Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

Correlation between vegetation indexes generated at Vitis Vinifera L. and soil, plant and production parameters for emergency application in decision making

ABSTRACT: Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.

Biomass and vegetation index by remote sensing in different caatinga forest areas

ABSTRACT: Continued unsustainable exploitation of natural resources promotes environmental degradation and threatens the preservation of dry forests around the world. This situation exposes the fragility and the necessity to study landscape transformations. In addition, it is necessary to consider the biomass quantity and to establish strategies to monitor natural and anthropic disturbances. Thus, this research analyzed the relationship between vegetation index and the estimated biomass using allometric equations in different Brazilian caatinga forest areas from satellite images. This procedure is performed by estimating the biomass from 9 dry tropical forest fragments using allometric equations. Area delimitations were obtained from the Embrapa collection of dendrometric data collected in the period between 2011 and 2012. Spectral variables were obtained from the orthorectified images of the RapidEye satellite. The aboveground biomass ranged from 6.88 to 123.82 Mg.ha-1. SAVI values were L = 1 and L = 0.5, while NDVI and EVI ranged from 0.1835 to 0.4294, 0.2197 to 0.5019, 0.3622 to 0.7584, and 0.0987 to 0.3169, respectively. Relationships among the estimated biomass and the vegetation indexes were moderate, with correlation coefficients (Rs) varying between 0.64 and 0.58. The best adjusted equation was the SAVI equation, for which the coefficient of determination was R² = 0.50, R2aj = 0.49, RMSE = 17.18 Mg.ha-1 and mean absolute error of prediction (MAE) = 14.07 Mg.ha-1, confirming the importance of the Savi index in estimating the caatinga aboveground biomass.

Impact of Structural, Photochemical and Instrumental Effects on Leaf and Canopy Reflectance Variability in the 500–600 nm Range

Current rapid technological improvement in optical radiometric instrumentation provides an opportunity to develop innovative measurements protocols where the remote quantification of the plant physiological status can be determined with higher accuracy. In this study, the leaf and canopy reflectance variability in the PRI spectral region (i.e., 500–600 nm) is quantified using different laboratory protocols that consider both instrumental and experimental set-up aspects, as well as canopy structural effects and vegetation photoprotection dynamics. First, we studied how an incorrect characterization of the at-target incoming radiance translated into an erroneous vegetation reflectance spectrum and consequently in an incorrect quantification of reflectance indices such as PRI. The erroneous characterization of the at-target incoming radiance translated into a 2% overestimation and a 31% underestimation of estimated chlorophyll content and PRI-related vegetation indexes, respectively. Second, we investigated the dynamic xanthophyll pool and intrinsic Chl vs. Car long-term pool changes affecting the entire 500–600 nm spectral region. Consistent spectral behaviors were observed for leaf and canopy experiments. Sun-adapted plants showed a larger optical change in the PRI range and a higher capacity for photoprotection during the light transient time when compared to shade-adapted plants. Outcomes of this work highlight the importance of well-established spectroscopy sampling protocols to detect the subtle photochemical features which need to be disentangled from the structural and biological effects.

Discrimination of forest species using medium spatial resolution images

This research aimed to evaluate the potential of orbital images from the Landsat-8/OLI and Sentinel-2 /MSI sensors in the distinction of species from a forest stand located in Campo Belo do Sul, State of Santa Catarina, Brazil. A total of 53 plots were allocated in the field, in which the central coordinate of the plot was collected using GPS receivers. In SIG environment, two images were used, one from each sensor, closely dated to the field campaign and with no clouds and other atmospheric factors. Then, the images were processed, and 17 vegetation indexes were calculated for each one. The indices were compared statistically by the t-Student test for independent samples. The indices that provided the best species differentiation were: CRI, GNDVI, NDI11, NDI12, NDVI, RDVI, SAVI, and SR. In addition, the species with greater prominence in the Landsat-8/OLI images was Eucalyptus spp. whereas Cunninghamia lanceolata (Lamb.) Hooker was easily distinguished in Sentinel-2 images. It was possible to differentiate the species from remote data derived from the Sentinel-2/MSI and Landsat-8/OLI sensors. However, further studies using other Remote Sensing data sources and other species are suggested.

Methods for Mid-Term Forecasting of Crop Export and Production

A vast number of studies are devoted to the short-term forecasting of agricultural production and market. However, those results are more helpful for market traders than producers and agricultural policy regulators because any structural change in that field requires a while to be implemented. The mid and long-term predictions (from one year and more) of production and market demand seem more helpful. However, this problem requires considering long-term dependencies between various features. The most natural way of analyzing all those features together is with deep neural networks. The paper presents neural network models for mid-term forecasting of crop production and export, which considers heterogeneous features such as trade flows, production levels, macroeconomic indicators, fuel pricing, and vegetation indexes. They also utilize text-mining to assess changes in the news flow related to the state agricultural policy, sanctions, and the context in the local and international food markets. We collected and combined data from various local and international providers such as UN FAOSTAT, UN Comtrade, social media, the International Monetary Fund for 15 of the world’s top wheat exporters. The experiments show that the proposed models with additive regularization can accurately predict grain export and production levels. We also confirmed that vegetation indexes and fuel prices are crucial for export prediction. Still, the fuel prices seem to be more important for predicting production than the NDVI indexes from past observations.

Disturbed Boundaries Extraction in Coal-grain Overlap Areas With High Groundwater Levels Using UAV-based Visible and Multispectral Imagery

Coal-grain overlap areas (CGOA) with high groundwater levels are vulnerable to subsidence and water logging during a series of mining activities, which have adverse impacts on crop yields. Such damage requires full reports of disturbed boundaries for the agricultural reimbursement and ongoing reclamation. Since direct measurements are difficult in such a case because of vast, unreachable areas, so it is necessary to be able to identify out-of-production boundary (OB) and reduced-production boundary (RB) in the corresponding region. In this study, OB was extracted by setting thresholds through characteristics of the cultivated land elevation based on UAV-generated digital surface model (DSM) and digital orthophoto map (DOM). Meanwhile, aboveground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI), were used to select the appropriate vegetation indexes (VI) to perform a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR) and random forest (RF) algorithms. Finally, an improved OTSU segmentation algorithm was applied to extract mild RB and severe RB. The results show elevation threshold segmentation method and the improved OTSU segmentation method can accurately recognize and extract disturbed boundaries, which are consistent with the tonal difference after crop damage in the image. This study provides reference methods and theoretical supports for disturbed boundaries determination in CGOA with high groundwater levels for further agricultural compensation and reclamation processes.

Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes

The assessment of rice panicle initiation is crucial for the management of nitrogen fertilizer application that affects yield and quality of grain. The occurrence of panicle initiation could be determined via either green ring, internode-elongation, or a 1–2 mm panicle, and was observed through manual dissection. The quadratic polynomial regression model was used to construct the model of the trend of normalized difference vegetation index-based vegetation indexes (NDVI-based VIs) between pre-tillering and panicle differentiation stages. The slope of the quadratic polynomial regression model tended to be alleviated in the period in which the panicle initiation stage should occur. The results indicated that the trend of the NDVI-based VIs was correlated with panicle initiation. NDVI-based VIs could be a useful indicator to remotely assess panicle initiation.

Uso de sensores remotos en la determinación del forraje disponible de Urochloa humidicola cv. Llanero bajo pastoreo en la Altillanura colombiana

Modernization of pastoral systems based on the use of Urochloa species in the Colombian Eastern Llanos need the use of remote sensing techniques from satellite platforms to estimate amount of offered forage. In the Carimagua Research Centre of the Colombian Corporation for Agricultural Research (Agrosavia), an Urochloa humidicola cv. Llanero pasture was evaluated using Landsat 8 and Sentinel 2A images. The NDVI, SAVI, EVI y GNDVI vegetation indexes determined by using the blue, green, red and near infrared bands; and the results analyzed with the R free software, to relate those indexes with forage availability field measures taken during the dry season. Forage availability ranged between 290 and 656 kg DM ha-1 and the vegetation indexes for the Landsat 8 and Sentinel 2A sensors were: NDVI = 0.67 (±0.037) and 0.69 (±0.061); SAVI = 0.48 (±0.048) and 0.41 (±0.046); EVI = 0.70 (±0.052) and 0.41 (±0.047); y GNDVI = 0.60 (±0.028) and 0.70 (±0.034), respectively. The relationships between vegetation indexes and forage availability were linear. The Coefficient of Determination (R2= 0.56‒0.72) and the Mean Square Error (MSR =63.95‒80.16) of the prediction equations were used. In conclusion, under the conditions of the study, the EVI for Landsat 8 and NDVI for Sentinel 2A were considered adequate for estimating forage availability of Urochloa humidicola cv. Llanero.

The improvement of the instrumental means for processing and analyzing of space information

The need of the creation of information system with specialized services which allow scientists and specialists to perform thematic processing of Earth remote sensing data, changing the data processing parameters in a certain way, and independently analyze the information received is shown.To achieve the goal of rapid provision of information, the development of specialized information system is considered on the example of the creation of the software package for the dissemination of operational space information, as well as a software package for predicting the yield of grain crops.The structure of the software package for the dissemination of operational space information is shown. The technical and functional characteristics of the separate components of the package are given. The relations of the subsystems of the package are shown during data receiving, processing, analyzing and providing information to interested specialists and researchers. Attention is drawn to the possibilities of wide application of the software package for the dissemination of operational space information for solving scientific and applied problems in various fields of knowledge.The practical application of vegetation indexes in the analysis of operational space information is shown. The data of the practical results of forecasting of the grain crops development, as well as the quantitative indicators of satellites sessions are presented. The possibility of further improvement and development of the created information system is shown.