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New Insights in Crop Monitoring and Management Using Remote Sensing Data

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: 30 July 2024 | Viewed by 12684

Special Issue Editors


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Guest Editor
College of Ecology and Environment, Xinjiang University, Urumqi 830049, China
Interests: grassland agro-ecosystem carbon–water–gas exchange; crop growth observation; model simulation

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Guest Editor
College of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, China
Interests: UAV-based remote sensing application for crop monitoring

Special Issue Information

Dear Colleagues,

(1) Remote sensing is promising for the monitoring and evaluation of crop growth, soil water and nutritional conditions and other associated agricultural indicators. This technology, combined with various analytical tools, such as artificial intelligence and process-based crop models, can be utilized to collect within-season and spatial data to derive information on crop growth and eco-physiological conditions. Remote sensing with multiple sensors on diverse platforms can generate big data, posing severe challenges in data processing, analysis and assimilation for the practical application of such data in agricultural production. On the other hand, technological developments in data fusion, machine learning and artificial intelligence provide opportunities to generate big data and derive new information, allowing for the optimization of crop production at unprecedented spatial and temporal scales.

(2) This Special Issue aims to compile the latest research in remote sensing technologies applied for monitoring and retrieving crop and soil biophysical variables and genetic and phenotypic parameters; it also welcomes remote-sensing-based solutions supporting field management decisions for improved resource use efficiency and sustainable production. These research topics look to resolve food security issues in developing and developed nations. We believe this issue will be of particular interest for stakeholders in agricultural policy areas, including climate change adaptation, digital agriculture and modern farming techniques.

(3) We welcome original research contributions, exhaustive reviews, new remote sensing methodologies and relevant applications in diverse agricultural environments using the latest agricultural technologies. Specifically, we invite papers on the following research topics: progress in scientific methodologies for crop monitoring and management using remote sensing data; innovative remote sensing and image analysis tools or methods for enhanced quantification of biophysical and biochemical variables of crops and soils; and application of a holistic system encompassing these approaches.

Prof. Dr. Jonghan Ko
Prof. Dr. Wei Xue
Dr. Xinwei Li
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

  • agricultural production
  • agroecosystems
  • crop management
  • crop monitoring
  • ground, UAV, airborne and satellite platforms
  • image processing and data-fusion technology
  • remote sensing
  • spatial data

Published Papers (9 papers)

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25 pages, 13271 KiB  
Article
Estimation of Biochemical Compounds in Tradescantia Leaves Using VIS-NIR-SWIR Hyperspectral and Chlorophyll a Fluorescence Sensors
by Renan Falcioni, Roney Berti de Oliveira, Marcelo Luiz Chicati, Werner Camargos Antunes, José Alexandre M. Demattê and Marcos Rafael Nanni
Remote Sens. 2024, 16(11), 1910; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16111910 - 26 May 2024
Viewed by 469
Abstract
An integrated approach that utilises hyperspectral and chlorophyll a fluorescence sensors to predict biochemical and biophysical parameters represents a new generation of remote-sensing research. The main objective of this study was to obtain a detailed spectral profile that correlates with plant physiology, thereby [...] Read more.
An integrated approach that utilises hyperspectral and chlorophyll a fluorescence sensors to predict biochemical and biophysical parameters represents a new generation of remote-sensing research. The main objective of this study was to obtain a detailed spectral profile that correlates with plant physiology, thereby enhancing our understanding and management of plant health, pigment profiles, and compound fingerprints. Leveraging datasets using non-imaging or passive hyperspectral and chlorophyll fluorescence sensors to collect data in Tradescantia species demonstrated significant differences in leaf characteristics with pigment concentrations and structural components. The main goal was to use principal component analysis (PCA) and partial least squares regression (PLS) methods to analyse the variations in their spectra. Our findings demonstrate a strong correlation between hyperspectral data and chlorophyll fluorescence, which is further supported by the development of hyperspectral vegetation indices (HVIs) that can accurately evaluate fingerprints and predict many compounds in variegated leaves. The higher the integrated analytical approach and its potential application in HVIs and fingerprints, the better the selection of wavelengths and sensor positions for rapid and accurate analysis of many different compounds in leaves. Nonetheless, limitations arose from the specificity of the data for the Tradescantia species, warranting further research across diverse plant types and compounds in the leaves. Overall, this study paves the way for more sustainable and informed agricultural practices through breakthroughs in the application of sensors to remote-sensing technologies. Full article
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17 pages, 5448 KiB  
Article
Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops
by Sambandh Bhusan Dhal, Stavros Kalafatis, Ulisses Braga-Neto, Krishna Chaitanya Gadepally, Jose Luis Landivar-Scott, Lei Zhao, Kevin Nowka, Juan Landivar, Pankaj Pal and Mahendra Bhandari
Remote Sens. 2024, 16(11), 1906; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16111906 - 25 May 2024
Viewed by 388
Abstract
Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This [...] Read more.
Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production. Full article
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25 pages, 11251 KiB  
Article
Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes
by Wei Xue, Jonghan Ko, Ruyin Cao and Zhiguo Yu
Remote Sens. 2023, 15(19), 4673; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15194673 - 23 Sep 2023
Viewed by 1056
Abstract
Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge. In this study, we put forward a [...] Read more.
Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge. In this study, we put forward a simple, but effective “Light and Temperature-Driven Growth model and Double Logistic function fusion algorithm” (LTDG_DL). The empirical basis of the LTDG_DL algorithm was traced from the de Wit crop growth simulation model and the commonly observed nonlinear correlation between the EVI and the Leaf Area Index (LAI). It assimilates the ground daily solar radiation and air temperature to generate seasonal profiles of the empirical LAI and EVI and conducts the within-season calibration of the empirical EVI by adjusting crop growth using cloud-free Landsat EVI observations. The initial date of seedling emergence (DOYini) and the accumulated Growing Degree Days for completion of the vegetative and Flowering stage (FGDDs) were variables to which the algorithm’s accuracy was most sensitive. The variable-constrained optimization of the LTDG_DL algorithm was performed by loading the seedling emergence calendar of local prevailing crops and establishing an FGDD lookup table with an exhaustive sampling without replication method. Compared to temporal interpolation functions and Landsat–MODIS spatiotemporal fusion algorithms, the LTDG_DL algorithm had superior performance in the predictions of the EVI increment slope at the vegetative growth stage, the timing of the peak EVI, and the protection of key Landsat EVI observations over cloud-contaminated and complex landscape agricultural systems. Finally, the advantages and limitations of the LTDG_DL algorithm are discussed. Full article
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22 pages, 6874 KiB  
Article
Estimating Plant Nitrogen by Developing an Accurate Correlation between VNIR-Only Vegetation Indexes and the Normalized Difference Nitrogen Index
by Yücel Çimtay
Remote Sens. 2023, 15(15), 3898; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15153898 - 7 Aug 2023
Cited by 1 | Viewed by 1895
Abstract
Nitrogen is crucial for plant physiology due to the fact that plants consume a significant amount of nitrogen during the development period. Nitrogen supports the root, leaf, stem, branch, shoot and fruit development of plants. At the same time, it also increases flowering. [...] Read more.
Nitrogen is crucial for plant physiology due to the fact that plants consume a significant amount of nitrogen during the development period. Nitrogen supports the root, leaf, stem, branch, shoot and fruit development of plants. At the same time, it also increases flowering. To monitor the vegetation nitrogen concentration, one of the best indicators developed in the literature is the Normalized Difference Nitrogen Index (NDNI), which is based on the usage of the spectral bands of 1510 and 1680 nm from the Short-Wave Infrared (SWIR) region of the electromagnetic spectrum. However, the majority of remote sensing sensors, like cameras and/or satellites, do not have an SWIR sensor due to high costs. Many vegetation indexes, like NDVI, EVI and MNLI, have also been developed in the VNIR region to monitor the greenness and health of the crops. However, these indexes are not very well correlated to the nitrogen content. Therefore, in this study, a novel method is developed which transforms the estimated VNIR band indexes to NDNI by using a regression method between a group of VNIR indexes and NDNI. Training is employed by using VNIR band indexes as the input and NDNI as the output, both of which are calculated from the same location. After training, an overall correlation of 0.93 was achieved. Therefore, by using only VNIR band sensors, it is possible to estimate the nitrogen content of the plant with high accuracy. Full article
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21 pages, 4768 KiB  
Article
A Three-Dimensional Conceptual Model for Estimating the Above-Ground Biomass of Winter Wheat Using Digital and Multispectral Unmanned Aerial Vehicle Images at Various Growth Stages
by Yongji Zhu, Jikai Liu, Xinyu Tao, Xiangxiang Su, Wenyang Li, Hainie Zha, Wenge Wu and Xinwei Li
Remote Sens. 2023, 15(13), 3332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133332 - 29 Jun 2023
Cited by 6 | Viewed by 1460
Abstract
The timely and accurate estimation of above-ground biomass (AGB) is crucial for indicating crop growth status, assisting management decisions, and predicting grain yield. Unmanned aerial vehicle (UAV) remote sensing technology is a promising approach for monitoring crop biomass. However, the determination of winter [...] Read more.
The timely and accurate estimation of above-ground biomass (AGB) is crucial for indicating crop growth status, assisting management decisions, and predicting grain yield. Unmanned aerial vehicle (UAV) remote sensing technology is a promising approach for monitoring crop biomass. However, the determination of winter wheat AGB based on canopy reflectance is affected by spectral saturation effects. Thus, constructing a generic model for accurately estimating winter wheat AGB using UAV data is significant. In this study, a three-dimensional conceptual model (3DCM) for estimating winter wheat AGB was constructed using plant height (PH) and fractional vegetation cover (FVC). Compared with both the traditional vegetation index model and the traditional multi-feature combination model, the 3DCM yielded the best accuracy for the jointing stage (based on RGB data: coefficient of determination (R2) = 0.82, normalized root mean square error (nRMSE) = 0.2; based on multispectral (MS) data: R2 = 0.84, nRMSE = 0.16), but the accuracy decreased significantly when the spike organ appeared. Therefore, the spike number (SN) was added to create a new three-dimensional conceptual model (n3DCM). Under different growth stages and UAV platforms, the n3DCM (RGB: R2 = 0.73–0.85, nRMSE = 0.17–0.23; MS: R2 = 0.77–0.84, nRMSE = 0.17–0.23) remarkably outperformed the traditional multi-feature combination model (RGB: R2 = 0.67–0.88, nRMSE = 0.15–0.25; MS: R2 = 0.60–0.77, nRMSE = 0.19–0.26) for the estimation accuracy of the AGB. This study suggests that the n3DCM has great potential in resolving spectral errors and monitoring growth parameters, which could be extended to other crops and regions for AGB estimation and field-based high-throughput phenotyping. Full article
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18 pages, 7151 KiB  
Article
Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods
by Yingxiang Feng, Wei Chen, Yiru Ma, Ze Zhang, Pan Gao and Xin Lv
Remote Sens. 2023, 15(10), 2680; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102680 - 22 May 2023
Cited by 2 | Viewed by 1795
Abstract
Cotton is one of the most important cash crops in Xinjiang, and timely seedling inspection and replenishment at the seedling stage are essential for cotton’s late production management and yield formation. The background conditions of the cotton seedling stage are complex and variable, [...] Read more.
Cotton is one of the most important cash crops in Xinjiang, and timely seedling inspection and replenishment at the seedling stage are essential for cotton’s late production management and yield formation. The background conditions of the cotton seedling stage are complex and variable, and deep learning methods are widely used to extract target objects from the complex background. Therefore, this study takes seedling cotton as the research object and uses three deep learning algorithms, YOLOv5, YOLOv7, and CenterNet, for cotton seedling detection and counting using images at six different times of the cotton seedling period based on multispectral images collected by UAVs to develop a model applicable to the whole cotton seedling period. The results showed that when tested with data collected at different times, YOLOv7 performed better overall in detection and counting, and the T4 dataset performed better in each test set. Precision, Recall, and F1-Score values with the best test results were 96.9%, 96.6%, and 96.7%, respectively, and the R2, RMSE, and RRMSE indexes were 0.94, 3.83, and 2.72%, respectively. In conclusion, the UAV multispectral images acquired about 23 days after cotton sowing (T4) with the YOLOv7 algorithm achieved rapid and accurate seedling detection and counting throughout the cotton seedling stage. Full article
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15 pages, 1739 KiB  
Article
Research on Cotton Field Irrigation Amount Calculation Based on Electromagnetic Induction Technology
by Jianwen Han, Mingyue Wang, Nan Wang, Jiawen Wang, Jie Peng and Chunhui Feng
Remote Sens. 2023, 15(8), 1975; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15081975 - 8 Apr 2023
Viewed by 1138
Abstract
The rapid and efficient acquisition of field-scale farmland soil profile moisture-distribution information is very important for achieving precise irrigation and the adjustment and deployment of irrigation strategies in farmland. EM38-MK2 is a portable, non-invasive device that induces electric currents in soil to generate [...] Read more.
The rapid and efficient acquisition of field-scale farmland soil profile moisture-distribution information is very important for achieving precise irrigation and the adjustment and deployment of irrigation strategies in farmland. EM38-MK2 is a portable, non-invasive device that induces electric currents in soil to generate secondary magnetic fields for the rapid measurement of apparent electrical conductivity in the field. In this study, cotton fields were used as experimental objects to obtain soil apparent conductivity data for three periods, which were combined with soil-moisture content data collected simultaneously from soil samples and measured in the laboratory to construct an apparent soil-profile moisture regression model. A simple kriging interpolation method was used to map the distribution of the irrigation volume in the field, considering only the highest irrigation volume in the field as the maximum water-holding capacity in the field. The results showed that EM38 could accurately detect the spatial variation of soil moisture in the field. The R2 of the linear fit between measured and predicted soil-water content ranged from 0.51 to 0.89; the RMSE ranged from 0.66 to 1.87; and the R2 and RPD of each soil-layer water content model of the single-period model were higher than those of the full-period model. By plotting the distribution of field irrigation, it could be seen that by comparing the predicted field irrigation with the actual irrigation, at least 160 m3 ha−1 of irrigation could be saved in all three periods at an irrigation depth of 40 cm, which is about 30% of the actual irrigation; at an irrigation depth of 60 cm, about 30% and 15% of irrigation could be reduced in July and August, respectively. There are three areas in the study area with high fixed-irrigation volumes located in the northwest corner, near 500 m in the northern half of the study area and 750 m east of the southern half of the study area. The results of this study proved that the use of EM38-MK2 to monitor and evaluate the soil-moisture content of the farmland at different periods can, to a certain extent, guide the irrigation amount needed to achieve efficient and precise irrigation in the field. Full article
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23 pages, 8889 KiB  
Article
Estimation of Cotton Nitrogen Content Based on Multi-Angle Hyperspectral Data and Machine Learning Models
by Xiaoting Zhou, Mi Yang, Xiangyu Chen, Lulu Ma, Caixia Yin, Shizhe Qin, Lu Wang, Xin Lv and Ze Zhang
Remote Sens. 2023, 15(4), 955; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15040955 - 9 Feb 2023
Cited by 5 | Viewed by 1639
Abstract
For crop growth monitoring and agricultural management, it is important to use hyperspectral remote sensing techniques to estimate canopy nitrogen content in a timely and accurate manner. The traditional nadir method has limited ability to assess the nitrogen trophic state of cotton shoots, [...] Read more.
For crop growth monitoring and agricultural management, it is important to use hyperspectral remote sensing techniques to estimate canopy nitrogen content in a timely and accurate manner. The traditional nadir method has limited ability to assess the nitrogen trophic state of cotton shoots, which is not conducive to high-precision nitrogen inversion, whereas the multi-angle remote sensing monitoring method can effectively extract the canopy’s physicochemical information. However, multi-angle spectral information is affected by a variety of factors, which frequently causes shifts in the band associated with nitrogen uptake, and lowers the estimation accuracy. The capacity of the spectral index to estimate aerial nitrogen concentration (ANC) in cotton was therefore investigated in this work under various observation zenith angles (VZAs), and the Relief−F method was employed to select the best spectral band with weight for ANC that is insensitive to VZA. Therefore, in this study, the ability of the spectral index to estimate ANC in cotton was explored under different VZAs, and the Relief-F algorithm was used to optimize the optimal spectral band with weight for ANC that is insensitive to VZA. The angle insensitive nitrogen index (AINI) for various VZAs was calculated using the expression (R530 − R704)/(R1412 + R704). The results show that the correlation between the spectral index and the ANC chosen in this study is stronger than the correlation between off-nadir observations, and the correlation coefficients between Photochemical Reflectance Index (PRI), AINI, and ANC are highest when VZA is −20° and −50° (r = 0.866 and 0.893, respectively). Compared with the traditional vegetation index, AINI had the best correlation with ANC under different VZAs (r > 0.84), and the performance of ANC in the backscatter direction was estimated to be better than that in the forward-scatter direction. At the same time, the ANC estimation model of the optimal indices AINI and PRI was combined with the machine learning method to achieve better accuracy, and the prediction accuracy of the random forest (RF) model was R2 = 0.98 and RMSE = 0.590. This study shows that the AINI index can estimate cotton ANC under different VZAs. Simultaneously, the backscattered direction is revealed to be more conducive to cotton ANC estimation. The findings encourage the use of multi-angle observations in crop nutrient estimation, which will also help to improve the use of ground-based and satellite sensors. Full article
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15 pages, 3943 KiB  
Technical Note
Automated Workflow for High-Resolution 4D Vegetation Monitoring Using Stereo Vision
by Martin Kobe, Melanie Elias, Ines Merbach, Martin Schädler, Jan Bumberger, Marion Pause and Hannes Mollenhauer
Remote Sens. 2024, 16(3), 541; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16030541 - 31 Jan 2024
Viewed by 1472
Abstract
Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, [...] Read more.
Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops
Authors: Sambandh Bhusan Dhal; Stavros Kalafatis; Krishna Chaitanya Gadepally; Jose Luis Landivar-Scott; Lei Zhao; Kevin Nowka; Ulisses Braga-Neto; Juan Landivar; Pankaj Pal; Mahendra Bhandari
Affiliation: Texas A&M AgriLife Research and Extension Center Corpus Christi Texas
Abstract: Cotton (Gossypium spp.) is a significant cash crop in the United States, requiring continuous monitoring of growth parameters for informed decision-making from planting to harvest. Recently, forecasting models have gained attention for predicting canopy indicators, aiding in-season planning and management decisions to enhance cotton production and profitability. This study utilized Unmanned Aerial System (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. The 2020 data were used to train Long Short-Term Memory (LSTM) models, which were then employed to predict CC values for 2021. These LSTM models were compared with real-time ARIMA models, evaluating their efficacy in predicting CC values up to 14 days in advance starting from the 28th day after crop emergence. Results indicated that multiple-input multi-step output LSTM models showed higher accuracy in predicting in-season CC values during early growth stages (up to the 56th day). For later stages, stacked-LSTM models exhibited precision in CC prediction. This underscores the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.

Title: Estimation of Biochemical Compounds in Tradescantia Leaves Using VIS-NIR-SWIR Hyperspectral and Chlorophyll a Fluorescence Sensors
Author: Falcioni
Highlights: Utilized hyperspectral sensors and chlorophyll fluorescence to predict biochemical and biophysical plant parameters.

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