Red Indices Research Articles

Estimation of the Forest Stand Biomass and Greenhouse Gas Emissions Using Lidar Surveys

At the research points, the relationship between the Normalized Difference Vegetation and Normalized Green Red Difference indices is characterized by a determination coefficient equal to 0.52. The estimation of the emission of carbon dioxide and nitrogen oxide in the forest air at an altitude of 40 m above the level of the soil cover during the growing season showed differences in their values during the daytime and at night. The results helped determine promising methods of inventory of the carbon landfill forest area for aboveground woody biomass assessment based on data obtained from several sources and land forest estimation research. The research involved: 1) integration of an unmanned aerial vehicle -based digital camera and lidar sensors in order to optimize the efficiency and cost of data collection; 2) taking advantage of high-resolution aerial photographs and sparse lidar point clouds using an information fusion approach and the ability to compensate for their shortcomings.

PEMANFAATAN NORMALIZED GREEN RED DIFFERENCE INDEX (NGRDI) UNTUK MENDUGA KADAR N-TOTAL PADA LAHAN PADI DI KABUPATEN PAMEKASAN

Inefficient fertilization is the cause of decreased rice production. This is due to the inappropriate use of fertilizers. The absence of information on soil nitrogen content in their area causes farmers not to apply fertilizer properly. Analysis of soil nitrogen content in Pamekasan Regency requires much time, cost, and effort. Remote sensing methods can be used to obtain information on soil nitrogen levels by reducing the number of samples used. This study aims to estimate nitrogen content in rice fields using the vegetation index in the form of NGRDI (Normalized Green Red Difference Index). This research was conducted by purposive random sampling method based on 40 land map units formed in Pamekasan Regency, laboratory analysis, statistical analysis, and making distribution maps. The results showed that there was a significant relationship between the NGRDI pixel value and the nitrogen content, with a correlation value of -0.42. The regression equation results obtained from the NGRDI value with nitrogen content are y = -0.269x + 0.139. The accuracy test was carried out by the RMSE method, and the result was 0.027. This shows that the NGRDI index can estimate soil nitrogen content in Pamekasan Regency

Remote Sensing for Sustainable Pistachio Cultivation and Improved Quality Traits Evaluation through Thermal and Non-Thermal UAV Vegetation Indices

Pistachio (Pistacia vera L.) has earned recognition as a significant crop due to its unique nutrient composition and its adaptability to the growing threat of climate change. Consequently, the utilization of remote sensing techniques for non-invasive pistachio monitoring has become critically important. This research was conducted in two pistachio orchards located in Spain, aiming to assess the effectiveness of vegetation indices (VIs) in estimating nut yield and quality under various irrigation conditions. To this end, high-resolution multispectral and thermal imagery were gathered using a Micasense ALTUM sensor carried by a DJI Inspire 2 drone in order to calculate the NDRE (normalized difference red edge index), GNDVI (green normalized difference vegetation index), NDVI (normalized difference vegetation index), and CWSI (crop water stress index). Each orchard underwent two flights at distinct growth stages, totaling four flights. In June, NDRE-carbohydrates (r = 0.78) and CWSI-oleic (r = 0.77) showed the highest correlations, while in September, CWSI-carbohydrates (r = 0.62) and NDVI-iron (r = 0.54) Despite NDVI’s limitations due to saturation effects, all VIs had significant yield and quality correlations, with GNDVI proving most effective in both flights. CWSI correlated considerably on both dates in terms of several quality parameters (carbohydrate percentage, magnesium, iron, and fatty acids, namely palmitoyl, stearic, oleic, and linoleic), surpassing non-thermal indices. Finally, it is important to consider the impact of environmental factors, such as the location of the sun, when interpreting the CWSI, as it modifies the temperature distribution pattern within the canopy. This study supports the viability of remote sensing and vegetation indices as potential tools for enhancing the management of pistachio orchards.

Tracking autumn photosynthetic phenology on Tibetan plateau grassland with the green–red vegetation index

Accurate monitoring autumn photosynthetic phenology is essential for understanding carbon cycles. The broadband green-red vegetation index (GRVI) derived from broadband red and green reflectance has been increasingly used in this field. However, the performance of GRVI in large areas remains unclear. We evaluated the performance of the normalized vegetation index (NDVI), normalized difference greenness index (NDGI), the near-infrared reflectance of vegetation (NIRv), solar-induced chlorophyll fluorescence (SIF) and GRVI in tracking autumn photosynthetic phenology of alpine grassland at flux sites and the entire Tibetan Plateau (TP). The results revealed that GRVI (R2 = 0.42, RMSE = 7.68 d and Bias = 4.39 d) performed comparable with SIF (R2 = 0.45, RMSE = 5.79 d and Bias = 0.71 d) in extracting the end of the photosynthetically active season (EOS) with eddy covariance flux measurements as reference. On contrary, a systematically later EOS was estimated by NDVI (Bias = 20.35 d), NDGI (Bias = 13.62 d) and NIRv (Bias = 6.56 d). The application example on the TP collaborated these findings. The divergent performances between indicators were rooted in the photosynthesis downregulation in autumn which is jointly controlled by canopy structure and physiology. Due to the fact that NDVI, NDGI and NIRv primarily depict vegetation structure, while the use of SIF, which represents vegetation photosynthetic physiology, is influenced by the limited spatial resolution and temporal coverage. Our study highlights the unique advantage of GRVI over other existing satellite indicators for estimating autumn photosynthetic phenology with high resolution and long-time span. We suggest revisiting the dynamics of autumn photosynthetic phenology using GRVI, which has significant implications on carbon uptake studies.

Establishment of a Bayesian network model to predict the survival of malignant peritoneal mesothelioma patients after cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy

Objectives To establish a Bayesian network (BN) model to predict the survival of patients with malignant peritoneal mesothelioma (MPM) treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC). Methods The clinicopathological data of 154 MPM patients treated with CRS + HIPEC at our hospital from April 2015 to November 2022 were retrospectively analyzed. They were randomly divided into two groups in a 7:3 ratio. Survival analysis was conducted on the training set and a BN model was established. The accuracy of the model was validated using a confusion matrix of the testing set. The receiver operating characteristic (ROC) curve and area under the curve were used to evaluate the overall performance of the BN model. Results Survival analysis of 107 patients (69.5%) in the training set found ten factors affecting patient prognosis: age, Karnofsky performance score, surgical history, ascites volume, peritoneal cancer index, organ resections, red blood cell transfusion, pathological types, lymphatic metastasis, and Ki-67 index (all p < 0.05). The BN model was successfully established after the above factors were included, and the BN model structure was adjusted according to previous research and clinical experience. The results of confusion matrix obtained by internal validation of 47 cases in the testing set showed that the accuracy of BN model was 72.7%, and the area under ROC was 0.74. Conclusions The BN model was established successfully with good overall performance and can be used as a clinical decision reference.

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.

Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations

Vegetation indices (VIs) have been used extensively for qualitative and quantitative remote sensing monitoring of vegetation vigor and growth dynamics. However, the saturation phenomenon of VIs (i.e., insignificant change at moderate to high vegetation densities) poses a known limitation to their ability to characterize surface vegetation over the dense canopy. Although the mechanisms underlying saturation are relatively straightforward and several VIs have been proposed to mitigate the saturation effect, the assessment of the saturation effect of VIs remains insufficient. Notably, no unified metric has been proposed to quantify the VI saturation phenomenon, limiting VI selection in practical applications. In this study, we proposed two indicators to describe the saturation phenomenon and utilized a well-validated three-dimensional (3D) canopy radiative transfer (RT) model large-scale remote sensing data and image simulation framework (LESS) to simulate the bidirectional reflectance factor (BRF) of six forests scenes and assessed the variations in VIs in relation to leaf area index (LAI) values over different backgrounds, sun-sensor geometries, and spatial distribution types. The saturation characteristics of 36 VIs were evaluated in combination with simulation results and satellite observations from multiple sensors. The ranking of VI saturation from simulated and satellite results revealed a good agreement. Our results indicated that the simple ratio vegetation index (SR) performed best with the highest saturation point and can well characterize the surface vegetation condition until LAI reaches 4. Besides, we found that the saturation effect of VIs was influenced by soil brightness, sun-sensor geometry, and canopy structure. SR, modified simple ratio (MSR) and normalized green red difference index (NGRDI) were the most susceptible to these disturbing factors, although they had higher resistance to saturation. Modified triangular vegetation index 1 (MTVI1), modified non-linear vegetation index (MNLI), triangular greenness index (TGI), and triangular vegetation index (TriVI) performed well overall, combining the ability to resist saturation and disturbance factors. Appropriate application of VIs can help better understand vegetation responses to climate change and accurately assess ecosystem status. Our results contribute to the understanding of the VI saturation effect and provide a combined model and satellite data experimental workflow in appropriate VI selection to accurately characterize vegetation.

High-precision sugarcane yield prediction by integrating 10-m Sentinel-1 VOD and Sentinel-2 GRVI indexes

Developing accurate models of sugarcane yield mapping at fine-scale is of paramount importance and will benefit many aspects of managing growth and harvest of sugarcane crops. Here, we combined high-spatial-resolution multi-sensor (optical and microwave) remote sensing data to estimate sugarcane yield using two approaches. First, we retrieved the 10 m resolution vegetation optical depth (VOD) using C-band Sentinel-1 synthetic aperture radar data via the water cloud model, then derived a combined VOD and Sentinel-2 derived green–red vegetation index (GRVI, a traditional vegetation index) time series. Second, we adopted the eXtreme Gradient Boost (XGBoost) machine learning algorithm to estimate total sugarcane production based on the aggregated mean monthly VOD and GRVI time series at the county scale. County-scale sugarcane yield data from 81 counties across Guangxi, China (2018–2020) were used for model training and testing. We then kept the trained best-performance model unchanged to re-predict pixel-scale (10-m resolution) yield using VOD and GRVI images. A SHapley Additive exPlanations (SHAP) approach was employed to test XGBoost knowledge regarding the mechanisms affecting yield. The main outcomes from this study were as follows. (1) The XGBoost model and VOD and GRVI time series unequivocally provided reliable and precise estimates of sugarcane yield, with a low relative error both at the county scale and at the pixel scale. (2) Predictions of sugarcane yield in the growing season using combined VOD and GRVI data yields high accuracy (R2 = 0.815) with specific spatial patterns of sugarcane yields for Guangxi province, and were feasible up to two months prior to harvest. (3) The SHAP model revealed that Sentinel-1 derived VOD data are important for predicting yield. This research portrays the benefits of combining VOD and GRVI data in automatic machine learning models for estimating sugarcane yield at high spatial and temporal resolutions.

A comparative analysis of growth kinetics, image analysis, and biofuel potential of different algal strains

Due to the global population growth and economic development, energy demand has increased worldwide. Countries take steps to improve their alternative and renewable energy sources. Algae is one of the alternative energy sources and can be used to produce renewable biofuel. In this study, nondestructive, practical, and rapid image processing techniques were applied to determine the algal growth kinetics and biomass potential of four algal strains, including C. minutum, Chlorella sorokiniana, C. vulgaris, and S. obliquus. Laboratory experiments were conducted to determine different aspects of biomass and chlorophyll production of those algal strains. Suitable non-linear growth models, including Logistic, modified Logistic, Gompertz, and modified Gompertz models, were employed to determine the growth pattern of algae. Moreover, the methane potential of harvested biomass was calculated. The algal strains were incubated for 18 days, and the growth kinetics were determined. After the incubation, the biomass was harvested and assessed for its chemical oxygen demand content and biomethane potential. Among the tested strains, C. sorokiniana was the best in biomass productivity (111.97 ± 0.9 mg L−1d−1). The calculated vegetation indices, namely; colorimetric difference, color index vegetation, vegetative, excess green, excess green minus excess red, combination, and brown index values showed a significant correlation with biomass and chlorophyll content. Among the tested growth models, the modified Gompertz shows the best growth pattern. Further, the estimated theoretical CH4 yield was highest for C. minutum (0.98 mL g−1) compared to other tested strains. The present findings suggest that image analysis can be used as an alternative method to study the growth kinetics and biomass production potential of different algae during cultivation in wastewater.

Incidence of harmful algal blooms in pristine subtropical ocean: a satellite remote sensing approach (Jeju Island)

Despite the increasing numbers of red tide events in the pristine subtropical ocean, a paucity of previous observations has limited understanding of harmful algae in the seas around the Korean Peninsula. Therefore, using six years (2012–2017) of Geostationary Ocean Color Imager (GOCI) satellite data, we characterized the red tides around Jeju Island, a volcanic island located near the paths of the Jeju Warm Current and Tsushima Warm Current, using the Normalized Red Tide Index (NRTI) method. The seawater around Jeju Island has for a long time been considered to be very clear, with relatively low suspended particulate matter concentrations and few harmful algae. Nonetheless, the satellite-based NRTI detection method used in this study detected and supported the existence of red tides in the coastal region around Jeju Island. Analysis of the red tide distribution showed that red tide first began to appear near the western coast of Jeju Island, then developed in the northern and eastern coastal regions, and finally vanished in the eastern coastal region. The monthly averages of the NRTI demonstrated a bloom event from April to May in every year. Additional fall blooms were detected in August–September, particularly in 2013 and 2016. The NRTI revealed strong interannual variations. The longest blooms occurred in 2015, and the most comprehensive and strongest event occurred in the spring of 2016. The latter three years (2015–2017) had much higher NRTI than the former three years (2012–2014). The probability of red tide occurrence at a given point during the 6-year study period revealed spatial differences. Relatively high probability of 0.3–0.5 was determined along the northern coastal region, whereas low probability of less than 0.2 was found along the southern region. Ground truth data also showed more frequent observations and higher red tide cell densities along the northern coast. Changes in NRTI in spring are positively correlated with changes in ENSO indices in winter. This study is the first to use a satellite-based approach with a vast long-term satellite database to elucidate the existence and probability of red tides near Jeju Island. We anticipate that this study will provide a useful strategy for remote monitoring of harmful algal blooms over wide regions using optical data.

Shelf-life of sheep &lt;i&gt;arrosticini&lt;/i&gt; packaged in protective atmosphere

The aim of this study was to evaluate the shelf-life of pre-packaged sheep arrosticini produced in a factory located in northern Italy. Samples were divided into two series and packaged in modified atmosphere with specific gas mixtures: conventional (C: 35% O2/15% CO2/50% N2) and experimental (E: 30% CO2/70% N2). All the samples were stored at 4°C for 10 days and subjected, after 5, 8 and 10 days (t5, t8, t10), to triplicate microbiological and chemical-physical (pH, total volatile basic nitrogen, thiobarbituric acid reactive substances) analyses. At the same time, the colorimetric analysis and a sensorial evaluation were carried out (pack tightness, presence of color or odor alterations), assigning a discrete score from 0 to 5. The samples showed initial total bacterial counts close to 5 Log CFU/g: these values gradually increased during storage, exceeding the threshold of 8 Log CFU/g in the C series; lower values were detected in the E series. A similar trend was highlighted for Enterobacteriaceae, with initial loads of about 3 Log CFU/g and an increase until t10, reaching values higher than 6 Log CFU/g in the C series and close to 5 Log CFU/g in the E series (P=0.002). E. coli also showed a similar trend, although with values approximately 1 Log lower than Enterobacteriaceae. Pseudomonas spp. showed initial counts close to 4.5 Log CFU/g, with a different increase in the C series (6.5 Log CFU/g at t10) and the E series (4.95 Log CFU/g) (P=0.006). The higher growth in the C series was also observed for lactic acid bacteria, with an increase from 3 to 5 Log CFU/g (3.8 Log CFU/g in the E series P=0.016). The other microbiological parameters showed very low counts and, in most cases undetectable counts (&lt;2 Log CFU/g) for the entire period considered. The initial values obtained from the measurement of the colorimetric indices were within the norm for this type of product: however, starting from t5, lower values of red index and lightness in the E series were measured, with an evident greying of meat surface. The results of the sensory evaluation indicated that the product showed optimal sensorial characteristics up to 8 days of shelf-life in the C series, while the use of an oxygen-free atmosphere, despite having a moderate inhibiting effect on the microbial populations, has led to an early modification of the product (5 days of storage), due to the presence of superficial greyish areas. The microbiological characteristics of arrosticini strictly depend on the hygienic conditions of slaughtering and production; even in optimal situations, the product is particularly perishable, and requires careful management of storage temperatures and times, to maintain its quality characteristics.

Associations between polymorphisms in leptin and leptin receptor genes and colorectal cancer survival.

Leptin (LEP) is an obesity-associated adipokine associated with tumor cell growth. We examined the relevance of genetic variants of LEP and leptin receptor (LEPR) to colorectal cancer (CRC) survival by using data from the Newfoundland Familial Colorectal Cancer Study. A total of 532 patients newly diagnosed with CRC between 1997 and 2003 were followed up until April 2010. Data on their demographics and lifestyles were collected via questionnaires. Genotyping of blood samples was performed with the Illumina Human Omni-Quad Bead chip. Multivariable Cox models were used to assess the relationships of 35 tag single-nucleotide polymorphisms (SNPs) in LEP and LEPR with overall survival (OS), disease-free survival (DFS), and CRC-specific survival. At the gene level, LEP was associated with DFS (P = 0.017), and LEPR was associated with both DFS (P = 0.021) and CRC-specific survival (P = 0.013) in patients with CRC. In single-SNP analysis, LEP rs11763517, LEPR rs9436301, and LEPR rs7602 were associated with DFS after adjustment for multiple testing. The LEPR haplotypes G-C-T (rs7534511-rs9436301-rs1887285) and A-A-G (rs7602-rs970467-rs9436748) were associated with prolonged OS among patients with CRC overall (G-C-T: HR, 0.63; 95% CI, 0.43-0.93; A-A-G: HR, 0.59; 95% CI, 0.38-0.91) and those diagnosed with colon cancer (G-C-T: HR, 0.54; 95% CI, 0.34-0.86; A-A-G: HR, 0.49; 95% CI, 0.29-0.83). Similar results were observed for DFS. Moreover, significant interactions were found among LEPR rs7602 (A vs. G), LEPR rs1171278 (T vs. C), red meat intake, and BMI status: the associations between these variants and prolonged DFS were limited to patients with below-median red meat consumption and body mass index (BMI) < 25 kg/m2. Polymorphic variations in the LEP and LEPR genes were associated with survival of patients after CRC diagnosis. The LEP/LEPR-CRC survival association was modified by participants' red meat intake and BMI.

Influence of high-pressure homogenization treatments combined with lysozyme activated packaging on microbiological and technological quality of vegetable smoothie during shelf life

In this study the effect of a high pressure homogenization (HPH) treatment, alone and in combination with a biodegradable polylactic acid (PLA) packaging activated with lysozyme on a smoothie made with apple, carrot and rice beverage was evaluated. In particular, the effects of treatments on the microbiological and quality characteristics of smoothies were assessed during storage at 4 and 10 °C in climatic chambers at RH of 50 %. Obtained results showed the efficacy of HPH at 100 MPa to reduce the initial cell load of mesophilic and psycothrophic aerobic bacteria, lactic acid bacteria, yeasts and aerobic spore-forming bacteria. Moreover, the combination of HPH and lysozyme activated packaging resulted in an increased antimicrobial effect against mesophilic, psychrotrophic and lactic acid bacteria during product storage at 10 °C. Yeast and spore forming bacteria showed, instead, lower cell loads independently from samples. Samples packed in active packaging, both treated and not, showed a better color retention in terms of lightness and red index, compared to control (C) ones both when stored at 4 and 10 °C. The HPH treated smoothies, both activated and not, at 4 °C remained microbiologically stable for more than 20 days. However, the use of active packaging has also allowed to reduce the microbiological proliferation during storage at 4 °C. Moreover, few differences in color indices were highlighted between samples stored at 4 °C. Obtained results indicate that the combination of HPH treatment and active packaging may represent a useful strategy to increase safety and shelf-life of vegetable smoothies.

Design of Vegetation Index for Identifying the Mosaic Virus in Sugarcane Plantation: A Brazilian Case Study

Phytosanitary control of crops requires the rapid mapping of diseases to enable management attention. This study aimed to evaluate the potential of vegetation indices for the detection of sugarcane mosaic disease. Spectral indices were applied to hyperspectral images collected by an unmanned aerial vehicle (UAV) to find the areas affected by the mosaic virus in sugarcane. Identifying indices capable of detecting diseased plants in agricultural crops supports data processing and the development of efficient tools. A new index was designed based on spectral regions, which presents higher differences between healthy and mosaic virus-infected leaves to enhance hyperspectral image pixels representing diseased plants. Based on the data generated, we propose the anthocyanin red edge index (AREI) for mosaic virus detection in sugarcane plantations. An index that can adequately identify sugarcane infected by the mosaic virus may incorporate wavelengths associated with variations in leaf pigment concentrations as well as changes in leaf structure. The indices that assessed to detect plants infected with the sugarcane mosaic virus were the normalised difference vegetation index (NDVI), normalised difference vegetation index red edge (NDVI705), new vegetation index (NVI), ARI2 and AREI. The results showed that AREI presented the best performance for the detection of mosaic in sugarcane from UAV images, giving an overall accuracy of 0.94, a kappa coefficient of 0.87, and omission and inclusion errors of 2.86% and 10.52%, respectively. The results show the importance of wavelengths associated with the concentration of chlorophyll and anthocyanin and the position of the red edge for the detection of diseases in sugarcane.

Most Reliable Haematological Indices for Diagnosis of Iron Deficiency Anaemia from Non-Iron Deficiency Anaemia in Reproductive-Age Females

Iron deficiency anaemia has become a major public health issue, especially in women of reproductive age. About 50% of anaemia is caused by iron deficiency anaemia. Numerous statistical procedures based on red blood cell parameters have been developed to easily, quickly, and inexpensively provide a differential diagnosis for the microcytic anaemia. The study aims to evaluate the reliability of the haematological discrimination indices (RBC count, Mentzer index, Red blood cell distribution width index (RDWI), Srivastava index, Green and King index, and Ehsani index) to distinguish iron deficiency anaemia from other causes of the microcytic anaemia. The blood samples were collected from non-pregnant women, and they had the microcytic anaemia. During the study, 101 blood samples were taken. A Complete blood count test and serum ferritin test were performed to calculation of the sensitivity, specificity, positive, and negative predictive value for each discrimination indices. The validity of discrimination indices was evaluated by using Youden's index. In correctly distinguishing iron deficiency anaemia from other causes of the microcytic anaemia, the Green and King index and RBC count showed the highest ability (77%) and (76%), respectively. None of the discrimination indices had 100% sensitivity or specificity. The highest and lowest sensitivity were found for the RBC count and RDWI, at 92% and 70%, respectively. The RDWI had the highest level of specificity (65%), and the Srivastava index had the lowest level of specificity (30%). The highest positive predictive value found for both Green and King, and RDWI was 78%, and the RBC count had the highest negative predictive value (78%). The Srivastava index had the lowest levels of positive and negative predictive values, 67% and 50%, respectively. According to the findings of this study, the most reliable index in the calculation of Youden's index is the Green and King index. It can be used as a pre-diagnostic tool for IDA and other causes of the microcytic anaemia

Do ultrasound form spontaneously nitrous pigments in nitrite-free pork meat batter?

The effects of ultrasound (US) on myoglobin modification, nitrous pigment formation, color, and total and free sulfhydryl content in nitrite-free pork meat batter were assessed. Five treatments were elaborated: Control (without US); TUS10′12 and TUS20’12 (sonication at 25 kHz, at 12 °C for 10 and 20 min, respectively); TUS10’18 and TUS20’18 (sonication at 25 kHz, at 18 °C for 10 and 20 min, respectively). Sonication for 20 min at 12 °C increased OxyMb and DeoxyMb pigments while reducing MetMb levels. This US condition also yielded higher red color indices and lower yellow color indices. Moreover, TUS20’12 exhibited enhanced nitrous pigment formation and decreased FerrylMb and free sulfhydryl (SH) values, indicating reduced oxidation in OxyMb and DeoxyMb pigments. In conclusion, the findings demonstrate that US can impart a cured color to nitrite-free meat products.

RSARE: A physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background

The green leaf area index (LAI) is an important structural parameter that can be used for monitoring plant transpiration and carbon cycling over large spatial extents. LAI is also critical for understanding biophysical processes and predicting crop productivity. However, the retrieval of wheat green LAI based on canopy reflectance is always affected by the background material. For example, in the Yangtze River Basin of China, rice residues are typically returned to rice–wheat rotation fields. Thus the background for observing wheat canopy varies from a mixture of traditional wheat-soil components to wheat-residue or wheat-residue-soil, posing a significant challenge for accurate LAI estimation at the early stages of crop growth. Furthermore, when current vegetation indices (VIs) are used to estimate LAI, leaf chlorophyll content (LCC) and field moisture are two additional confounding factors. To resolve these issues, we propose a novel residue-soil adjusted red edge difference index (RSARE), which draws on concepts from a simple physical algorithm, i.e., linear spectral mixture analysis (LSMA). The results of field, satellite and modeling experiments demonstrate that the RSARE-LAI model is generally not sensitive to LCC, or to variations in residue-soil and traditional soil backgrounds, and estimates LAI with high accuracy (RMSEval = 0.55, RRMSEval=20.71%). In comparison to traditional VI-LAI maps, RASRE-LAI maps are less sensitive to confounding factors associated with the background components, field moisture, LCC, and both inter-annual and geographical differences. This novel residue-soil background-resistant RSARE particularly improves the accuracy of wheat LAI estimation at low LAI levels, thus facilitating large-scale mapping of LAI in the early growing season.

Vegetation Indices for Predicting the Growth and Harvest Rate of Lettuce

Urbanization has provided greater demand for food, and the search for strategies capable of reducing waste is essential to ensure food security. Lettuce (Lactuca sativa L.) culture has a short life cycle and its harvest point is determined visually, causing waste and important losses. Using vegetation indices could be an important alternative to reduce errors during harvest definition. The objective of this study was to evaluate different vegetation indices to predict the growth rate and harvest point of lettuce. Twenty-five genotypes of biofortified green lettuce were evaluated. The Green Leaf Index (GLI), Normalized Green Red Difference Index (NGRDI), Spectral Slope Saturation Index (SI), and Overall Hue Index (HUE) were calculated from images captured at 1, 8, 18, 24, and 36 days after transplanting (vegetative state). The diameter and average leaf area of plants were measured using QGIS software. Green mass, number of leaves, and plant and stem diameter were measured in the field. The means were compared using the Scott–Knott test (p ≤ 0.05) and simple linear regression models were generated to monitor the growth rate, obtaining R2 values ranging from 62% to 99%. Genetic dissimilarity was confirmed by the multivariate analysis presenting a cophenetic correlation coefficient of 88.49%. Furthermore, validation between data collected in the field versus data obtained by imaging was performed using Pearson’s correlations and showed moderate to high values. Overall, the vegetation indices SI, GLI, and NGRDI were efficient for monitoring the growth rate and determining the harvest point of different green lettuce genotypes, in attempts to reduce waste and losses. It is suggested that the definition of the harvest point based on vegetation indices are specific for each genotype.

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges’ rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties.

Techno-functional, Nutritional and Sensorial Qualities of Raw, Brined and Dried Mushrooms (Pleurotus ostreatus) Produced on Oil Palm By- Products in Benin

Edible mushrooms consumed in Benin include a diversity of species including oyster mushrooms (Pleurotus ostreatus). This Oyster mushroom production is increasing in the South of the country. The study aims to evaluate their techno-functional, nutritional and sensory properties in order to optimize their exploitation in food technology and gastronomy. Therefore, 900g of Pleurotus ostreatus were produced on Oil Palm By- Products and divided into three lots of 300g for the study. Lot 1 was used to evaluate the techno-functional and nutritional parameters of fresh mushrooms. Lot 2 was brined while lot 3 was dried. Samples from all three lots were collected and used to study the variability in sensory quality by treatment. The study shows that Pleurotus ostreatus mushrooms produced in Benin have several technological-functional and nutritional benefits that can be used for food security. The luminance values (L*), red index (a*) and yellow index (b*) for fresh oyster mushrooms were respectively 74.6, 2.78 and 26.35. The hue value and the chroma value of the raw mushrooms were respectively of 5.98 and 26.49. The pH of fresh oyster mushrooms was 6.35. The water holding capacity was 11.85% and the technological yield was 88.15%. Nutritionally, the dry matter, fat, ash and protein content of the Pleurotus ostreatus were 9.8%, 2.6%, 6.7%, and 14.78% respectively. About sensorial quality, fresh and brined mushrooms were better appreciated than dried mushrooms. Overall, Pleurotus ostreatus mushrooms produced in palm residues have several techno-functional and nutritional assets and may be promoted for food and nutritional security.