, Jing Wang, Yunpeng Cui, Juan Liu and Li Chen
Agronomy 2022, 12(10), 2342; https://doi.org/10.3390/agronomy12102342 - 28 Sep 2022
Cited by 11 | Viewed by 3432
Abstract
Parcel-level cropland maps are an essential data source for crop yield estimation, precision agriculture, and many other agronomy applications. Here, we proposed a rice field mapping approach that combines agricultural field boundary extraction with fine-resolution satellite images and pixel-wise cropland classification with Sentinel-1 [...] Read more.
Parcel-level cropland maps are an essential data source for crop yield estimation, precision agriculture, and many other agronomy applications. Here, we proposed a rice field mapping approach that combines agricultural field boundary extraction with fine-resolution satellite images and pixel-wise cropland classification with Sentinel-1 time series SAR (Synthetic Aperture Radar) imagery. The agricultural field boundaries were delineated by image segmentation using U-net-based fully convolutional network (FCN) models. Meanwhile, a simple decision-tree classifier was developed based on rice phenology traits to extract rice pixels with time series SAR imagery. Agricultural fields were then classified as rice or non-rice by majority voting from pixel-wise classification results. The evaluation indicated that SeresNet34, as the backbone of the U-net model, had the best performance in agricultural field extraction with an IoU (Intersection over Union) of 0.801 compared to the simple U-net and ResNet-based U-net. The combination of agricultural field maps with the rice pixel detection model showed promising improvement in the accuracy and resolution of rice mapping. The produced rice field map had an IoU score of 0.953, while the User‘s Accuracy and Producer‘s Accuracy of pixel-wise rice field mapping were 0.824 and 0.816, respectively. The proposed model combination scheme merely requires a simple pixel-wise cropland classification model that incorporates the agricultural field mapping results to produce high-accuracy and high-resolution cropland maps. Full article
(This article belongs to the Special Issue Remote Sensing, GIS, and AI in Agriculture)
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16 pages, 3141 KiB  
Article
Estimation of Potato Chlorophyll Content from UAV Multispectral Images with Stacking Ensemble Algorithm
by Huanbo Yang, Yaohua Hu, Zhouzhou Zheng, Yichen Qiao, Kaili Zhang, Taifeng Guo and Jun Chen
Agronomy 2022, 12(10), 2318; https://doi.org/10.3390/agronomy12102318 - 27 Sep 2022
Cited by 12 | Viewed by 2341
Abstract
Rapid and accurate crop chlorophyll content estimation is crucial for guiding field management and improving crop yields. This study explored the potential for potato chlorophyll content estimation based on unmanned aerial vehicle (UAV) multispectral imagery. To search the optimal estimation method, three parts [...] Read more.
Rapid and accurate crop chlorophyll content estimation is crucial for guiding field management and improving crop yields. This study explored the potential for potato chlorophyll content estimation based on unmanned aerial vehicle (UAV) multispectral imagery. To search the optimal estimation method, three parts of research were conducted as following. First, a combination of support vector machines (SVM) and a gaussian mixture model (GMM) thresholding method was proposed to estimate fractional vegetation cover (FVC) during the potato growing period, and the proposed method produced efficient estimates of FVC; among all the selected vegetation indices (VIs), the soil adjusted vegetation index (SAVI) had the highest accuracy. Second, the recursive feature elimination (RFE) algorithm was utilized to screen the VIs and texture features derived from multispectral images: three Vis, including modified simple ratio (MSR), ratio vegetation index (RVI) and normalized difference vegetation index (NDVI); three texture features, including correlation in the NIR band (corr-NIR), correlation in the red-edge band (corr-Red-edge) and homogeneity in the NIR band (hom-NIR), showed higher contribution to chlorophyll content estimation. Finally, a stacking model was constructed with K-Nearest Neighbor (KNN), a light gradient boosting machine (light-GBM), SVM algorithm as the base model and linear fitting as the metamodel, and four machine learning algorithms (SVM, KNN, light-GBM and stacking) were used to build the chlorophyll content estimation model suitable for different growing seasons. The results were: (1) The performance of the estimation model could be improved based on both VIs and texture features over using single-type features, and the stacking algorithm yielded the highest estimation accuracy with an R2 value of 0.694 and an RMSE value of 0.553; (2) When FVC was added, the estimation model accuracy was further improved, and the stacking algorithm also produced the highest estimation accuracy with R2 value of 0.739, RMSE value of 0.511 (3) When comparing modeling algorithms, stacking algorithms had greater advantages in the estimation chlorophyll content with potato plants than using single machine learning algorithms. This study indicates that taking into account the combination of VIs reflecting spectral characteristics, texture features reflecting spatial information and the FVC reflecting canopy structure properties can accomplish higher chlorophyll content estimation accuracy, and the stacking algorithm can integrate the advantages of a single machine learning model, with great potential for estimation of potato chlorophyll content. Full article
(This article belongs to the Special Issue Remote Sensing, GIS, and AI in Agriculture)
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14 pages, 3098 KiB  
Article
Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification
by Jacó C. Gomes and Díbio L. Borges
Agronomy 2022, 12(8), 1733; https://doi.org/10.3390/agronomy12081733 - 22 Jul 2022
Cited by 14 | Viewed by 4152
Abstract
Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at [...] Read more.
Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains. Full article
(This article belongs to the Special Issue Remote Sensing, GIS, and AI in Agriculture)
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