Remote Sensing

Research

Jump to: Other

18 pages, 39830 KiB

Open AccessArticle

Satellite-Based Detection of Farmland Manuring Using Machine Learning Approaches

by David Marzi and Fabio Dell’Acqua

Remote Sens. 2025, 17(6), 1028; https://doi.org/10.3390/rs17061028 - 15 Mar 2025

Viewed by 459

Abstract

In agriculture, manuring offers several benefits, which include improving soil fertility, structure, water retention, and aeration; all these factors favor plant health and productivity. However, improper handling and application of manure can pose risks, such as spread of pathogens and water pollution. Mitigation [...] Read more.

In agriculture, manuring offers several benefits, which include improving soil fertility, structure, water retention, and aeration; all these factors favor plant health and productivity. However, improper handling and application of manure can pose risks, such as spread of pathogens and water pollution. Mitigation of such risks requires not only proper storage and composting practices, but also compliance with correct application periods and techniques. Spaceborne Earth observation can contribute to mapping manure applications and identifying possible critical situations, yet manure detection from satellite data is still a largely open question. The aim of this research is an automated, machine learning (ML)-based approach to detecting manure application on crop fields in time sequences of spaceborne, multi-source optical Earth Observation data. In the first stage of this research, multispectral data alone was considered; a pool of different spectral indexes were analyzed to identify the ones most impacted by manure application. Increments of the selected indexes from one satellite acquisition to the next were used as features to train and test various machine learning models. Two agricultural areas—one in Spain and one in Italy—were considered. Fair levels of accuracy were achieved when training and testing were carried out in the same geographical context, whereas ML models trained on one context and tested on the other reported significantly lower—albeit still acceptable—accuracy levels. In the stage that followed, thermal data was integrated and used alongside multispectral indexes. This addition led to significant improvements in accuracy levels, despite possible thermal-to-multispectral sampling mismatch in time series. Our results appear to indicate that ML-based approaches to manuring detection from space require training on the targeted geographical context, although transfer learning can probably be leveraged and only fine-tuning training will be needed. Spaceborne thermal data, where available, should be included in the input data pool to improve the quality of the final result. The proposed method is meant as a first step towards a suite of techniques that should enable large-scale, consistent monitoring of agricultural activities to check compliance with environmental regulations and provide enhanced traceability information for food products. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

26 pages, 9074 KiB

Open AccessArticle

Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation

by Reza Maleki, Falin Wu, Guoxin Qu, Amel Oubara, Loghman Fathollahi and Gongliu Yang

Remote Sens. 2025, 17(2), 283; https://doi.org/10.3390/rs17020283 - 15 Jan 2025

Viewed by 722

Abstract

The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences [...] Read more.

The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

21 pages, 5846 KiB

Open AccessArticle

Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection

by Feng Gao, Martha Anderson and Rasmus Houborg

Remote Sens. 2024, 16(22), 4145; https://doi.org/10.3390/rs16224145 - 7 Nov 2024

Cited by 1 | Viewed by 1351

Abstract

Crop emergence is critical for crop growth modeling, crop condition monitoring, and crop yield estimation. Ground collections of crop emergence dates are time-consuming and can only include limited fields. Remote sensing time series have been used to detect crop emergence. However, the impacts [...] Read more.

Crop emergence is critical for crop growth modeling, crop condition monitoring, and crop yield estimation. Ground collections of crop emergence dates are time-consuming and can only include limited fields. Remote sensing time series have been used to detect crop emergence. However, the impacts of the temporal and spatial resolutions of these time series on crop emergence detection have not been thoroughly evaluated. This paper assesses corn and soybean emergence detection using various remote sensing datasets (i.e., VENµS, Planet Fusion, Sentinel-2, Landsat, and Harmonized Landsat and Sentinel-2 (HLS)) with diverse spatial and temporal resolutions. The green-up dates from the remote sensing time series are detected using the within-season emergence (WISE) algorithm and assessed using ground emergence observations and planting records of corn, soybeans, and alfalfa from the Beltsville Agricultural Research Center (BARC) fields in Maryland, USA, from 2019 to 2023. Our results showed that most emergence events (~95%) could be detected when the frequency of usable observations reached ten days or less. Planet Fusion captured all crop emergences and outperformed other datasets, with a mean difference (MD) of <1 day, a mean absolute difference (MAD) of <5 days, and a root mean square error (RMSE) of <6 days compared to the ground-observed emergence dates. The HLS and Sentinel-2 time series captured most emergences of corn and soybeans with MD < 3 days, MAD < 7 days, and RMSE < 9 days. Landsat detected less than half of the crop emergences in recent years when both Landsat-8 and -9 were available. In our study area, temporal revisit plays a more crucial role in emergence detection than spatial resolution. Spatial resolutions from 5 to 30 m are suitable for field-level summaries in the study area. However, the 30 m HLS lacked sub-field details in fields with mixed cropping systems. The findings from this study could benefit remotely sensed crop emergence detection from local to regional scales. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

27 pages, 6288 KiB

Open AccessArticle

Detection of Maize Crop Phenology Using Planet Fusion

by Caglar Senaras, Maddie Grady, Akhil Singh Rana, Luciana Nieto, Ignacio Ciampitti, Piers Holden, Timothy Davis and Annett Wania

Remote Sens. 2024, 16(15), 2730; https://doi.org/10.3390/rs16152730 - 25 Jul 2024

Cited by 1 | Viewed by 1855

Abstract

Accurate identification of crop phenology timing is crucial for agriculture. While remote sensing tracks vegetation changes, linking these to ground-measured crop growth stages remains challenging. Existing methods offer broad overviews but fail to capture detailed phenological changes, which can be partially related to [...] Read more.

Accurate identification of crop phenology timing is crucial for agriculture. While remote sensing tracks vegetation changes, linking these to ground-measured crop growth stages remains challenging. Existing methods offer broad overviews but fail to capture detailed phenological changes, which can be partially related to the temporal resolution of the remote sensing datasets used. The availability of higher-frequency observations, obtained by combining sensors and gap-filling, offers the possibility to capture more subtle changes in crop development, some of which can be relevant for management decisions. One such dataset is Planet Fusion, daily analysis-ready data obtained by integrating PlanetScope imagery with public satellite sensor sources such as Sentinel-2 and Landsat. This study introduces a novel method utilizing Dynamic Time Warping applied to Planet Fusion imagery for maize phenology detection, to evaluate its effectiveness across 70 micro-stages. Unlike singular template approaches, this method preserves critical data patterns, enhancing prediction accuracy and mitigating labeling issues. During the experiments, eight commonly employed spectral indices were investigated as inputs. The method achieves high prediction accuracy, with 90% of predictions falling within a 10-day error margin, evaluated based on over 3200 observations from 208 fields. To understand the potential advantage of Planet Fusion, a comparative analysis was performed using Harmonized Landsat Sentinel-2 data. Planet Fusion outperforms Harmonized Landsat Sentinel-2, with significant improvements observed in key phenological stages such as V4, R1, and late R5. Finally, this study showcases the method’s transferability across continents and years, although additional field data are required for further validation. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

26 pages, 14613 KiB

Open AccessArticle

Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds

by Wenjie Li, Biyu Tang, Zhen Hou, Hongbo Wang, Zongyu Bing, Qiong Yang and Yongqiang Zheng

Remote Sens. 2024, 16(12), 2142; https://doi.org/10.3390/rs16122142 - 13 Jun 2024

Cited by 4 | Viewed by 1614

Abstract

Crop phenotyping data collection is the basis for precision agriculture and smart decision-making applications. Accurately obtaining the canopy volume of citrus trees is crucial for yield prediction, precise fertilization and cultivation management. To this end, we developed a dynamic slicing and reconstruction (DR) [...] Read more.

Crop phenotyping data collection is the basis for precision agriculture and smart decision-making applications. Accurately obtaining the canopy volume of citrus trees is crucial for yield prediction, precise fertilization and cultivation management. To this end, we developed a dynamic slicing and reconstruction (DR) algorithm based on 3D point clouds. The algorithm dynamically slices nearby slices based on their proportional area change and density difference; for each slice point cloud, the average distance of each point from others is taken as the initial α value for the AS algorithm. This value is iteratively summed until it reconstructs the complete shape, allowing the volume of each slice shape to be determined. Compared with six point cloud-based reconstruction algorithms, the DR approach achieved the best results in removing perforations and lacunae (0.84) and exhibited volumetric consistency (1.53) that closely aligned with the growth pattern of citrus trees. The DR algorithm effectively addresses the challenges of adapting the thickness and number of canopy point cloud slices to the shape and size of the canopy in the ASBS and CHBS algorithms, as well as overcoming inaccuracies and incompleteness in reconstructed canopy models caused by limitations in capturing detailed features using the PCH algorithm. It offers improved adaptive ability, finer volume computations, better noise reduction, and anomaly removal. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Graphical abstract

21 pages, 4584 KiB

Open AccessArticle

Estimation of Daily Maize Gross Primary Productivity by Considering Specific Leaf Nitrogen and Phenology via Machine Learning Methods

by Cenhanyi Hu, Shun Hu, Linglin Zeng, Keyu Meng, Zilong Liao and Kuang Wang

Remote Sens. 2024, 16(2), 341; https://doi.org/10.3390/rs16020341 - 15 Jan 2024

Cited by 1 | Viewed by 1729

Abstract

Maize gross primary productivity (GPP) contributes the most to the global cropland GPP, making it crucial to accurately estimate maize GPP for the global carbon cycle. Previous research validated machine learning (ML) methods using remote sensing and meteorological data to estimate plant GPP, [...] Read more.

Maize gross primary productivity (GPP) contributes the most to the global cropland GPP, making it crucial to accurately estimate maize GPP for the global carbon cycle. Previous research validated machine learning (ML) methods using remote sensing and meteorological data to estimate plant GPP, yet they disregard vegetation physiological dynamics driven by phenology. Leaf nitrogen content per unit leaf area (i.e., specific leaf nitrogen (SLN)) greatly affects photosynthesis. Its maximum allowable value correlates with a phenological factor conceptualized as normalized maize phenology (NMP). This study aims to validate SLN and NMP for maize GPP estimation using four ML methods (random forest (RF), support vector machine (SVM), convolutional neutral network (CNN), and extreme learning machine (ELM)). Inputs consist of vegetation index (NDVI), air temperature, solar radiation (SSR), NMP, and SLN. Data from four American maize flux sites (NE1, NE2, and NE3 sites in Nebraska and RO1 site in Minnesota) were gathered. Using data from three NE sites to validate the effect of SLN and MMP shows that the accuracy of four ML methods notably increased after adding SLN and MMP. Among these methods, RF and SVM achieved the best performance of Nash–Sutcliffe efficiency coefficient (NSE) = 0.9703 and 0.9706, root mean square error (RMSE) = 1.5596 and 1.5509 gC·m⁻²·d⁻¹, and coefficient of variance (CV) = 0.1508 and 0.1470, respectively. When evaluating the best ML models from three NE sites at the RO1 site, only RF and CNN could effectively incorporate the impact of SLN and NMP. But, in terms of unbiased estimation results, the four ML models were comprehensively enhanced by adding SLN and NMP. Due to their fixed relationship, introducing SLN or NMP alone might be more effective than introducing both simultaneously, considering the data redundancy for methods like CNN and ELM. This study supports the integration of phenology and leaf-level photosynthetic factors in plant GPP estimation via ML methods and provides a reference for similar research. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

22 pages, 5119 KiB

Open AccessArticle

Spatial Estimation of Daily Growth Biomass in Paddy Rice Field Using Canopy Photosynthesis Model Based on Ground and UAV Observations

by Megumi Yamashita, Tomoya Kaieda, Hiro Toyoda, Tomoaki Yamaguchi and Keisuke Katsura

Remote Sens. 2024, 16(1), 125; https://doi.org/10.3390/rs16010125 - 27 Dec 2023

Viewed by 1557

Abstract

Precision farming, a labor-saving and highly productive form of management, is gaining popularity as the number of farmers declines in comparison to the increasing global food demand. However, it requires more efficient crop phenology observation and growth monitoring. One measure is the leaf [...] Read more.

Precision farming, a labor-saving and highly productive form of management, is gaining popularity as the number of farmers declines in comparison to the increasing global food demand. However, it requires more efficient crop phenology observation and growth monitoring. One measure is the leaf area index (LAI), which is essential for estimating biomass and yield, but its validation requires destructive field measurements. Thus, using ground and UAV observation data, this study developed a method for indirect LAI estimation based on relative light intensity under a rice canopy. Daily relative light intensity was observed under the canopy at several points in paddy fields, and a weekly plant survey was conducted to measure the plant length, above-ground biomass, and LAI. Furthermore, images from ground-based and UAV-based cameras were acquired to generate NDVI and the canopy height (CH), respectively. Using the canopy photosynthetic model derived from the Beer–Lambert law, the daily biomass was estimated by applying the weekly estimated LAI using CH and the observed light intensity data as input. The results demonstrate the possibility of quantitatively estimating the daily growth biomass of rice plants, including spatial variation. The near-real-time estimation method for rice biomass by integrating observation data at fields with numerical models can be applied to the management of major crops. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

40 pages, 10942 KiB

Open AccessArticle

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

by Yuqing Hou, Yunfei Wu, Linsheng Wu, Lei Pei, Zhaoying Zhang, Dawei Ding, Guangshuai Wang, Zhongyang Li and Yongguang Zhang

Remote Sens. 2023, 15(24), 5689; https://doi.org/10.3390/rs15245689 - 11 Dec 2023

Cited by 2 | Viewed by 2201

Abstract

Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on [...] Read more.

Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on land surface phenology, primarily related to the start and end of the growing season. In contrast, the monitoring of crop growth within an agronomic framework has been limited, particularly in the context of recently developed solar-induced chlorophyll fluorescence (SIF) data. Additionally, some critical growth stages have not received adequate attention or evaluation. This study aims to assess the utility of SIF data, collected from both ground and satellite measurements, for identifying critical crop growth stages within the realm of remote sensing phenological estimation. A comparative analysis was conducted using enhanced vegetation index (EVI) data at the Shangqiu site in the North China Plain from 2018 to 2022. Both SIF and EVI time-series data, obtained from ground and satellite sources, undergo a comprehensive phenological estimation framework encompassing pre-processing, modeling, and transition characterization. This approach involves reconciling time-series phenological patterns with crop growth stages, revealing the necessity of redefining the mapping relationship between these two fundamental concepts. After preprocessing the time-series data, the framework incorporates the phenological modeling process employing two double logistic models and a spline model for comparison. Additionally, it includes phenological transition characterization using four different methods. Consequently, each input dataset undergoes an assessment, resulting in 12 sets of estimations, which are compared to select the ideal estimation portfolio for identifying the growth stages of maize and winter wheat. Our findings highlight the efficacy of SIF data in accurately identifying the growth stages of maize and winter wheat, achieving remarkable results with an R-square exceeding 0.9 and an RMSE of less than 1 week for key growth stages (KGSs). Notably, SIF data demonstrate superior accuracy, robustness, and sensitivity to phenological events when compared to EVI data. This study establishes an estimation portfolio utilizing SIF data, involving the Gu model, a double logistic model, as the preferred phenological modelling method together with various compositing methods and transition characterization methods, suitable for most KGSs. These findings create opportunities for future research aimed at enhancing and standardizing crop growth stage identification using remote sensing data for a wide range of KGSs. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

24 pages, 12940 KiB

Open AccessArticle

Modelling Two Sugarcane Agro-Industrial Yields Using Sentinel/Landsat Time-Series Data and Their Spatial Validation at Different Scales in Costa Rica

by Bryan Alemán-Montes, Alaitz Zabala, Carlos Henríquez and Pere Serra

Remote Sens. 2023, 15(23), 5476; https://doi.org/10.3390/rs15235476 - 23 Nov 2023

Viewed by 3013

Abstract

Sugarcane production is a relevant socioeconomic activity in Costa Rica that requires tools to improve decision-making, particularly with the advancement of agronomic management using remote sensing (RS) techniques. Some contributions have evaluated sugarcane yield with RS methods, but some gaps remain, such as [...] Read more.

Sugarcane production is a relevant socioeconomic activity in Costa Rica that requires tools to improve decision-making, particularly with the advancement of agronomic management using remote sensing (RS) techniques. Some contributions have evaluated sugarcane yield with RS methods, but some gaps remain, such as the lack of operational models for predicting yields and joint estimation with sugar content. Our study is a contribution to this topic that aims to apply an empirical, operational, and robust method to estimate sugarcane yield (SCY) and sugar content (SC) through the combination of field variables, climatic data, and RS vegetation indices (VIs) extracted from Sentinel-2 and Landsat-8 imagery in a cooperative in Costa Rica for four sugarcane harvest cycles (2017–2018 to 2020–2021). Based on linear regression models, four approaches using different VIs were evaluated to obtain the best models to improve the RMSE results and to validate them (using the harvest cycle of 2021–2022) at two management scales: farm and plot. Our results show that the historical yield average, the maximum historical yield, and the growing cycle start were essential factors in estimating SCY and the former variable for SC. For SCY, the most explicative VI was the Simple Ratio (SR), whereas, for SC, it was the Ratio Vegetation Index (RVI). Adding VIs from different months was essential to obtain the phenological variability of sugarcane, being the most common results September, December and January. In SC estimation, precipitation (in May and December) was a clear explicatory variable combined mainly with RVI, whereas in SCY, it was less explanatory. In SCY, RMSE showed values around 8.0 t·ha⁻¹, a clear improvement from 12.9 t·ha⁻¹, which is the average obtained in previous works, whereas in SC, it displayed values below 4.0 kg·t⁻¹. Finally, in SCY, the best validation result was obtained at the plot scale (RMSE of 7.7 t·ha⁻¹), but this outcome was not verified in the case of SC validation because the RMSE was above 4.0 kg·t⁻¹. In conclusion, our operational models try to represent a step forward in using RS techniques to improve sugarcane management at the farm and plot scales in Costa Rica. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Graphical abstract

21 pages, 19779 KiB

Open AccessArticle

Leaf Spectral Analysis for Detection and Differentiation of Three Major Rice Diseases in the Philippines

by Jean Rochielle F. Mirandilla, Megumi Yamashita, Mitsunori Yoshimura and Enrico C. Paringit

Remote Sens. 2023, 15(12), 3058; https://doi.org/10.3390/rs15123058 - 11 Jun 2023

Cited by 6 | Viewed by 4905

Abstract

Monitoring the plant’s health and early detection of disease are essential to facilitate effective management, decrease disease spread, and minimize yield loss. Spectroscopic techniques in remote sensing offer less laborious methods and high spatiotemporal scale to monitor diseases in crops. Spectral measurements during [...] Read more.

Monitoring the plant’s health and early detection of disease are essential to facilitate effective management, decrease disease spread, and minimize yield loss. Spectroscopic techniques in remote sensing offer less laborious methods and high spatiotemporal scale to monitor diseases in crops. Spectral measurements during the development of disease infection may reveal differences among diseases and determine the stage it can be effectively detected. In this study, spectral analysis was performed over the visible and near-infrared (400–850 nm) portions of the spectrum to detect and differentiate three major rice diseases in the Philippines, namely tungro, BLB, and blast disease. Reflectance of infected rice leaves was recorded repeatedly from inoculation to the late stage of each disease. Results show that spectral reflectance is characteristically affected by each disease, resulting in different spectral, signature sensitivity, and first-order derivatives. Red and red-edge wavelength ranges are the most sensitive to the three diseases. Near-infrared wavelengths decreased as tungro and blast diseases progressed. In addition, the spectral reflectance was resampled to common reflectance sensitivity bands of optical sensors and used in the cluster analysis. It showed that BLB and blast can be detected in the early disease stage on the IRRI Standard Evaluation System (SES) scale of 1 and 3, respectively. Alternatively, tungro was detected in its later stage, with an 11–30% height reduction and no distinct yellow to yellow-orange discoloration (5 SES scale). Three regression techniques, Partial Least Square, Random Forest, and Support Vector Regression were performed separately on each disease to develop models predicting its severity. The validation results of the PLSR and SVR models in tungro and blast show accuracy levels that are promising to be used in estimating the severity of the disease in leaves while RFR shows the best results for BLB. Early disease detection and regression models from spectral measurements and analysis for disease severity estimation can help in disease monitoring and proper disease management implementation. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

Other

Jump to: Research

14 pages, 4460 KiB

Open AccessTechnical Note

Deep-Learning-Based Rice Phenological Stage Recognition

by Jiale Qin, Tianci Hu, Jianghao Yuan, Qingzhi Liu, Wensheng Wang, Jie Liu, Leifeng Guo and Guozhu Song

Remote Sens. 2023, 15(11), 2891; https://doi.org/10.3390/rs15112891 - 1 Jun 2023

Cited by 13 | Viewed by 4343

Abstract

Crop phenology is an important attribute of crops, not only reflecting the growth and development of crops, but also affecting crop yield. By observing the phenological stages, agricultural production losses can be reduced and corresponding systems and plans can be formulated according to [...] Read more.

Crop phenology is an important attribute of crops, not only reflecting the growth and development of crops, but also affecting crop yield. By observing the phenological stages, agricultural production losses can be reduced and corresponding systems and plans can be formulated according to their changes, having guiding significance for agricultural production activities. Traditionally, crop phenological stages are determined mainly by manual analysis of remote sensing data collected by UAVs, which is time-consuming, labor-intensive, and may lead to data loss. To cope with this problem, this paper proposes a deep-learning-based method for rice phenological stage recognition. Firstly, we use a weather station equipped with RGB cameras to collect image data of the whole life cycle of rice and build a dataset. Secondly, we use object detection technology to clean the dataset and divide it into six subsets. Finally, we use ResNet-50 as the backbone network to extract spatial feature information from image data and achieve accurate recognition of six rice phenological stages, including seedling, tillering, booting jointing, heading flowering, grain filling, and maturity. Compared with the existing solutions, our method guarantees long-term, continuous, and accurate phenology monitoring. The experimental results show that our method can achieve an accuracy of around 87.33%, providing a new research direction for crop phenological stage recognition. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sensing for Precision Farming and Crop Phenology

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI