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Data-Driven Approaches and State-of-the-Art Machine Learning Techniques in Support of the Remote Sensing and Agriculture

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 30858

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Special Issue Editors

Dr. Inkyu Sa

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Guest Editor

Queensland Centre for Advanced Technologies (QCAT), Pullenvale, QLD 4069, Australia
Interests: UAV; robot vision; state estimation; deep learning in agriculture (horticulture); reinforcement learning

Dr. Marija Popović

E-Mail Website
Guest Editor

Cluster of Excellence "PhenoRob", Rheinische Friedrich-Wilhelms-Universität Bonn, Niebuhrstraße 1a, 53113 Bonn, Germany
Interests: active sensing; environmental mapping; informative path planning; robotic decision-making; agricultural robotics

Dr. Ho Seok Ahn

E-Mail Website
Guest Editor

Centre for Automation and Robotic Engineering Science, Department of Electrical and Computer Engineering, University of Auckland, Auckland, New Zealand
Interests: agricultural robots; IPT; smart farm; human-robot interaction
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Special Issue Information

Dear Colleagues,

As we have witnessed for decades, high-quality sensing systems and high-fidelity datasets play a pivotal role in agricultural scenarios. High-resolution and multi- or hyperspectral vegetation images promise to help to identify and distinguish early-stage vital crop diseases through state-of-the-art, data-driven machine learning approaches. This, in turn, will prevent wide spreading at an early stage (e.g., golden time) and will ultimately help to increase total yield estimation.

Multimodal sensing systems and datasets can also significantly enhance the performance of machine learning methods by providing a more discriminative and abundant source of information, such as metadata (e.g., spatial and temporal seasonal temperature estimates) or bandwise imageries and LiDAR information.

Since the early stage of agriculture, remote sensing has been considered one of the major sources of data for subsequent analysis in this context, including predictive and prescriptive analytics and plant phenotyping. Furthermore, the recent glory of deep learning and artificial intelligence are built upon large volumes of datasets in diverse environments such as on-/off-farm (e.g., fruit logistic industry) or laboratory settings. In this sense, remote data capture systems in agriculture and horticulture serve as an important supplier, feeding essential data in a timely manner.

This Special Issue of Remote Sensing will focus on data-driven approaches and state-of-the art machine learning techniques in support of remote sensing and agriculture. We are seeking papers related (but not limited) to the following topics.

Data-driven approaches for remote sensing and agriculture;
Approaches to cost-effective sensing for day/night continuous operation;
Multimodal sensing using heterogeneous sensors in remote agriculture location;
Aerial and ground data capture approaches in agriculture and precision farming;
Sensor suite for soil/crop monitoring, prediction, and decision making;
Theoretical and empirical data-driven techniques, including machine learning;
Satellite imagery for environmental and agricultural applications;
Sensing and yield estimation in precision agriculture;
Horticultural (fruit and flower) data capture using vision or multimodality sensors (e.g., LiDAR and vision);
Approaches to cost-effective sensing for day/night continuous operation;
Long-term spatiotemporal data capture in unstructured farming environments;
Proprioceptive and exteroceptive sensing for soil preparation, seeding, crop protection, and harvesting;
Adaptive sampling and informative data collection;
Adaptive technologies that manage plants, soil or animals according to as-sensed status;
High-fidelity agricultural dataset for supervised and unsupervised deep learning.

Dr. Inkyu Sa
Dr. Marija Popović
Dr. Ho Seok Ahn
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

Precision farming
Crop phenotyping
Predictive and prescriptive analytics
Multimodal sensing
Remote sensing
Deep learning
Supervised-, unsupervised-, semi-supervised learning
Artificial intelligence
Drone in agriculture
Agricultural dataset
Aerial and ground robotics
Multispectral images
Hyperspectral images
Satellite images
Radar images
Thermal images
Proprioceptive and exteroceptive sensing
LiDAR
Sensor fusion
Spatiotemporal sensing
Acoustic sensing
In situ data sampling
Informative sampling

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Published Papers (6 papers)

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Research

27 pages, 14881 KiB

Open AccessArticle

Mapping Irrigated Croplands from Sentinel-2 Images Using Deep Convolutional Neural Networks

by Wei Li, Ying Sun, Yanqing Zhou, Lu Gong, Yaoming Li and Qinchuan Xin

Remote Sens. 2023, 15(16), 4071; https://doi.org/10.3390/rs15164071 - 17 Aug 2023

Cited by 1 | Viewed by 1850

Abstract

Understanding the spatial distribution of irrigated croplands is crucial for food security and water use. To map land cover classes with high-spatial-resolution images, it is necessary to analyze the semantic information of target objects in addition to the spectral or spatial–spectral information of local pixels. Deep convolutional neural networks (DCNNs) can characterize the semantic features of objects adaptively. This study uses DCNNs to extract irrigated croplands from Sentinel-2 images in the states of Washington and California in the United States. We integrated the DCNNs of 101 layers, discarded pooling layers, and employed dilation convolution to preserve location information; these are models which were used based on fully convolutional network (FCN) architectures. The findings indicated that irrigated croplands may be effectively detected at various phases of crop growth in the fields. A quantitative analysis of the trained models revealed that the three models in the two states had the lowest values of Intersection over Union (IoU) and Kappa, i.e., 0.88 and 0.91, respectively. The deep models’ temporal portability across different years was acceptable. The lowest values of recall and OA (overall accuracy) from 2018 to 2021 were 0.91 and 0.87, respectively. In Washington, the lowest OA value from 10 to 300 m resolution was 0.76. This study demonstrates the potential of FCNs + DCNNs approaches for mapping irrigated croplands across large regions, providing a solution for irrigation mapping. The spatial resolution portability of deep models could be improved further by designing model architectures. Full article

(This article belongs to the Special Issue Data-Driven Approaches and State-of-the-Art Machine Learning Techniques in Support of the Remote Sensing and Agriculture)

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17 pages, 6041 KiB

Open AccessArticle

Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images

by Javier Rodriguez-Vazquez, Miguel Fernandez-Cortizas, David Perez-Saura, Martin Molina and Pascual Campoy

Remote Sens. 2023, 15(6), 1700; https://doi.org/10.3390/rs15061700 - 22 Mar 2023

Cited by 2 | Viewed by 2362

Abstract

This paper presents a novel semi-supervised approach for accurate counting and localization of tropical plants in aerial images that can work in new visual domains in which the available data are not labeled. Our approach uses deep learning and domain adaptation, designed to handle domain shifts between the training and test data, which is a common challenge in this agricultural applications. This method uses a source dataset with annotated plants and a target dataset without annotations and adapts a model trained on the source dataset to the target dataset using unsupervised domain alignment and pseudolabeling. The experimental results show the effectiveness of this approach for plant counting in aerial images of pineapples under significative domain shift, achieving a reduction up to 97% in the counting error (1.42 in absolute count) when compared to the supervised baseline (48.6 in absolute count). Full article

(This article belongs to the Special Issue Data-Driven Approaches and State-of-the-Art Machine Learning Techniques in Support of the Remote Sensing and Agriculture)

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17 pages, 8610 KiB

Open AccessArticle

Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Images

by Ignazio Gallo, Anwar Ur Rehman, Ramin Heidarian Dehkordi, Nicola Landro, Riccardo La Grassa and Mirco Boschetti

Remote Sens. 2023, 15(2), 539; https://doi.org/10.3390/rs15020539 - 16 Jan 2023

Cited by 89 | Viewed by 13285

Abstract

Weeds are a crucial threat to agriculture, and in order to preserve crop productivity, spreading agrochemicals is a common practice with a potential negative impact on the environment. Methods that can support intelligent application are needed. Therefore, identification and mapping is a critical step in performing site-specific weed management. Unmanned aerial vehicle (UAV) data streams are considered the best for weed detection due to the high resolution and flexibility of data acquisition and the spatial explicit dimensions of imagery. However, with the existence of unstructured crop conditions and the high biological variation of weeds, it remains a difficult challenge to generate accurate weed recognition and detection models. Two critical barriers to tackling this challenge are related to (1) a lack of case-specific, large, and comprehensive weed UAV image datasets for the crop of interest, (2) defining the most appropriate computer vision (CV) weed detection models to assess the operationality of detection approaches in real case conditions. Deep Learning (DL) algorithms, appropriately trained to deal with the real case complexity of UAV data in agriculture, can provide valid alternative solutions with respect to standard CV approaches for an accurate weed recognition model. In this framework, this paper first introduces a new weed and crop dataset named Chicory Plant (CP) and then tests state-of-the-art DL algorithms for object detection. A total of 12,113 bounding box annotations were generated to identify weed targets (Mercurialis annua) from more than 3000 RGB images of chicory plantations, collected using a UAV system at various stages of crop and weed growth. Deep weed object detection was conducted by testing the most recent You Only Look Once version 7 (YOLOv7) on both the CP and publicly available datasets (Lincoln beet (LB)), for which a previous version of YOLO was used to map weeds and crops. The YOLOv7 results obtained for the CP dataset were encouraging, outperforming the other YOLO variants by producing value metrics of 56.6%, 62.1%, and 61.3% for the [email protected] scores, recall, and precision, respectively. Furthermore, the YOLOv7 model applied to the LB dataset surpassed the existing published results by increasing the [email protected] scores from 51% to 61%, 67.5% to 74.1%, and 34.6% to 48% for the total mAP, mAP for weeds, and mAP for sugar beets, respectively. This study illustrates the potential of the YOLOv7 model for weed detection but remarks on the fundamental needs of large-scale, annotated weed datasets to develop and evaluate models in real-case field circumstances. Full article

(This article belongs to the Special Issue Data-Driven Approaches and State-of-the-Art Machine Learning Techniques in Support of the Remote Sensing and Agriculture)

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24 pages, 18321 KiB

Open AccessArticle

Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction

by Mailson Freire de Oliveira, Brenda Valeska Ortiz, Guilherme Trimer Morata, Andrés-F Jiménez, Glauco de Souza Rolim and Rouverson Pereira da Silva

Remote Sens. 2022, 14(23), 6171; https://doi.org/10.3390/rs14236171 - 6 Dec 2022

Cited by 10 | Viewed by 3645

Abstract

Methods using remote sensing associated with artificial intelligence to forecast corn yield at the management zone level can help farmers understand the spatial variability of yield before harvesting. Here, spectral bands, topographic wetness index, and topographic position index were integrated to predict corn yield at the management zone using machine learning approaches (e.g., extremely randomized trees, gradient boosting machine, XGBoost algorithms, and stacked ensemble models). We tested four approaches: only spectral bands, spectral bands + topographic position index, spectral bands + topographic wetness index, and spectral bands + topographic position index + topographic wetness index. We also explored two approaches for model calibration: the whole-field approach and the site-specific model at the management zone level. The model’s performance was evaluated in terms of accuracy (mean absolute error) and tendency (estimated mean error). The results showed that it is possible to predict corn yield with reasonable accuracy using spectral crop information associated with the topographic wetness index and topographic position index during the flowering growth stage. Site-specific models increase the accuracy and reduce the tendency of corn yield forecasting on management zones with high, low, and intermediate yields. Full article

(This article belongs to the Special Issue Data-Driven Approaches and State-of-the-Art Machine Learning Techniques in Support of the Remote Sensing and Agriculture)

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25 pages, 60551 KiB

Open AccessArticle

Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods

by Osman Ilniyaz, Alishir Kurban and Qingyun Du

Remote Sens. 2022, 14(2), 415; https://doi.org/10.3390/rs14020415 - 17 Jan 2022

Cited by 20 | Viewed by 4404

Abstract

The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R² and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively; those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework. Full article

(This article belongs to the Special Issue Data-Driven Approaches and State-of-the-Art Machine Learning Techniques in Support of the Remote Sensing and Agriculture)

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21 pages, 10869 KiB

Open AccessArticle

Spatial and Temporal Analyses of Vegetation Changes at Multiple Time Scales in the Qilian Mountains

by Lifeng Zhang, Haowen Yan, Lisha Qiu, Shengpeng Cao, Yi He and Guojin Pang

Remote Sens. 2021, 13(24), 5046; https://doi.org/10.3390/rs13245046 - 12 Dec 2021

Cited by 25 | Viewed by 3817

Abstract

The Qilian Mountains (QLMs), an important ecological protective barrier and major water resource connotation area in the Hexi Corridor region, have an important impact on ecological security in western China due to their ecological changes. However, most existing studies have investigated vegetation changes and their main driving forces in the QLMs on the basis of a single scale. Thus, the interactions among multiple environmental factors in the QLMs are still unclear. This study was based on normalised difference vegetation index (NDVI) data from 2000 to 2019. We systematically analysed the spatial and temporal characteristics of the QLMs at multiple time scales using trend analysis, ensemble empirical mode decomposition, Geodetector, and correlation analysis methods. At different time scales under single-factor and multi-factor interactions, we examined the mechanisms of the vegetation changes and their drivers. Our results showed that the vegetation in the QLMs showed a trend of overall improvement in 2000–2019, at a rate of 0.88 × 10⁻³, mainly in the central western regions. The NDVI in the QLMs showed a short change cycle of 3 and 5 years and a long-term trend. Sunshine time and wind speed were the main drivers of the vegetation variation in the QLMs, followed by temperature. Precipitation affected the vegetation spatial variation within a certain altitude range. However, temperature and precipitation had stronger explanatory powers for the vegetation variation in the western QLMs than in the eastern part. Their interaction was the dominant factor in the regional differences in vegetation. The responses of the NDVI to temperature and precipitation were stronger in the long time series. The main drivers of vegetation variation were land surface temperature and precipitation in the east and temperature and evapotranspiration in the west. Precipitation was the main driver of vegetation growth in the northern and southwestern QLMs on both the short- and long-term scales. Vegetation changes were more significantly influenced by short-term temperature changes in the east but by a combination of temperature and precipitation in most parts of the QLMs on a 5-year time scale. Full article

(This article belongs to the Special Issue Data-Driven Approaches and State-of-the-Art Machine Learning Techniques in Support of the Remote Sensing and Agriculture)

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