Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field

Improving Nitrogen Use Efficiency and Yield in Louisiana Sugarcane Production Systems

Proper nitrogen (N) management is essential to optimize crop production. This study was conducted to evaluate different N fertilizer management strategies to improve N use efficiency and yield in sugarcane production in Louisiana. This research was initiated in 2013 at the Sugar Research Station in St. Gabriel, LA and was arranged in a randomized complete block design with four replications consisting of different N rates (0, 45, 90, and 135 kg N ha-1) and sources (urea-46% N, ammonium nitrate [AN]-34% N, and urea-ammonium-nitrate solution [UAN]-32% N dribbled and knifed-in) as treatments. Sensor readings were taken from different N response trials to validate the sugarcane yield potential prediction and N response index (RI) models based on normalized difference vegetation index (NDVI). Soil nitrate (NO3-) and ammonium (NH4+) at 0-15 and 15-30 cm depths were also measured at different dates after N fertilization. At the grand growth stage, plots which were knifed-in with UAN showed a more even distribution of NO3- and NH4+ compared to urea- and AN-treated plots for both depths. Among the treatments, the highest sugarcane yield was achieved from plots treated with 90 kg N ha-1 as UAN knife-in and 135 kg N ha-1 as AN. Yield potential prediction models established in 2012 and 2015 could be used to estimate sugar and cane yield using NDVI readings collected at 21 (r2=0.30 and r2=0.51) and 60 (r2=0.41 and r2=0.52) days after N fertilization (DANF), respectively. Both RI and modified RI models demonstrated a better level of precision when RI was predicted at 60 DANF (r2=0.30) for both cane and sugar yield compared to 21 DANF (r2=0.15). The outcomes of this study demonstrated the effectivity of UAN knife-in as N source and the current N recommendation, but there were indications that application of higher N rate may further maximize yield. This study also revealed some limitations of the models used for predicting the components of remote sensor-based N recommendations for Louisiana sugarcane production. Apart from strengthening the yield and sensor readings database, areas of focus for future research include the use of different vegetation indices and reflectance readings from different wavebands

Soil permittivity estimation over croplands using full and compact polarimetric SAR data

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θ FP and θ CP , respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θ FP and θ CP , unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient, r ≥ 0.62.Peer ReviewedPostprint (author's final draft

Literature Search for Extraction and Characterization of Fragrant Compounds from Ten Flowering Plants

This capstone literature project conducted a literature search on the extraction of essential oils from the leaves of ten different flowering plants using steam distillation and/or solvent extraction methods. Additionally, the project focused on characterizing and identifying the fragrant compounds present in the essential oils of these ten plants. By conducting a comprehensive literature search, this project provides a summary of the methods employed for extraction and the composition of fragrant compounds in each essential oil. A diverse range of fragrant compounds were found in various plants and flowers, including major and minor compounds such as alcohols, aldehydes, ketones, terpenes, and esters. The plants of interest in this capstone project focused on included Oriental Lily, Tuberose, Neroli, Ylang-Ylang, Gardenia, Catalpa tree, Locust flower, Eucalyptus, Viburnum, and Wisteria Vine

Optical Sensors

This book is a compilation of works presenting recent developments and practical applications in optical sensor technology. It contains 10 chapters that encompass contributions from various individuals and research groups working in the area of optical sensing. It provides the reader with a broad overview and sampling of the innovative research on optical sensors in the world

Proceedings of the COST SUSVAR/ECO-PB Workshop on organic plant breeding strategies and the use of molecular markers

In many countries,national projects are in progress to investigate the sustainable low-input approach.In the present COST network,these projects are coordinated by means of exchange of materials,establishing common methods for assessment and statistical analyses and by combining national experimental results.The common framework is cereal production in low-input sustainable systems with emphasis on crop diversity.The network is organised into six Working Groups,five focusing on specific research areas and one focusing on the practical application of the research results for variety testing:1)plant genetics and plant breeding,2)biostatistics,3)plant nutrition and soil microbiology,4)weed biology and plant competition,5)plant pathology and plant disease resistance biology and 6)variety testing and certification.It is essential that scientists from many disciplines work together to investigate the complex interactions between the crop and its environment,in order to be able to exploit the natural regulatory mechanisms of different agricultural systems for stabilising and increasing yield and quality.The results of this cooperation will contribute to commercial plant breeding as well as official variety testing,when participants from these areas disperse the knowledge achieved through the EU COST Action

COVER CROPPING: SENSOR-BASED ESTIMATIONS OF BIOMASS YIELD AND NUTRIENT UPTAKE AND ITS IMPACT ON SUGARCANE PRODUCTIVITY

Sugarcane in Louisiana can be harvested for up to three years from one planting. Soil cultivation along sides of established beds is done for weed control and improve fertilizer use efficiency which increases the risk of soil degradation and yield decline. Planting cover crops (CC) is a soil conservation practice and an effective strategy to improve soil health and nutrient recycling. Limited work has been done on remote sensor-based evaluation of the potential nutrient benefits from cover crops and its effect on nutrient cycling on sugarcane systems. This study was conducted to evaluate the effect of two planting methods (broadcast and drilling) and three seeding rates (100%, 50%, and 25% of NRCS recommendation) of a mix of three legumes and two brassicas CC species and a control without CC, on sugarcane yield and quality parameters, and on soil nutrients levels. This study was also used for the acquisition of normalized difference vegetation index (NDVI), collected using GreenSeeker® and multispectral camera (MicaSense® - RedEdge-M) mounted on an unmanned aerial vehicle, to correlate with CC biomass and nutrient uptake. The NDVI readings and CC biomass clippings, using the quadrat frame method, were collected a week before CC termination. Tissue analysis was carried out by C:N dry combustion analyzer and nitric acid digestion-hydrogen peroxide for multi-element analysis. Cane yield was acquired with a chopper harvester and a dump billet wagon. Quality components were obtained by a SpectraCane® automated near infrared (NIR) analyzer for quality parameters. Soil inorganic nitrogen (N) content (NH4+ + NO3-) was quantified using KCl extraction procedure and flow injection analysis. Other soil nutrients content was determined based on Mehlich-3 extraction procedure followed by ICP. A strong positive correlation between the GreenSeeker NDVI (NDVI-GS) and aerial images derived NDVI (NDVI-AI) was obtained with a coefficient of determination (R2) value of 0.63. Adjustment of NDVI with, number of days, cumulative growing degree days, and number of days with positive growing degree days, from planting to sensing increased the R2 values up to 0.76, 0.76 and 0.73, respectively. The NDVI-GS obtain a stronger linear relationship with CC dry biomass and N content than NDVI-AI. Good positive correlations (0.48 \u3e R2 \u3e 0.12) were found between NDVI and some macronutrients (P and K) and micronutrients (Mn and Cu). Overall, there was no significant effect of planting method and seeding rate observed on cane yield and quality parameters. Moreover, there was no statistical difference on CC nutrient removal rate among the treatments (p\u3e0.05). For plant cane, the average cane and sugar yield across sites was 96 Mg ha-1 and 10794 kg ha-1, respectively. Lower yield was attained by the ratoon crops averaging only at 71 Mg ha-1 cane yield and 7197 kg ha-1 sugar yield. Remote sensing is a promising and viable technique to estimate CC biomass and nutrient uptake. Finally, this study corroborates the long-term effect of CC on nutrient management and their effect on cane yield and quality parameters

Design of a hybrid organosolv-ionosolv lignin fractionation method and lignin-like polymer synthesis for value added applications

Biofuel technology has been introduced to reduce the reliance on fossil fuels. It is also superior for addressing environmental issues, such as greenhouse gas (GHG) emissions. Biofuel production from lignocellulosic biomass has been attracting attention due to the high abundance of the feedstock and the incredible GHG emission reduction associated with the production process. Ionic liquid pretreament, the ionoSolv process, and organosolv pretreatment are well-known for their selective fractionation performance. The ionoSolv process is able to generate a highly digestible cellulose fraction and the organosolv process is famous for producing high quality lignin as the side product, where the lignin generated is suitable for value-added applications. Here, a hybrid pretreament process has been developed based on these two processes, where two protic ionic liquids and three organic solvents were the selected solvents. The new process has been tested on three classes of feedstocks, miscanthus, pine and agricultural residues. The pretreatment effectiveness was determined by enzymatic saccharification and compositional analysis. The isolated lignin fraction was subjected to HSQC and GPC analysis. For miscanthus, ethanol/butanol-IL process was able to produce a highly digestible pulp with a glucose yield of up to 85%, 10% higher than the standard ionoSolv pulp, due to more profound lignin removal for the hybrid process. The process maintained its functionality with a range of IL acidities, 1.00 to 1.02 (acid/base) and up to 50% wt biomass loading. Similar glucose yield increases were observed for pine, rice husk and bagasse. For the process of two straws, additional hemicellulose releases were detected in enzymatic hydrolysis while the level of glucose yields remained the same as for the ionoSolv ones. HSQC NMR of the lignin indicated that α-alkoxylation took place during ethanol/butanol-IL fractionation, inhibiting lignin condensation. Three major monolignols were synthesised and radical polymerisation induced by horseradish peroxidases was conducted for the monolignols synthesised. The lignin-like polymer was analysed by GPC.Open Acces

Novel clustering schemes for full and compact polarimetric SAR data: An application for rice phenology characterization

Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier’s parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological stages.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI), and the European Funds for Regional Development (EFRD) under Project TEC 2017-85244-C 2-1-P. The work of Dipankar Mandal was supported by the Ministry of Human Resource Development, Government of India (New Delhi, India) towards his Ph.D. assistantship through grant no. RSPHD0210