REVIEW OF THE APPLICATIONS OF SATELLITE REMOTE SENSING IN ORGANIC FARMING – PART II

The use of remote sensing methods for monitoring, managing, and decision support in agriculture is increasingly intensifying. With the advancement of technologies, they become more accessible, while the quality and security of the obtained data are improving. Striving to improve the quality of the environment and its preservation, expanding the areas occupied by organic farming will allow us to achieve these goals. At the same time, this type of agriculture provides healthy and safe food. For this reason, it is of great importance to start applying satellite data in organic farming as quickly as possible. In Part II of the "Review of the applications of satellite remote sensing in organic farming," we examine the various areas of satellite data application in organic farming. Five different areas of satellite data application in organic farming have been identified, including satellite remote sensing monitoring of weeds, remote sensing of crop stress and irrigation needs, yield forecasting using remote sensing methods and remote sensing monitoring of plant nutrition. From the review conducted, we found that satellite data can significantly support and facilitate the transition to organic farming, adequate fertilization, application in phytosanitary monitoring of crops, and assessment of crop stress

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Assessing the utility of unmanned aerial vehicle remotely sensed data for estimating maize leaf area index (LAI) and yield across the growing season.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Background: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

Kasvuston biomassan määrittäminen multispektrikamerakuvien ja 3D-mallinnuksen avulla

Dronejen määrä on kasvanut niin yksityis- kuin yrityskäytössä. Myös maataloudessa ollaan kiinnostuneita droneista, sillä niitä käyttäen on helppo ja nopea tarkkailla suuria peltoaloja. Lisäksi automaattiset lentotehtävät ovat helppoja toteuttaa. Dronekuvaukset antavat laajemman kuvauksen koko pellosta, kuin mitä pellon reunasta voidaan nähdä. Ilmakuvat pelloista helpottavat jatkotoimenpiteiden suunnittelua, kuten esimerkiksi pellon ruiskutustarpeen arvioimista tai lannoituskartan tekemistä. Dronekuvauksia voidaan myös käyttää kasvuston biomassojen arvioimiseen. Tällöin voidaan tarkkailla kasvuston kehittymistä kasvukauden aikana lohkokohtaisesti. Tämän tutkimuksen tavoitteena oli selvittää, kuinka multispektrikameran kuvia ja 3D-mallia voidaan hyödyntää kasvien tarkkailussa. Kasvustosta mitattavia ominaisuuksia olivat kasvien maanpäällinen biomassa, lehtivihreäpitoisuus ja lehtialaindeksi. Tutkimuksessa oli 8 eri kasvia/lannoitustasoa. Tässä tutkimuksessa käytettiin multispektrikameraa ja tavallista RGB-kameraa kasvuston ominaisuuksien määrittämiseen. Multispektrikameran avulla voitiin määrittää kasvuston heijastusarvoja, jotka kuvasivat sitä, kuinka paljon kasvit heijastivat auringon säteilyä takaisin. Multispektrikamera mittasi heijastusarvoja viideltä eri aallonpituusalueelta (sininen, vihreä, punainen, red edge ja NIR), joiden avulla laskettiin NDVI -kasvillisuusindeksi. Näitä heijastusarvoja ja indeksejä verrattiin kasvuston kuiva-ainemassaan, lehtialaindeksiin ja lehtivihreäpitoisuuteen. RGB-kameran ottamista kuvista luotiin kasvustosta 3D-malli, josta laskettiin kasvuston tilavuus. Kasvuston tilavuuksia verrattaisiin sen biomassoihin ja lehtialaindeksin arvoihin. Kuvista laskettujen ja kasvustosta määritettyjen muuttujien välisen riippuvuuden tarkasteluun käytettiin lineaarista regressioanalyysiä. Multispektrikuvista määritetyt muuttujat selittivät näiden tulosten mukaan hieman heikommin kasvuston kuiva-ainemassaa ja lehtialaindeksiä kuin RGB-kameran kuvista määritetyt 3D-mallit. Multispektrikameran kuvaamasta aineistosta voimakkain määritetty riippuvuus oli härkäpavun lehtialaindeksin ja NDVI:n välillä (R2 = 0,85). Multispektrikameran heijastusarvo-/indeksiaineistoa käyttäen määritetyt selitysasteet olivat pieniä: kasvuston kuiva-ainemassa oli keskimäärin 0,15, lehtivihreäpitoisuus 0,14 ja lehtialaindeksi 0,21. 3D-mallinnuksen korkein selitysaste oli kauran kuiva-ainemassan ja siitä mitattujen tilavuuksien välillä (R2 = 0,91). Keskimäärin riippuvuuden selitysaste oli 0,69 tarkasteltaessa kasvien kuiva-ainemassoja ja 3D-mallien tilavuuksia. Kasvien lehtialaindeksin ja 3D-mallien välisen riippuvuuden keskimääräinen selitysaste oli 0,57. Näiden tulosten perusteella multispektrikameran datoista NDVI-indeksi soveltui parhaiten kasvuston kuiva-ainemassan, lehtialaindeksin ja lehtivihreäpitoisuuden määrittämiseen. Eri heijastusalueiden/NDVI-indeksin ja kasvien ominaisuuksien välisissä riippuvuuksissa on kuitenkin eroja eri kasvien välillä. 3D-mallit tuottivat voimakkaampia riippuvuuksia kasvuston biomassan ja lehtialaindeksin arvioimiseen kuin multispektrikuvista määritetyt suureet. Aineiston analysointi laskentamenetelmillä, jotka hyödyntävät useampien aallonpituusalueiden arvoja sekä niistä laskettuja indeksejä, olisi todennäköisesti ollut nyt käytettyä lineaarista regressiota tehokkaampi menetelmä aineiston analysoinnissa. Ulkoisten tekijöiden aiheuttamien häiriöiden poistaminen multispektrikameran kuvista oli hyvin haasteellista. Varsinkin kuivan maan heijastusarvot poikkesivat kasvuston heijastusarvoista. Jatkotutkimuksissa pitäisi kehittää erilaisia kasvillisuusindeksejä, jotka vähentävät ympäristön aiheuttamaa häiriötä. Tämän lisäksi tulisi kehittää aineistojen käsittelyä siten, että hyödynnetään useita aallonpituusalueita ja kasvillisuusindeksejä kasvuston ominaisuuksien ja kuvista mitattujen muuttujien välisen riippuvuuden määrittämiseksi. Sen lisäksi tulisi tutkia kasvilajikohtaisia kuvantamistekniikoita, sillä eri kasveilla on erilaiset heijastusarvot.The number of drones has increased in both the private and corporate sectors. There is also an interest in the use of drones in agriculture since by using them the large fields can be monitored easily. Automatic flight systems of drones are simple to use. More accurate overview of the field can be got by utilizing the drones than by making observations from the side of the field. With aerial photographs the measures for the field can be planned further. For example, based on the photos pesticide spraying or fertilize spreading can be planned for the field. Drones can also be used to estimate crop biomasses. With drones the development of the crops is possible to observe as a timeseries during the growing season. The aim of this study was to explore the use of multispectral images and 3D models in crop monitoring. Crop leaf area index (LAI), biomass and chlorophyll content were measured. There were 8 different plants/fertilization levels in this study. In this study, a multispectral camera and a RGB-camera were used to estimate crops features. With a multispectral camera the reflectance values of the vegetation, which described how much of the incoming sun radiation was reflected back from the vegetation, were able to determine. The multispectral camera had five spectral bands (blue, green, red, red edge and NIR). Based on these bands NDVI vegetation index was calculated. The reflectance values and vegetation indices were compared to the dry matter mass, LAI, and chlorophyll content determinations of the vegetation. From the images of the RGB-camera 3D-models were created to calculate crop volumes. Calculated volumes were compared to crop dry matter mass and LAI measurements. Linear regression analysis was used to examine the relationship between the variables calculated from the images and the parameters determined from the crops on the field. According to these results, the variables determined from the multispectral images explained the dry matter mass and leaf area index of the crop slightly less than the 3D-models determined from the RGB images. The strongest determined dependence of the data recorded by the multispectral camera was between the faba bean LAI and NDVI (R2 = 0,85). The relationship between the reflection/index data of multispectral camera and crop parameter was weak: average coefficient of determination for dry matter mass of the crop was 0.15, for chlorophyll content 0.14, and for LAI 0,21. The highest coefficient of determination for 3D model of crop volume was between the dry matter mass of oats (R2 = 0.91). The mean coefficient of dependence was 0.69 for the relationship between the plant dry matter masses and 3D model volumes. The mean coefficient of determination for the relationship between the leaf area index of plants and the 3D model volumes was 0.57. Based on these results, from the multispectral camera data, the NDVI index was best suited to determine the crops dry matter mass, leaf area index, and chlorophyll content. However, there were differences in the dependencies between different spectral bands/NDVI index and plant properties determined from different crops. 3D models produced stronger dependences for estimating crop dry matter mass and leaf area index than the quantities determined from multispectral images. Analyzing the data with more sophisticated calculation methods utilizing the values of several spectral bands and the indices in the same time would probably have been a more efficient method to analyzing the data than the current used linear regression used in this study. Removing errors, caused by external factors, from multispectral images was found to be very difficult. Especially reflectance values of dry soil differed clearly from vegetations values. Further studies are needed to develop vegetation indices that can reduce errors caused by external factors. In addition, data processing of images should be developed to utilize multiple spectral bands and vegetation indices to determine the relationship between crop characteristics and variables measured from images. In addition, different plant species imaging techniques should be investigated, as different plants have different reflection values

Detecting Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imagery

The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcmax), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcmax and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcmax, pigments, CBC, and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.This research was supported by grant: ITS2017-095: Design and Implementation of control strategies for Xylella fastidiosa, Project 5. Government of the Balearic Islands, Spain. Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through gran agreement XF-ACTORS (727987).Peer reviewe

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400⁻1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R2LAI = 0.79, RMSELAI [m2m−2] = 0.18, R2CHL = 0.77, RMSECHL [µg cm−2] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R2yield = 0.88, RMSEyield [dt ha−1] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield

Uumanned Aerial Vehicle Data Analysis For High-throughput Plant Phenotyping

The continuing population is placing unprecedented demands on worldwide crop yield production and quality. Improving genomic selection for breeding process is one essential aspect for solving this dilemma. Benefitted from the advances in high-throughput genotyping, researchers already gained better understanding of genetic traits. However, given the comparatively lower efficiency in current phenotyping technique, the significance of phenotypic traits has still not fully exploited in genomic selection. Therefore, improving HTPP efficiency has become an urgent task for researchers. As one of the platforms utilized for collecting HTPP data, unmanned aerial vehicle (UAV) allows high quality data to be collected within short time and by less labor. There are currently many options for customized UAV system on market; however, data analysis efficiency is still one limitation for the fully implementation of HTPP. To this end, the focus of this program was data analysis of UAV acquired data. The specific objectives were two-fold, one was to investigate statistical correlations between UAV derived phenotypic traits and manually measured sorghum biomass, nitrogen and chlorophyll content. Another was to conduct variable selection on the phenotypic parameters calculated from UAV derived vegetation index (VI) and plant height maps, aiming to find out the principal parameters that contribute most in explaining winter wheat grain yield. Corresponding, two studies were carried out. Good correlations between UAV-derived VI/plant height and sorghum biomass/nitrogen/chlorophyll in the first study suggested that UAV-based HTPP has great potential in facilitating genetic improvement. For the second study, variable selection results from the single-year data showed that plant height related parameters, especially from later season, contributed more in explaining grain yield. Advisor: Yeyin Sh

Assessment of maize crop health and water stress based on multispectral and thermal infrared unmanned aerial vehicle phenotyping in smallholder farms.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF.No submissions form available