Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Wheat (Triticum aestivum L.), rice (Oryza sativa L.), and maize (Zea mays L.) provide about two-thirds of all energy in human diets, and four major cropping systems in which these cereals are grown represent the foundation of human food supply. Yield per unit time and land has increased markedly during the past 30 years in these systems, a result of intensified crop management involving improved germplasm, greater inputs of fertilizer, production of two or more crops per year on the same piece of land, and irrigation. Meeting future food demand while minimizing expansion of cultivated area primarily will depend on continued intensification of these same four systems. The manner in which further intensification is achieved, however, will differ markedly from the past because the exploitable gap between average farm yields and genetic yield potential is closing. At present, the rate of increase in yield potential is much less than the expected increase in demand. Hence, average farm yields must reach 70–80% of the yield potential ceiling within 30 years in each of these major cereal systems. Achieving consistent production at these high levels without causing environmental damage requires improvements in soil quality and precise management of all production factors in time and space. The scope of the scientific challenge related to these objectives is discussed. It is concluded that major scientific breakthroughs must occur in basic plant physiology, ecophysiology, agroecology, and soil science to achieve the ecological intensification that is needed to meet the expected increase in food demand. <s> BIB001 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Applications of remote sensing in crop production are becoming increasingly popular due in part to an increased concern with pollution of surface and ground waters due to over-fertilization of agricultural lands and the need to compensate for spatial variability in a field. Past research in this area has focused primarily on N stress in crops. Other stresses and the interactions have not been fully evaluated. A field experiment was conducted to determine wavelengths andlor combinations of wavelengths that are indicative of P and N deficiency and also the interaction between these in corn (Zea mays L.). The field experiment was a randomized complete block design with four replications using a factorial arrangement of treatments in an irrigated continuous corn system. The treatment included four N rates (0, 67, 134, and 269 kg N ha -1 ) and four P rates (0, 22, 45, and 67 kg P ha -1 ). Spectral radiance measurements were taken at various growth stages in increments from 350 to 1000 nm and correlated with plant N and P concentration, plant biomass, grain N and P concentration, and grain yield. Reflectance in the near-infrared (NIR) and blue regions was found to predict early season P stress between growth stages V6 and V8. Late season detection of P stress was not achieved. Plant N concentration was best predicted using reflectance in the red and green regions of the spectrum, while grain yield was estimated using reflectance in the NIR region, with the particular wavelengths of importance changing with growth stage. <s> BIB002 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> The data acquired from the hyperspectral airborne sensor DAIS-7915 over Izrael Valley in northern Israel was processed to yield quantitative soil properties maps of organic matter, soil field moisture, soil saturated moisture, and soil salinity. The method adopted for this purpose was the Visible and Near Infrared Analysis (VNIRA) approach, which yields an empirical model for predicting the soil property in question from both wet chemistry and spectral information of a representative set of samples (calibration set). Based on spectral laboratory data that show a significant capability to predict the above soil properties and populations using the VNIRA strategy, the next step was to examine this feasibility under a hyperspectral remote sensing (HSR) domain. After atmospherically rectifying the DAIS-7915 data and omitting noisy bands, the VNIRA routine was performed to yield a prediction equation model for each property, using the reflectance image data. Applying this equation on a pixel-bypixel basis reve... <s> BIB003 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Abstract This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy). At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation. At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWTcanopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level ( EWT ― ). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWTcanopy and EWT ― was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWTcanopy and an overestimation of leaf EWT ― with relative errors around 19%. Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models. <s> BIB004 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Abstract This paper compares predictions of soil organic carbon (SOC) using visible and near infrared reflectance (vis–NIR) hyperspectral proximal and remote sensing data. Soil samples were collected in the Narrabri region, dominated by Vertisols, in north western New South Wales (NSW), Australia. Vis–NIR spectra were collected over this region proximally with an AgriSpec portable spectrometer (350–2500 nm) and remotely from the Hyperion hyperspectral sensor onboard satellite (400–2500 nm). SOC contents were predicted by partial least-squares regression (PLSR) using both the proximal and remote sensing spectra. The spectral resolution of the proximal and remote sensing data did not affect prediction accuracy. However, predictions of SOC using the Hyperion spectra were less accurate than those of the Agrispec data resampled to similar resolution as the Hyperion spectra. Finally, the SOC map predicted using Hyperion data shows similarity with field observations. There is potential for the use of hyperspectral remote sensing for predictions of soil organic carbon. The use of these techniques will facilitate the implementation of digital soil mapping. <s> BIB005 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Continuing population and consumption growth will mean that the global demand for food will increase for at least another 40 years. Growing competition for land, water, and energy, in addition to the overexploitation of fisheries, will affect our ability to produce food, as will the urgent requirement to reduce the impact of the food system on the environment. The effects of climate change are a further threat. But the world can produce more food and can ensure that it is used more efficiently and equitably. A multifaceted and linked global strategy is needed to ensure sustainable and equitable food security, different components of which are explored here. <s> BIB006 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Global food demand is increasing rapidly, as are the environmental impacts of agricultural expansion. Here, we project global demand for crop production in 2050 and evaluate the environmental impacts of alternative ways that this demand might be met. We find that per capita demand for crops, when measured as caloric or protein content of all crops combined, has been a similarly increasing function of per capita real income since 1960. This relationship forecasts a 100–110% increase in global crop demand from 2005 to 2050. Quantitative assessments show that the environmental impacts of meeting this demand depend on how global agriculture expands. If current trends of greater agricultural intensification in richer nations and greater land clearing (extensification) in poorer nations were to continue, ∼1 billion ha of land would be cleared globally by 2050, with CO2-C equivalent greenhouse gas emissions reaching ∼3 Gt y−1 and N use ∼250 Mt y−1 by then. In contrast, if 2050 crop demand was met by moderate intensification focused on existing croplands of underyielding nations, adaptation and transfer of high-yielding technologies to these croplands, and global technological improvements, our analyses forecast land clearing of only ∼0.2 billion ha, greenhouse gas emissions of ∼1 Gt y−1, and global N use of ∼225 Mt y−1. Efficient management practices could substantially lower nitrogen use. Attainment of high yields on existing croplands of underyielding nations is of great importance if global crop demand is to be met with minimal environmental impacts. <s> BIB007 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Increasing population and consumption are placing unprecedented demands on agriculture and natural resources. Today, approximately a billion people are chronically malnourished while our agricultural systems are concurrently degrading land, water, biodiversity and climate on a global scale. To meet the world's future food security and sustainability needs, food production must grow substantially while, at the same time, agriculture's environmental footprint must shrink dramatically. Here we analyse solutions to this dilemma, showing that tremendous progress could be made by halting agricultural expansion, closing 'yield gaps' on underperforming lands, increasing cropping efficiency, shifting diets and reducing waste. Together, these strategies could double food production while greatly reducing the environmental impacts of agriculture. <s> BIB008 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Hyperspectral imaging (HSI) offers high potential as a non-invasive diagnostic tool for disease detection. In this paper leaf characteristics and spectral reflectance of sugar beet leaves diseased with Cercospora leaf spot, powdery mildew and leaf rust at different development stages were connected. Light microscopy was used to describe the morphological changes in the host tissue due to pathogen colonisation. Under controlled conditions a hyperspectral imaging line scanning spectrometer (ImSpector V10E) with a spectral resolution of 2.8 nm from 400 to 1000 nm and a spatial resolution of 0.19 mm was used for continuous screening and monitoring of disease symptoms during pathogenesis. A pixel-wise mapping of spectral reflectance in the visible and near-infrared range enabled the detection and detailed description of diseased tissue on the leaf level. Leaf structure was linked to leaf spectral reflectance patterns. Depending on the interaction with the host tissue, the pathogens caused disease-specific spectral signatures. The influence of the pathogens on leaf reflectance was a function of the developmental stage of the disease and of the subarea of the symptoms. Spectral reflectance in combination with Spectral Angle Mapper classification allowed for the differentiation of mature symptoms into zones displaying all ontogenetic stages from young to mature symptoms. Due to a pixel-wise extraction of pure spectral signatures a better understanding of changes in leaf reflectance caused by plant diseases was achieved using HSI. This technology considerably improves the sensitivity and specificity of hyperspectrometry in proximal sensing of plant diseases. <s> BIB009 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> In the coming decades, continued population growth, rising meat and dairy consumption and expanding biofuel use will dramatically increase the pressure on global agriculture. Even as we face these future burdens, there have been scattered reports of yield stagnation in the world's major cereal crops, including maize, rice and wheat. Here we study data from ∼2.5 million census observations across the globe extending over the period 1961-2008. We examined the trends in crop yields for four key global crops: maize, rice, wheat and soybeans. Although yields continue to increase in many areas, we find that across 24-39% of maize-, rice-, wheat- and soybean-growing areas, yields either never improve, stagnate or collapse. This result underscores the challenge of meeting increasing global agricultural demands. New investments in underperforming regions, as well as strategies to continue increasing yields in the high-performing areas, are required. <s> BIB010 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> A detailed retrospective of the Green Revolution, its achievement and limits in terms of agricultural productivity improvement, and its broader impact at social, environmental, and economic levels is provided. Lessons learned and the strategic insights are reviewed as the world is preparing a “redux” version of the Green Revolution with more integrative environmental and social impact combined with agricultural and economic development. Core policy directions for Green Revolution 2.0 that enhance the spread and sustainable adoption of productivity enhancing technologies are specified. <s> BIB011 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> C. PRECISION AGRICULTURE <s> Abstract This paper presents a method for mapping water stress in a maize field using hyperspectral remote sensing imagery. An airborne survey using AISA (Specim, Finland) was performed in July 2008 over an experimental farm in Italy. Hyperspectral data were acquired over a maize field with three different irrigation regimes. An intensive field campaign was also conducted concurrently with imagery acquisition to measure relative leaf water content (RWC), active chlorophyll fluorescence (ΔF/ F m ′ ), leaf temperature (Tl) and Leaf Area Index (LAI). The analysis of the field data showed that at the time of the airborne overpass the maize plots with irrigation deficits were experiencing a moderate water stress, affecting the plant physiological status (ΔF/ F m ′ , difference between Tl and air temperature (Tair), and RWC) but not the canopy structure (LAI). Among the different Vegetation Indices (VIs) computed from the airborne imagery the Photochemical Reflectance Index computed using the reflectance at 570 nm as the reference band (PRI570) showed the strongest relationships with ΔF/ F m ′ (r2 = 0.76), Tl − Tair (r2 = 0.82) and RWC (r2 = 0.64) and the red-edge Chlorophyll Index (CIred-edge) with LAI (r2 = 0.64). Thus PRI has been proven to be related to water stress at early stages, before structural changes occurred. A method based on an ordinal logit regression model was proposed to map water stress classes based on airborne hyperspectral imagery. PRI570 showed the highest performances when fitted against water stress classes, identified by the irrigation amounts applied in the field, and was therefore used to map water stress in the maize field. This study proves the feasibility of mapping stress classes using hyperspectral indices and demonstrates the potential applicability of remote sensing data in precision agriculture for optimizing irrigation management. <s> BIB012
Many studies have indicated that the world's crop production needs to be doubled by the end 2050 due to the rapidly growing population in the world BIB007 . However, various studies have shown that the crop yields are no longer increasing at a rate to fulfill the growing population needs BIB001 , BIB010 . Recent studies have also indicated that increasing crop yields without using more land for cultivation, is the most effective way for ensuring food security BIB006 , BIB008 . Global poverty and undernourishment can directly be reduced by increasing crop production; moreover most of the poor and undernourished population consists of farmers themselves BIB011 . Zhang et al.  reviewed the state of the art deep learning techniques for representative feature extraction and scene understanding in remote sensing hyperspectral images, Traditionally crop monitoring for disease, water stress, nutrients and insect attack was carried out by manual visual inspection from the ground. These methods were limited by the fact that the visual symptoms often appear at later stages of disease, thus making it difficult to restore plant health. Advancement in airborne and ground based HSI methods has made possible the evaluation of crop stresses, analyzing soil and vegetation characteristics in a cost effective manner, thus replacing the traditional scouting methods. Drought stress is an important factor affecting crop yields. Chances of a successful crop can be highly increased by timely detection of water related stresses. High water level stresses are noticeable in variations in photosynthetic pigments. These changes lead to yellowish tint in crops, due to the increase reflectance of red wavelength. Unlike human eye, HSI sensors can detect these changes at earlier stages. Colombo et al. BIB004 indicated that changes in leaf equivalent water thickness (EWT) were responsible for changes in leaf reflectance in the visible and infrared spectrum. They stated that hyperspectral regression indices calculated from HSI were powerful tools for estimation of water content at leaf as well as at landscape level. Rascher et al.  used a portable HSI system and photochemical reflectance index to estimate water stress in leaves of tropical tress and observed the temporal effects of dehydrations on tree leaves. Rossini et al. BIB012 found that HSI is useful in detecting drought stress at farm level with corn. They showed that irrigation deficits can be accurately mapped well before drought stress affected the canopy structure. Deficiencies in nutrients and soil contamination cause various symptoms that can be assessed by HSI. Schuerger et al.  used HSI to observe zinc deficiency and toxicity for identification of chlorophyll levels relating to stress symptoms. They indicated that traditional direct sampling methods are much more costly than HSI. Dunagan et al.  analyzed mercury levels in mustard plants and found that spectral signatures were notably related to the contaminant levels. Osborne et al. BIB002 showed that biomass, yield under stress, nitrogen and phosphorous concentrations can be estimated by using HSI. Mahlein et al. BIB009 studied different development stages of diseased sugar beet leaves using HSI. Figure 6 shows spectral signatures of healthy as well as diseased sugar beet leaves. This study also showed that HSI has a great potential for analyzing plant diseases. Analysis of soil characteristics can play a vital role in increasing crop yields. Ben-Dor et al. BIB003 al successfully mapped vital characteristics of soil in a field scale experiment including moisture, soil organic matter and soil sanity. Gomez et al. BIB005 estimated the organic carbon content in soil with accuracy. Growth monitoring of crops has made it possible to forecast production. Liu et al.  improved winter wheat yield prediction using new spectral parameters. The fine classification technology in agriculture has also matured greatly. Figure 7 shows the HSI based fine classification of vegetable growing regions.