Remote Sensing for Site-Specific Crop Management: Evaluating the Potential of Digital Multi-Spectral Imagery for Monitoring Crop Variability and Weeds within Paddocks

This paper analyses the potential and limitations of airborne remote sensing systems for detecting crop growth variability and weed infestation within paddocks at specified capture times. The detection of areas of crop growth variability can help farmers become aware of regions within their paddock where they may be experiencing above and below average yields due to changes in soil or management conditions. For instance, the early detection of weed infestation within cereal crops is crucial for lessening their impact on the final yield. Transect sampling within a canola paddock of a broad acre agricultural property in the South West of Western Australia was conducted synchronous with the capture of 1m spatial resolution DMSI. The four individual bands (blue, green, red and near- infrared) of the DMSI were correlated with LAI and weed density counts collected in the paddock. Statistical analyses show the LAI of canola had strong negative correlations with the blue (-0.93) and red (-0.89) bands and a strong positive correlation was found with the near-infrared band (0.82). The strong correlations between the canola LAI and selected bands of the DMSI indicate that this may be a suitable technique for monitoring canola variability to derive information layers that can be used in creating meaningful "within-field" management units. Likewise, DMSI could be used as a non-invasive tool for in season crop monitoring. The correlation analysis with the weed density (e.g. self sown wheat, ryegrass and clover) attributed to only one negative weak correlation with the red band (-0.38). The less successful detection of weeds is attributed to the minimal weeddensity within the paddock (e.g. mean 34 plants m-2) and indistinct spectral difference from canola at the early time of imagery capture required by farmers for effective variable rate applications of herbicides.LAI, remote sensing, crop density, vegetation indices, weed mapping., Crop Production/Industries,

Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture

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Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

A precise estimation and the characterization of the spatial variability of microclimate conditions (MCCs) are essential for risk assessment and site-specific management of vector-borne diseases and crop pests. The objective of this study was to estimate at local scale, and assess the uncertainties of Surface Microclimate Indicators (SMIs) derived from airborne infrared thermography and multispectral imaging. SMIs including Surface Temperature (ST) were estimated in southern Quebec, Canada. The formulation of their uncertainties was based on in-situ observations and the law of propagation of uncertainty. SMIs showed strong local variability and intra-plot variability of MCCs in the study area. The ST values ranged from 290 K to 331 K. They varied more than 17 K on vegetable crop fields. The correlation between ST and in-situ observations was very high (r = 0.99, p = 0.010). The uncertainty and the bias of ST compared to in-situ observations were 0.73 K and ±1.42 K respectively. This study demonstrated that very high spatial resolution multispectral imaging and infrared thermography present a good potential for the characterization of the MCCs that govern the abundance and the behavior of disease vectors and crop pests in a given area

Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops. Evaluation of their accuracy in vegetable crops is limited. This study explored the accuracy of proximal hyperspectral and satellite multispectral sensors (Sentinel-2 and WorldView-3) for the prediction of carrot root yield across three growing regions featuring different cropping configurations, seasons and soil conditions. Above ground biomass (AGB), canopy reflectance measurements and corresponding yield measures were collected from 414 sample sites in 24 fields in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia. The optimal sensor (hyperspectral or multispectral) was identified by the highest overall coefficient of determination between yield and different vegetation indices (VIs) whilst linear and non-linear models were tested to determine the best VIs and the impact of the spatial resolution. The optimal regression fit per region was used to extrapolate the point source measurements to all pixels in each sampled crop to produce a forecasted yield map and estimate average carrot root yield (t/ha) at the crop level. The latter were compared to commercial carrot root yield (t/ha) obtained from the growers to determine the accuracy of prediction. The measured yield varied from 17 to 113 t/ha across all crops, with forecasts of average yield achieving overall accuracies (% error) of 9.2% in WA, 10.2% in Qld and 12.7% in Tas. VIs derived from hyperspectral sensors produced poorer yield correlation coefficients (R2 < 0.1) than similar measures from the multispectral sensors (R2 < 0.57, p < 0.05). Increasing the spatial resolution from 10 to 1.2 m improved the regression performance by 69%. It is impossible to non-destructively estimate the pre-harvest spatial yield variability of root vegetables such as carrots. Hence, this method of yield forecasting offers great benefit for managing harvest logistics and forward selling decisions

Precision Oliviculture: Research Topics, Challenges, and Opportunities—A Review

Since the beginning of the 21st century, there has been an increase in the agricultural area devoted to olive growing and in the consumption of extra virgin olive oil (EVOO). The continuous change in cultivation techniques implemented poses new challenges to ensure environmental and economic sustainability. In this context, precision oliviculture (PO) is having an increasing scientific interest and impact on the sector. Its implementation depends on various technological developments: sensors for local and remote crop monitoring, global navigation satellite system (GNSS), equipment and machinery to perform site-specific management through variable rate application (VRA), implementation of geographic information systems (GIS), and systems for analysis, interpretation, and decision support (DSS). This review provides an overview of the state of the art of technologies that can be employed and current applications and their potential. It also discusses the challenges and possible solutions and implementations of future technologies such as IoT, unmanned ground vehicles (UGV), and machine learning (ML)

Role of Hyperspectral imaging for Precision Agriculture Monitoring

In the modern era precision agriculture has started emerging as a new revolution. Remote sensing is generally regarded as one of the most important techniques for agricultural monitoring at multiple spatiotemporal scales. This has expanded from traditional systems such as imaging systems, agricultural monitoring, atmospheric science, geology and defense to a variety of newly developing laboratory-based measurements. The development of hyperspectral imaging systems has taken precision agriculture a step further. Because of the spectral range limit of multispectral imagery, the detection of minute changes in materials is significantly lacking, this shortcoming can be overcome by hyperspectral sensors and prove useful in many agricultural applications. Recently, various emerging platforms also popularized hyperspectral remote sensing technology, however, it comes with the complexity of data storage and processing. This article provides a detailed overview of hyperspectral remote sensing that can be used for better estimation in agricultural applications

Signals in the Soil: Subsurface Sensing

In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges

Development of software to process aerial images for agricultural purposes

Remote sensing has been used in precision agriculture for monitoring crop health, weed management, detecting nutrient stress, and yield forecasting. One method of implementing remote sensing is through aerial imagery. Aerial imagery is being used in precision agriculture for a variety causes such as to detect crop stress, fertilizer skips and overlaps, nitrogen excesses and deficiencies and detect irregular or reduced crop stand. These crop features are noted by extracting spectral information from the images. The spectral data is obtained from the images by using software programs. The software programs process the images one at a time or assemble them together and process them all at once. To obtain information about an extensive region of agricultural crop and save time, it is advisable to assemble the images and process them simultaneously. This research provides a low cost software program to assemble images and process the images simultaneously to obtain data pertinent to make decision process regarding agricultural crops. This study utilized geographic location of the area being photographed as reference points for creating the mosaic of the images taken. The software has the ability to assemble images taken randomly over a specified area. Vegetative indices are used as the parameter to detect crop vigor and density. Normalized difference vegetative index and ratio vegetative index were measured from the spectral information in the images. The software achieved the capability of assembling 100 randomly taken images in less than two minutes and represents the variation in vegetative indices in varying shades of red, providing a map for detecting crop variability

Optimizing precision irrigation of a vineyard to improve water use efficiency and profitability by using a decision-oriented vine water consumption model

While the agronomic and economic benefits of regulated deficit irrigation (RDI) strategies have long been established in red wine grape varieties, spatial variability in water requirements across a vineyard limits their practical application. This study aims to evaluate the performance of an integrated methodology—based on a vine water consumption model and remote sensing data—to optimize the precision irrigation (PI) of a 100-ha commercial vineyard during two consecutive growing seasons. In addition, a cost-benefit analysis (CBA) was conducted of the tested strategy. Using an NDVI generated map, a vineyard with 52 irrigation sectors and the varieties Tempranillo, Cabernet and Syrah was classified in three categories (Low, Medium and High). The proposed methodology allowed viticulturists to adopt a precise RDI strategy, and, despite differences in water requirement between irrigation sectors, pre-defined stem water potential thresholds were not exceeded. In both years, the difference between maximum and minimum water applied in the different irrigation sectors varied by as much as 25.6%. Annual transpiration simulations showed ranges of 240.1–340.8 mm for 2016 and 298.6–366.9 mm for 2017. According to the CBA, total savings of 7090.00 € (2016) and 9960.00 € (2017) were obtained in the 100-ha vineyard with the PI strategy compared to not PI. After factoring in PI technology and labor costs of 5090 €, the net benefit was 20.0 € ha−1 in 2016 and 48.7 € ha−1 in 2017. The water consumption model adopted here to optimize PI is shown to enhance vineyard profitability, water use efficiency and yield.info:eu-repo/semantics/publishedVersio