Remote Sensing-Based Machine Learning Applications in Agriculture

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: closed (1 November 2023) | Viewed by 16691

Special Issue Editor

Remote Sensing and GIS Unit, International Crops Research Institute for Semi Arid Tropics Tropics, Hyderabad 502324, India
Interests: remote sensing; land cover mapping; agriculture; crop monitoring; water resources; spatial modelling

Special Issue Information

Dear Colleagues,

Agriculture is key to food security. It has the potential to improve the quality of life and livelihoods by increasing incomes and providing stable employment. However, according to the United Nations, the global population is projected to reach about 10 billion by 2050. That would demand a 50% increase in food production. The food and the nutritional demands of ballooning populations worldwide would require more precise and timely agricultural cropland products. Monitoring agriculture becomes essential in attaining food security for implementation of various agricultural serving programs. In agriculture, monitoring of crop changes, land use changes and other physical parameters helps in estimating agriculture statistics, crop yield and drought risk, which in turn helps solving problems faced by farming communities. Therefore, fast and reliable monitoring is a must. Using traditional methods, monitoring requires a large amount of satellite data downloading and processing time. Cloud computing platforms enable us to save time in downloading and processing satellite data. In recent times, machine-learning algorithms like Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) have been used by researchers to classify satellite data and their derivatives into spatial agriculture products. Additionally, usage of UAVs has been increasing to monitor the agriculture on a field scale.

This Special Issue is aimed at bringing together research reports that describe new methodologies, applications, and algorithms recently developed in monitoring agriculture. The objective of the Special Issue is to increase awareness of the studies of using various approaches and areas of research in agriculture.

Contributions are expected to deal with (but not limited to) the use of artificial intelligence, deep learning, machine learning, big data, UAVs/drones, and any of the areas described in the keywords that are related to the assessment and monitoring of agriculture.

Dr. Murali Krishna Gumma
Guest Editor

Manuscript Submission Information

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Keywords

  • remote sensing
  • multispectral sensor
  • hyperspectral sensor
  • precision agriculture
  • digital agriculture
  • artificial intelligence
  • deep learning
  • machine learning
  • big data
  • UAV/drone
  • mapping various spatial crop products
  • monitoring of different growth stages of crops
  • detection and identification of crops
  • advanced modelling techniques
  • assimilation of remote sensing data into modelling

Published Papers (9 papers)

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Research

15 pages, 5354 KiB  
Article
Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning
by Marcelo Araújo Junqueira Ferraz, Thiago Orlando Costa Barboza, Pablo de Sousa Arantes, Renzo Garcia Von Pinho and Adão Felipe dos Santos
AgriEngineering 2024, 6(1), 20-33; https://doi.org/10.3390/agriengineering6010002 - 05 Jan 2024
Viewed by 705
Abstract
The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied [...] Read more.
The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied by an RGB camera seamlessly integrated into an UAV. The overarching objective is to elucidate the viability of this technological amalgamation for accurate maize plant height estimation, facilitated by the application of advanced machine learning algorithms. The research involves the computation of key vegetation indices—NDVI, NDRE, and GNDVI—extracted from PlanetScope satellite images. Concurrently, UAV-based plant height estimation is executed using digital elevation models (DEMs). Data acquisition encompasses images captured on days 20, 29, 37, 44, 50, 61, and 71 post-sowing. The study yields compelling results: (1) Maize plant height, derived from DEMs, demonstrates a robust correlation with manual field measurements (r = 0.96) and establishes noteworthy associations with NDVI (r = 0.80), NDRE (r = 0.78), and GNDVI (r = 0.81). (2) The random forest (RF) model emerges as the frontrunner, displaying the most pronounced correlations between observed and estimated height values (r = 0.99). Additionally, the RF model’s superiority extends to performance metrics when fueled by input parameters, NDVI, NDRE, and GNDVI. This research underscores the transformative potential of combining satellite imagery, UAV technology, and machine learning for precision agriculture and maize plant height estimation. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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20 pages, 5042 KiB  
Article
Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing
by Arif K. Wijayanto, Ahmad Junaedi, Azwar A. Sujaswara, Miftakhul B. R. Khamid, Lilik B. Prasetyo, Chiharu Hongo and Hiroaki Kuze
AgriEngineering 2023, 5(4), 2000-2019; https://doi.org/10.3390/agriengineering5040123 - 01 Nov 2023
Cited by 1 | Viewed by 1235
Abstract
An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types [...] Read more.
An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types at different ages (six, nine, and twelve weeks after planting), analyzing data from four spectral bands and vegetation indices using various algorithms for classification. The results show that the neural network (NN) algorithm is superior, achieving an area under the curve value of 0.804. The twelfth week post-planting yielded the most accurate results, with green reflectance the dominant predictor, surpassing the traditional vegetation indices. This study demonstrates the rapid and effective classification of rice varieties using UAV-based multispectral sensors and NN algorithms to enhance agricultural practices and global food security. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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16 pages, 5791 KiB  
Article
Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India
by Roja Mandapati, Murali Krishna Gumma, Devender Reddy Metuku, Pavan Kumar Bellam, Pranay Panjala, Sagar Maitra and Nagaraju Maila
AgriEngineering 2023, 5(4), 1909-1924; https://doi.org/10.3390/agriengineering5040117 - 20 Oct 2023
Viewed by 1414
Abstract
Crop yield estimation has gained importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. This research focused on rice yield estimation at the field level [...] Read more.
Crop yield estimation has gained importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. This research focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Using Sentinel-2 satellite data, the rice crop was mapped with the help of ground data and machine learning algorithms, attaining an accuracy of 93.04%. Crop management data for the DSSAT tool were collected during the field visits; the model results revealed a 0.80 correlation between observed and predicted yields. Due to its strong correlation with LAI (0.82), the normalized difference vegetation index (NDVI) was selected as the critical element for integration with the model. A spatial LAI map was generated using the linear equation developed between the NDVI and LAI. The relationship between LAI and yield was used to create a spatial yield map. The study’s findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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23 pages, 6183 KiB  
Article
Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal
by Ghada Sahbeni, Balázs Székely, Peter K. Musyimi, Gábor Timár and Ritvik Sahajpal
AgriEngineering 2023, 5(4), 1766-1788; https://doi.org/10.3390/agriengineering5040109 - 09 Oct 2023
Viewed by 1925
Abstract
Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic features. [...] Read more.
Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic features. Due to Nepal’s high-relief terrain, its districts exhibit diverse geographic and soil properties, leading to a wide range of yields, which poses challenges for modeling efforts. In light of this, we evaluated the performance of two machine learning algorithms, namely, the gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost). The results demonstrated the superiority of the XGBoost-based model, achieving a determination coefficient (R2) of 0.89 and an RMSE of 0.3 t/ha for training, with an R2 of 0.61 and an RMSE of 0.42 t/ha for testing. The calibrated model improved the overall accuracy of yield estimates by up to 10% compared to GBM. Notably, total nitrogen content, slope, total column water vapor (TCWV), organic matter, and fractional vegetation cover (FVC) significantly influenced the predicted values. This study highlights the effectiveness of combining multi-source data and Sentinel-3 SLSTR, particularly proposing XGBoost as an alternative tool for accurately estimating yield at lower costs. Consequently, the findings suggest comprehensive and robust estimation models for spatially explicit yield forecasting and near-future yield projection using satellite data acquired two months before harvest. Future work can focus on assessing the suitability of agronomic practices in the region, thereby contributing to the early detection of yield anomalies and ensuring food security at the national level. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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13 pages, 3028 KiB  
Article
Automated Mapping of Cropland Boundaries Using Deep Neural Networks
by Artur Gafurov
AgriEngineering 2023, 5(3), 1568-1580; https://doi.org/10.3390/agriengineering5030097 - 12 Sep 2023
Viewed by 1264
Abstract
Accurately identifying the boundaries of agricultural land is critical to the effective management of its resources. This includes the determination of property and land rights, the prevention of non-agricultural activities on agricultural land, and the effective management of natural resources. There are various [...] Read more.
Accurately identifying the boundaries of agricultural land is critical to the effective management of its resources. This includes the determination of property and land rights, the prevention of non-agricultural activities on agricultural land, and the effective management of natural resources. There are various methods for accurate boundary detection, including traditional measurement methods and remote sensing, and the choice of the best method depends on specific objectives and conditions. This paper proposes the use of convolutional neural networks (CNNs) as an efficient and effective tool for the automatic recognition of agricultural land boundaries. The objective of this research paper is to develop an automated method for the recognition of agricultural land boundaries using deep neural networks and Sentinel 2 multispectral imagery. The Buinsky district of the Republic of Tatarstan, Russia, which is known to be an agricultural region, was chosen for this study because of the importance of the accurate detection of its agricultural land boundaries. Linknet, a deep neural network architecture with skip connections between encoder and decoder, was used for semantic segmentation to extract arable land boundaries, and transfer learning using a pre-trained EfficientNetB3 model was used to improve performance. The Linknet + EfficientNetB3 combination for semantic segmentation achieved an accuracy of 86.3% and an f1 measure of 0.924 on the validation sample. The results showed a high degree of agreement between the predicted field boundaries and the expert-validated boundaries. According to the results, the advantages of the method include its speed, scalability, and ability to detect patterns outside the study area. It is planned to improve the method by using different neural network architectures and prior recognized land use classes. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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16 pages, 11331 KiB  
Article
Mapping Shrimp Pond Dynamics: A Spatiotemporal Study Using Remote Sensing Data and Machine Learning
by Pavan Kumar Bellam, Murali Krishna Gumma, Pranay Panjala, Ismail Mohammed and Aya Suzuki
AgriEngineering 2023, 5(3), 1432-1447; https://doi.org/10.3390/agriengineering5030089 - 25 Aug 2023
Cited by 1 | Viewed by 1345
Abstract
Shrimp farming and exporting is the main income source for the southern coastal districts of the Mekong Delta. Monitoring these shrimp ponds is helpful in identifying losses incurred due to natural calamities like floods, sources of water pollution by chemicals used in shrimp [...] Read more.
Shrimp farming and exporting is the main income source for the southern coastal districts of the Mekong Delta. Monitoring these shrimp ponds is helpful in identifying losses incurred due to natural calamities like floods, sources of water pollution by chemicals used in shrimp farming, and changes in the area of cultivation with an increase in demand for shrimp production. Satellite imagery, which is consistent with good spatial resolution and helpful in providing frequent information with temporal imagery, is a better solution for monitoring these shrimp ponds remotely for a larger spatial extent. The shrimp ponds of Cai Doi Vam township, Ca Mau Province, Viet Nam, were mapped using DMC-3 (TripleSat) and Jilin-1 high-resolution satellite imagery for the years 2019 and 2022. The 3 m spatial resolution shrimp pond extent product showed an overall accuracy of 87.5%, with a producer’s accuracy of 90.91% (errors of omission = 11.09%) and a user’s accuracy of 90.91% (errors of commission = 11.09%) for the shrimp pond class. It was noted that 66 ha of shrimp ponds in 2019 were observed to be dry in 2022, and 39 ha of other ponds had been converted into shrimp ponds in 2022. The continuous monitoring of shrimp ponds helps achieve sustainable aquaculture and acts as crucial input for the decision makers for any interventions. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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17 pages, 1372 KiB  
Article
Comparing Two Methods of Leaf Area Index Estimation for Rice (Oryza sativa L.) Using In-Field Spectroradiometric Measurements and Multispectral Satellite Images
by Jorge Serrano Reyes, José Ulises Jiménez, Evelyn Itzel Quirós-McIntire, Javier E. Sanchez-Galan and José R. Fábrega
AgriEngineering 2023, 5(2), 965-981; https://doi.org/10.3390/agriengineering5020060 - 29 May 2023
Viewed by 1536
Abstract
This work presents a remote sensing application to estimate the leaf area index (LAI) in two rice (Oryza sativa L.) varieties (IDIAP 52-05 and IDIAP FL 137-11), as a proxy for crop performance. In-field, homogeneous spectroradiometric measurements (350–1050 nm) were carried in [...] Read more.
This work presents a remote sensing application to estimate the leaf area index (LAI) in two rice (Oryza sativa L.) varieties (IDIAP 52-05 and IDIAP FL 137-11), as a proxy for crop performance. In-field, homogeneous spectroradiometric measurements (350–1050 nm) were carried in two campaigns (June–November 2017 and January–March 2018), on a private farm, TESKO, located in Juan Hombrón, Coclé Province, Panama. The spectral fingerprint of IDIAP 52-05 plants was collected in four dates (47, 67, 82 and 116 days after sowing), according to known phenological stages of rice plant growth. Moreover, true LAI or green leaf area was measured from representative plants and compared to LAI calculated from normalized PlanetScope multi-spectral satellite images (selected according to dates close to the in-field collection). Two distinct estimation models were used to establish the relationships of measured LAI and two vegetational spectral indices (NDVI and MTVI2). The results show that the MTVI2 based model has a slightly higher predictive ability of true LAI (R2 = 0.92, RMSE = 2.20), than the NDVI model. Furthermore, the satellite images collected were corrected and satellite LAI was contrasted with true LAI, achieving in average 18% for Model 2 for MTVI2, with the NDVI (Model 1) corrected model having a smaller error around 13%. This work provides an important advance in precision agriculture, specifically in the monitoring of total crop growth via LAI for rice crops in the Republic of Panama. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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15 pages, 1824 KiB  
Article
Garlic Field Classification Using Machine Learning and Statistic Approaches
by Imas Sukaesih Sitanggang, Intan Aida Rahmani, Wahyu Caesarendra, Muhammad Asyhar Agmalaro, Annisa Annisa and Sobir Sobir
AgriEngineering 2023, 5(1), 631-645; https://doi.org/10.3390/agriengineering5010040 - 15 Mar 2023
Cited by 1 | Viewed by 2008
Abstract
The level of garlic consumption in Indonesia increases as the population grows. This is because most of the ingredients of Indonesian food recipes contain garlic. However, local garlic production is not sufficient to fulfil the demand. Therefore, the Indonesian government imported garlic from [...] Read more.
The level of garlic consumption in Indonesia increases as the population grows. This is because most of the ingredients of Indonesian food recipes contain garlic. However, local garlic production is not sufficient to fulfil the demand. Therefore, the Indonesian government imported garlic from other countries to fulfil the demand. To reduce the import capacity of garlic, the government made a regulation to increase the potential area for garlic cultivation in several priority locations in Indonesia, one of which is Sembalun District, East Lombok. To support government regulation, this study presents an application of machine learning and a statistic approach for the garlic field mapping method in Sembalun, Indonesia. This study comprises several steps including the Sentinel-1A images data acquisition, image preprocessing, machine learning and statistic model training, and model evaluation. k-nearest neighbor (k-NN) and maximum likelihood classification (MLC) methods are selected in this study. The performance of k-NN and MLC are compared to other garlic field classification results developed in previous studies using pixel-based and image-based classifications. The comparison results show that the k-NN classification is slightly better than the SVM classification and also that it outperformed the MLC method. In addition, MLC works faster than k-NN in learning the dataset and testing the models. The classification results can be used to estimate garlic production in the study area. The study concludes that the proposed methods are better than other classification models and the statistic approach. The future study will improve dataset quality to increase the model’s accuracy. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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12 pages, 11123 KiB  
Article
Accuracy Comparison of YOLOv7 and YOLOv4 Regarding Image Annotation Quality for Apple Flower Bud Classification
by Wenan Yuan
AgriEngineering 2023, 5(1), 413-424; https://doi.org/10.3390/agriengineering5010027 - 20 Feb 2023
Cited by 12 | Viewed by 4146
Abstract
Object detection is one of the most promising research topics currently, whose application in agriculture, however, can be challenged by the difficulty of annotating complex and crowded scenes. This study presents a brief performance assessment of YOLOv7, the state-of-the-art object detector, in comparison [...] Read more.
Object detection is one of the most promising research topics currently, whose application in agriculture, however, can be challenged by the difficulty of annotating complex and crowded scenes. This study presents a brief performance assessment of YOLOv7, the state-of-the-art object detector, in comparison to YOLOv4 for apple flower bud classification using datasets with artificially manipulated image annotation qualities from 100% to 5%. Seven YOLOv7 models were developed and compared to corresponding YOLOv4 models in terms of average precisions (APs) of four apple flower bud growth stages and mean APs (mAPs). Based on the same test dataset, YOLOv7 outperformed YOLOv4 for all growth stages at all training image annotation quality levels. A 0.80 mAP was achieved by YOLOv7 with 100% training image annotation quality, meanwhile a 0.63 mAP was achieved with only 5% training image annotation quality. YOLOv7 improved YOLOv4 APs by 1.52% to 166.48% and mAPs by 3.43% to 53.45%, depending on the apple flower bud growth stage and training image annotation quality. Fewer training instances were required by YOLOv7 than YOLOv4 to achieve the same levels of classification accuracies. The most YOLOv7 AP increase was observed in the training instance number range of roughly 0 to 2000. It was concluded that YOLOv7 is undoubtedly a superior apple flower bud classifier than YOLOv4, especially when training image annotation quality is suboptimal. Full article
(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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