scholarly journals OPTIMIZATION OF SUGARCANE HARVEST USING REMOTE SENSING

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 857-861 ◽  
Author(s):  
M. Rahimi Jamnani ◽  
A. Liaghat ◽  
F. Mirzaei
Keyword(s):  
Remote Sensing ◽  
Sucrose Concentration ◽  
Sugar Content ◽  
Vegetation Indices ◽  
Sucrose Content ◽  
Harvest Time ◽  
Optimal Model ◽  
Entire Area ◽  
Sugarcane Varieties ◽  
Representative Farms

Abstract. In Iranian sugarcane agro-industries, the harvest time is estimated by sucrose content. Measuring the sucrose content in the juice (pol) during sugarcane harvest season will help users and farmers to achieve the best time for sugarcane harvest, which is important in accurate agricultural management. In harvest season, the pol percent is measured weekly by the destructive method through sampling of different areas of representative farms. In the this method, all fields are not sampled due to the plurality of fields and the need for expenditure and workforce, and the measured samples do not represent the entire area of a field. The aim of this paper is to find an optimal model for determine best harvest time for four sugarcane varieties using satellite vegetation indices, and also to obtain a zoning map which represents the areas ready for harvest during a harvest season in order to achieve maximum sucrose content. The results showed that, compared with NDVI and GVI, GNDVI represented higher correlation with pol (R2=0.885). The optimum values of GNDVI were found to be between 0.5 and 0.55, which indicated the areas with highest sucrose concentration. In addition, the zoning map was presented that makes it possible to separate spatially the areas ready for harvest in each field and they were also showed that central areas of farms ripened (reach maximum sugar content) sooner than sideways.

Performance of New Sugarcane Varieties in the Cabo Rojo-Hormigueros Area of Puerto Rico

1969 ◽  
Vol 62 (1) ◽  
pp. 48-55
Author(s):  
M. Pérez-Zapata ◽  
G. Ramírez-Oliveras ◽  
C. González-Molina
Keyword(s):  
Puerto Rico ◽  
Sugar Content ◽  
Total Sugar ◽  
Sugar Production ◽  
Sucrose Content ◽  
Commercial Variety ◽  
Sugarcane Varieties ◽  
High Sugar ◽  
Plant Crop

The performance of 34 new sugarcane varieties was evaluated in a plant crop and two ratoons at two sites in southwestern Puerto Rico. At Bonilla farm in Cabo Rojo the five leading varieties were PR 65-413, PR 65-339, PR 62-739, UCW 53-69, and PR 980. PR 980, which is the leading commercial variety of the Cabo Rojo area, ranked fifth in total sugar production per acre. PR 65-413 and PR 65-339 have the greatest potential as commercial varieties for the Cabo Rojo area, since they are high sugar yielders and suited to mechanization. In the humid valley of Central Eureka in Hormigueros, the most outstanding varieties were PR 1152, PR 61-902, PR 1140, CP 52-43, and NCo 310. PR 1152 is high in sugar content and cane tonnage production, and is suitable for mechanized harvesting. PR 1140 and PR 61-902 also had a good sucrose content, but their performance in subsequent crops was poor. NCo 310 and UCW 53-69 are not suitable for mechanized harvesting.

scholarly journals Foliar herbivory increases sucrose concentration in bracteal extrafloral nectar of cotton

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258836
Author(s):  
Cody C. Gale ◽  
Pierre Lesne ◽  
Caroline Wilson ◽  
Anjel M. Helms ◽  
Charles P-C. Suh ◽  
...  
Keyword(s):  
Sucrose Concentration ◽  
Sugar Content ◽  
Caloric Content ◽  
Sucrose Content ◽  
Nectar Production ◽  
Total Sugar Content ◽  
Sucrose Induction ◽  
Predatory Insects ◽  
A Minor ◽  
Induced Changes

Cultivated cotton, such as Gossypium hirsutum L., produces extrafloral (EF) nectar on leaves (foliar) and reproductive structures (bracteal) as an indirect anti-herbivore defense. In exchange for this carbohydrate-rich substance, predatory insects such as ants protect the plant against herbivorous insects. Some EF nectar-bearing plants respond to herbivory by increasing EF nectar production. For instance, herbivore-free G. hirsutum produces more bracteal than foliar EF nectar, but increases its foliar EF nectar production in response to herbivory. This study is the first to test for systemically induced changes to the carbohydrate composition of bracteal EF nectar in response to foliar herbivory on G. hirsutum. We found that foliar herbivory significantly increased the sucrose content of bracteal EF nectar while glucose and fructose remained unchanged. Sucrose content is known to influence ant foraging behavior and previous studies of an herbivore-induced increase to EF nectar caloric content found that it led to increased ant activity on the plant. As a follow-up to our finding, ant recruitment to mock EF nectar solutions that varied in sucrose content was tested in the field. The ants did not exhibit any preference for either solution, potentially because sucrose is a minor carbohydrate component in G. hirsutum EF nectar: total sugar content was not significantly affected by the increase in sucrose. Nonetheless, our findings raise new questions about cotton’s inducible EF nectar responses to herbivory. Further research is needed to determine whether an herbivore-induced increase in sucrose content is typical of Gossypium spp., and whether it constitutes a corollary of systemic sucrose induction, or a potentially adaptive mechanism which enhances ant attraction to the plant

scholarly journals Novel Vegetation Indices for Cotton Boll Opening Status Estimation Using Sentinel-2 Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1712 ◽  
Author(s):  
Yu Ren ◽  
Yanhua Meng ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Yuxing Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Great Influence ◽  
Remote Sensing Data ◽  
Area Ratio ◽  
Wide Field ◽  
Entire Area ◽  
Sentinel 2

The application of chemical harvest aids to defoliate leaves and ripen bolls plays a significant role in the once-over machine harvest of cotton (Gossypium hirsutum L.) fields. The boll opening rate (BOR) is a key indicator for the determination of harvest aid spraying times. However, the most commonly used method to determine BOR is manual investigation, which is subjective and cannot have a holistic judgment of the entire area. Remote sensing can be employed to overcome these limitations, due to a wide field of vision, acceptably spatial and temporal resolution, and rich spectral information beyond the perception of the human eye. The reflectance of open cotton bolls is relatively high in the visible and near-infrared bands. High reflectance of open bolls has a great influence on the reflectance of the mixed pixels on remote sensing imagery. Therefore, it is an effective method to detect boll opening status by constructing vegetation indices with the sensitive spectral bands of imagery. In this study, we proposed two new vegetation indices based on Sentinel-2 remote sensing data, namely, the boll area ratio index (BARI) and the boll opening rate index (BORI), in order to estimate the boll opening status on a regional scale. The proposed indices were strongly correlated with the boll area ratio (BAR) and BOR. In particular, BARI exhibited the most accurate and robust performance with BAR in the prediction (R2 = 0.754, RMSE = 2.56%) and validation (R2 = 0.706, RMSE = 5.00%) among all the indices, including published indices we chose. Furthermore, when comparing to all other indices, BORI demonstrated the best and satisfactory estimation with BOR in the prediction (R2 = 0.675, RMSE = 7.96%) and validation (R2 = 0.616, RMSE = 2.79%). Meanwhile, an exponential growth relationship between BOR and BAR was identified, and the underlying mechanisms behind this phenomenon were discussed. Overall, through our study, we provided convenient and accurate vegetation indices for the investigation of boll opening status in a cotton-producing area by accessible and free Sentinel-2 imagery.

Paddy Growing Stages Model Based on Vegetation Indices Using Ultra-High Spatial Resolution Images

2020 ◽  
Vol 17 (2) ◽  
pp. 1275-1281
Author(s):  
S. Supriatna ◽  
Fida Afdhalia ◽  
Iqbal Putut Ash Shidiq ◽  
Masita Dwi Mandini Manessa ◽  
Yoanna Ristya
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Rice Production ◽  
Alternative Methods ◽  
Food Sources ◽  
Optimal Model ◽  
Sensing Technology ◽  
Sensing Platform ◽  
Green Vegetation

Paddy is one of the most important food sources in Indonesia. It is evidenced by the increasing number of national rice consumption averagely at 6.29% per year, particularly in 2011–2015. However, the production seems does not equally match the rise in consumption. Estimates in rice production are relatively unreliable. It is due to the uneven planting time in several areas and a conventional method applied to estimate the production. This study proposes alternative methods to estimate rice production. This study aims to analyze the paddy growing stages and determine the most optimal model to estimate the paddy growing stages based on the vegetation indices. This study used the excellence of remote sensing technology especially for paddy field monitoring, emphasizing on paddy growing stages assessment. An airborne remote sensing platform, specifically the Unmanned Aerial Vehicle (UAV) is used to map the rice field in Bekasi Regency, West Java Province. Through mapping at low altitude, the UAV can produce images with ultra-high resolution, so it is very well used for mapping the paddy growing stages with diverse characteristics. Several vegetation indices, derived from Red, Green, and Blue (RGB) bands, namely Normalized Green Red Difference Index (NGRDI), Excess Green Vegetation Index (ExG), and Visible Atmospherically Resistant Index (VARI). Furthermore, the regression model is used to obtain the most optimal model of the three vegetation indices used for estimating the paddy growing stages. The result showed that the UAV with RGB bands could be used as a sensor to determine the relationship between vegetation indices to the paddy growing stages and the most optimal model for estimating the paddy growing stages based on the vegetation indices is ExG (R2 = 0.88).

scholarly journals Biochemical Profiling of Source and Sink Tissues at Different Growth Stages of Early and Late Maturing Varieties of Sugarcane (Saccharum spp. hybrids)

2018 ◽  
Vol 15 (3) ◽  
pp. 611-618 ◽  
Author(s):  
Indu Verma ◽  
Kriti Roopendra ◽  
Amaresh Chandra ◽  
Aisha Kamal
Keyword(s):  
Sugar Content ◽  
Reducing Sugar ◽  
Growth Stages ◽  
Sucrose Content ◽  
Sugarcane Varieties ◽  
Distinct Source ◽  
Sucrose Storage ◽  
Sink Demand ◽  
Source Sink ◽  
Source And Sink

Sugarcane being C4 crop exhibits distinct source-sink signaling pathway that helps in storing remarkably high amount of sucrose in its sink tissues that makes it a highly remunerable crop worldwide. In the present study sugar content was profiled in both source and sink tissues of early (CoJ64) and late (BO91) maturing sugarcane varieties. At early growth stage (i.e. at 210 DAP) sink tissues of both varieties exhibited higher reducing sugar and low sucrose content while in source tissues both sucrose and reducing sugar content was observed high, depicted lower sink demand for sucrose. With maturity, when sink demand for sucrose storage increased, rise in sucrose content was seen in sink tissues, whereas in source tissues gradual decrease in sucrose and reducing sugar content was observed. Accumulation of sucrose was found much higher in CoJ64 than those in BO91. In CoJ64 maximum sucrose content (64.2%) was seen at 330 DAP while in BO91 it was 41.8% at 390 DAP. At this stage, source tissues too exhibited higher sucrose and reducing sugar content. Thus sucrose synthesis in source tissues and its transportation to the sink tissues is primarily governed by the sink demand.

LUMINESCENT DETERMINATION OF SUGAR IN NATURAL HONEY

2020 ◽  
Vol 0 (4) ◽  
pp. 29-32
Author(s):  
B.M. GAREEV ◽  
◽  
A.M. ABDRAKHMANOV ◽  
G.L. SHARIPOV ◽  
◽  
...  
Keyword(s):  
Quantum Dots ◽  
Laboratory Test ◽  
Aqueous Solutions ◽  
Analytical Method ◽  
Sugar Content ◽  
Carbon Quantum Dots ◽  
Photoluminescence Intensity ◽  
Sucrose Content ◽  
Natural Honey

The photoluminescence of carbon quantum dots synthesized from natural honey and mixtures of honey and sugar has been studied. An increase in the sugar content leads to a decrease in the photoluminescence intensity without changing the shape of the luminescence spectrum of these quantum dots aqueous solutions, which is associated with a decrease in the yield of their synthesis in the sugar presence. The discovered effect can be used to detect sugar in honey. When examining five different market samples of flower honey using this method, two of them showed a significant decrease in the photoluminescence intensity. A laboratory test for compliance with GOST 19792-2017 Standard requirements established an excess of the sucrose content in these samples. Luminescent determination of sugar in honey does not require complicated equipment and can be used to develop a new analytical method for determining the sugar content in counterfeit natural honey.

Analysis of Remote Sensing based Vegetation Indices (VIs) for Unmanned Aerial System (UAS): A Review

2020 ◽  
Vol 3 (2) ◽  
pp. 58-73
Author(s):  
Vijay Bhagat ◽  
Ajaykumar Kada ◽  
Suresh Kumar
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Satellite Remote Sensing ◽  
Vegetation Indices ◽  
Unmanned Aerial System ◽  
Sustainable Land Management ◽  
Efficient Tool ◽  
Site Specific ◽  
The Earth ◽  
Interesting Area

Unmanned Aerial System (UAS) is an efficient tool to bridge the gap between high expensive satellite remote sensing, manned aerial surveys, and labors time consuming conventional fieldwork techniques of data collection. UAS can provide spatial data at very fine (up to a few mm) and desirable temporal resolution. Several studies have used vegetation indices (VIs) calculated from UAS based on optical- and MSS-datasets to model the parameters of biophysical units of the Earth surface. They have used different techniques of estimations, predictions and classifications. However, these results vary according to used datasets and techniques and appear very site-specific. These existing approaches aren’t optimal and applicable for all cases and need to be tested according to sensor category and different geophysical environmental conditions for global applications. UAS remote sensing is a challenging and interesting area of research for sustainable land management.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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