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Site-specific seeding using proximal and remote sensors data fusion
No full textTraditional agronomic management applies a uniform rate seeding (URS) density throughout the field assuming that the entire field is equally productive. This is a misconception since most agricultural soils are spatially and temporally heterogeneous, and hence a unique seed rate never be optimal throughout a field. Consequently, the URS allows to grow an improper number of plant populations in differently fertile zones in a field, which raises inter-crop competitions and reduces crop yield and thus production profit. Planting density also is linked with the application rate of other farming inputs, and therefore, the non-optimal seeding rates may increase production costs associated with fertilizer and pesticide application, and affect the soil-water environment. Ideally, in-field heterogeneity should be managed properly by implementing precision farming technology. Site-specific seeding (SSS) is such a precision agricultural operation that can ensure an optimized application of seeding rate for each fertility zone by considering the in-field soil variations.
The aim of this study is to develop an optimized SSS approach based on the fusion of multiple soil and crop quality indicators estimated with state-of-the-art proximal and remote sensing technologies for maximizing crop yield and production margin. In the literature, there are two methods for implementing SSS i.e., map-based and sensor-based. The map-based SSS system allows adjusting seeding rates depending on the management zone (MZ) map delineated with reference soil and/or crop information. The sensor-based system adjusts seed rates based on an on-line measured soil fertility index (SFI), which is then used as input for real-time calculation and implementation of the recommended seeding rate. The SSS has been often practiced using map-based technology, frequently relying on yield or soil electrical conductivity (EC) data while ignoring many other important soil-related information. Hence, this study intended to move forward the map-based SSS using MZ map delineated with the fusion of multiple soil and crop data layers. It also focuses on identifying the key yield-limiting factors and thus proposing a set of proxies in the delineation of the MZ map accurately as well as in the development of an effective SFI. As the traditional laboratory-based analytical methods are slow and limited to low-resolution estimation of MZ proxies, this study intends to propose the best sensor and/or a combination of sensing technologies for estimating the key yield-limiting factors for delineating the MZ map. Moreover, the early works did not follow a systematic approach to optimize seed rate and they assigned the seed rate per MZ arbitrarily. Therefore, this research proposes an approach of SSS rate optimization. Most importantly, to date no research about sensor-based SSS is available, and hence this thesis will develop, for the first time, a sensor-based SSS technology. As a very limited studies evaluated SSS, the agronomic and economic benefits of SSS have not been largely explored. They showed inconsistent economic profits, which hinder the SSS adoption by end-users.
Therefore, this study also focuses on evaluating the agronomic and economic performance of SSS in comparison with the URS for two major crops i.e., maize and potato.
Multiple field experiments were conducted for evaluating the agronomic and economic benefits of map-based SSS for potato and maize in two cropping seasons. An on-line visible and near-infrared spectroscopy (vis-NIRS), electromagnetic induction (EMI) sensor, and Sentinel-2 image data were used for delineating three types of MZ maps, which are used for designing SSS treatments: 1)SSS based on apparent electrical conductivity (ECa) measured with an EMI sensor(EMI-SSS), 2) fusion of on-line vis-NIRS estimated soil properties with normalised difference vegetation index (NDVI) retrieved from Sentinel-2 based SSS (visNIRSen-SSS), and 3) another fusion-based SSS integrating data from EMI, on-line vis-NIRS, and Sentinel-2 retrieved NDVI data (EvisNIRSen-SSS). Five treatments of seed rate distributed over the MZ maps were tested to compare the performance of the “Kings” and “Robin Hood” methods for recommending the seed-to-seed spacing for both seed and consumption potatoes and seed rates for maize. The “Kings” method recommends the highest seeding rate for the highest fertile MZ and vice-versa while the “Robin Hood” follows the opposite principle by feeding most to the least fertile MZ. After all, the agro-economic performance of these five treatments was compared with those of URS. Results showed that the “Kings” approach outperformed the “Robin Hood” method both for potatoes and maize production. The SSS resulted in higher yield and gross margin by up to 597 € ha-1 in contrast to URS. The enhanced gross margin mainly emerged from increased crop yield instead of savings on the seed costs. The ECa-based SSS and the visNIRSen-SSS performed equally with a mild variation over the test sites. Since the EvisNIRSen-SSS revealed the highest gross margin for potato, the fusion of data obtained from EMI, vis-NIRS and Sentinel-2 seemed to be the most effective approach for MZ delineation in designing and implementing of the map-based SSS. Higher improvement in yield and the gross margin was observed in potato (380 to 597 € ha-1) than maize production (93 € ha-1). The highest profitability of SSS of up to 56.50 % was observed in the field with the lowest productivity. Despite increasing, crop yield and gross margin by SSS were statistically insignificant except in one test field with consumption potatoes.
Since crop yields frequently illustrated high-positive correlations with soil pH, OC, P, K, Mg and MC, these were identified as key fertility indicators in developing the SFI that was used as input for the sensor-based SSS. They were also considered as the key yield-limiting factors and used as proxies for MZ delineation needed for map-based SSS. The on-line vis-NIRS sensor can estimate all these MZ proxies accurately with high-spatial resolution, hence is highly recommended for realizing the two methods of SSS.
A fully automated sensor-based SSS system was developed and validated. It consists of hardware and software integrating an on-line vis-NIRS soil sensor, an on-line SFI prediction model, a seed rate calculation algorithm, and a variable rate planter machine. While the on-line soil sensor was set on the front end of a tractor, the maize planter actuates the seed rate according to the SFI measured by the on-line sensor. The system was tested in one field with silage maize when it revealed its potential for increasing yield by 1.4 t ha-1 and thus the gross margin by 91 € ha-1, compared to the URS. The study proved that the vis-NIRS sensor was the ideal sensing technology to assess soil fertility and MC for accurate calculations of seeding rate recommendations.
In conclusion, SSS is found to be a promising precision agriculture solution to increase crop productivity and thus the economic margin by scientifically managing in-field heterogeneity through optimizing the input seeding rates according to the yield potential of different zones of a field. To succeed in SSS application, soil pH, OC, P, K, Mg and MC should be included in the MZ delineation for map-based SSS and in SFI determination for sensor-based application. This will allow for a correct seed rate calculation, which should then be implemented according to the “Kings” method (e.g., feeding the rich). It is highly suggested in future works to include soil clay and elevation data as MZ proxies and in the SFI derivation. The study shows interesting economic and agronomic benefits that could be harvested when SSS is adopted by farmers. It is therefore recommended to adopt SSS in the commercial farming system. This study has become a reference and thus opened a window for further development and implementation of SSS for other crops
Removal of external influences from on-line Vis-NIR spectral data for predicting soil organic carbon : comparison of spectra transfer vs orthogonalization methods
No full textSoil organic carbon (SOC) stock is strongly linked with soil ecosystem functions through its capacity to hold soil water, stabilize soil structure, supply nutrients, and support soil microbes. Accordingly, accurate estimation of SOC is crucial for key applications in agriculture and the environment. On-line visible and near-infrared (Vis-NIR) spectroscopy has made SOC estimation cost-effective and time-efficient compared to the traditional wet chemical analysis. However, external factors including soil moisture content (MC), variation in sensor-to-soil distance, ambient light and temperature are known to negatively affect the prediction accuracy of Vis-NIR sensor. Therefore, the present study has compared the performances of four correction algorithms to remove the MC effect [i.e., direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), and orthogonal signal correction, OSC)] against non-corrected (NC) spectral models developed with partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and M5Rules regression techniques. The DS and PDS algorithms transfer spectra from a reference to another reference system while EPO and OSC correct the spectral part that is orthogonal to the property of interest, say SOC in this study. An on-line soil sensing platform coupled with a Vis-NIR spectrophotometer (305–1700 nm) (CompactSpec by Tec5 Technology, Germany) was used to scan twelve agricultural fields in Belgium and France. A total of 372 soil samples collected during the on-line measurement were divided into a calibration (260 samples) and a prediction (112 samples) dataset. The latter set together with identical laboratory-measured 112 dry soil spectra formed a transfer dataset to develop EPO, DS and PDS correction matrices. Results showed that models after EPO and OSC corrections resulted in improved measurement accuracy [coefficient of determination (R2) = 0.60 to 0.82, and ratio of prediction to deviation (RPD) = 1.59 to 2.36)], compared to the NC models (R2 = 0.58 to 0.73, RPD = 1.56 to 1.93).The PDS showed almost similar accuracy (R2 = 0.58 to 0.67, RPD = 1.56 to 1.74) to NC models, while DS provided deteriorated prediction accuracy (R2 = -0.10 to 0.26, RPD = 0.96 to 1.17). The EPO and OSC models provided better prediction accuracy than that of the PDS corrected models revealing that the orthogonalization outperformed spectral transfer. The OSC-M5Rules (R2 = 0.82, RPD = 2.36) obtained the highest accuracy followed by EPO-M5Rules (R2 = 0.74, RPD = 1.99) and NC-M5Rules (R2 = 0.73, RPD = 1.93), which outperformed all PLSR, RF and SVM models. Therefore, it is recommended that on-line Vis-NIR spectra should be corrected for orthogonality with OSC algorithm instead of spectra transferring before building a machine learning model for prediction of SOC accurately
Spectra transfer based learning for predicting and classifying soil texture with short-ranged Vis-NIRS sensor
No full textClassifying soil texture is critical to investigate soil processes and functions influencing agronomic decisions and environmental stewardship. Despite the multiple benefits together with accurate estimation potential of visible and near-infrared reflectance spectroscopy (Vis-NIRS) over the traditional method of texture analysis, the pre-diction accuracy of the Vis-NIRS decreases due to the negative effect of soil moisture content (MC) on soil spectra. This study evaluated the performance of piecewise direct standardization (PDS) and direct standardi-zation (DS) for eliminating the soil MC influence from fresh (non-processed wet) soil spectra to improve the accuracy of soil texture classification using a short-ranged (305-1700 nm) Vis-NIRS (Tec5 Technology, Ger-many). A set of 96 composite soil samples was collected from 18 fields, and the second set of 294 non-composite samples was collected from 10 of these 18 fields. All soil samples were scanned using a Vis-NIRS sensor after drying, grinding, and sieving, while the non-composite samples were additionally scanned in wet conditions. After analysing the composite samples for percent sand, silt, and clay, partial least squares regression (PLSR) models were calibrated (72 samples in calibration set) and validated (23 samples in validation set). Then, the PLSR model was used to predict the textural fractions for the wet, air-dried, DS and PDS transferred spectra of non-composite soil samples followed by their texture classification. Validation results indicated that the Vis-NIRS sensor predicted sand, silt, and clay fractions with excellent accuracies [coefficient of determination (R2) = 0.88-0.93; residual prediction deviation (RPD) = 2.91-3.74; ratio of the performance to interquartile range (RPIQ) = 2.12-5.02; root means square error (RMSE) = 3.62-8.06 %). Spectra transferred by the PDS and DS improved the texture classification accuracy up to 70 % and 90 %, respectively, while the wet soil spectra misclassified most fields (accuracy = 20 %). Unlike PDS, the DS predicted particles' distribution seemed approximately similar to laboratory observations. Therefore, it was concluded that transferring the wet Vis-NIRS spectra by the DS algorithm can provide a rapid and accurate prediction of particle fractions and classification of soil texture classes with minimized effort, time, and cost
An automated system of soil sensor-based site-specific seeding for silage maize : a proof of concept
No full textDespite recent studies of map-based site-specific seeding (SSS) revealing improved agronomic and economic outcomes over uniform rate seeding (URS), to our best knowledge no visible and near infrared spectroscopy (visNIRS) sensor-based SSS studies exist to date. This study aimed to develop and evaluate an automated sensorbased SSS technology for silage maize (Zea mays L.) production. An on-line visible and near-infrared reflectance spectroscopy (vis-NIRS) sensor was installed in front of a tractor to provide real-time input data to control the seed rate using a precision seeding machine mounted at the back of the tractor. A LabVIEW-based software was developed and used to predict soil fertility index using on-line vis-NIR spectra, which was then used to calculate the seed rate and transfer it to the controller of the seeding machine. The agronomic and economic benefits of SSS were compared with URS under a one-site-year experiment. Results showed that the proposed sensor-based SSS technology was 87.5% efficient in controlling the desired seed rates, according to the soil fertility status. In parallel with the observed spatial similarity between predicted soil fertility index and actual seed rates, a strong linear association (R2 = 0.80) was also achieved. As a result, SSS improved silage yield by 1.4 t ha-1 (4.4%), while sowing a lower seed rate (86.4 kSeeds ha-1) than the URS (90 kSeeds ha-1). This improved gross margin by 91 euro ha-1, of which only 7 euro ha-1 was attributed to savings on seed cost. The proposed sensorbased SSS technology was technically sound and thus transforms within-field fertility variations into agroeconomic benefits effectively
Development of a soil fertility index using on-line Vis-NIR spectroscopy
No full textSoil fertility index (SFI) is commonly used for soil fertility assessment, which is critical for managing in-field variabilities and maximizing crop production with minimum environmental impacts. However, the majority of earlier SFIs were laboratory-based soil analyses. This study developed a novel SFI using on-line collected visible and near-infrared (vis-NIR) spectra. Six agricultural fields were scanned using an on-line vis-NIR sensor (CompactSpec, Tec5 Technology, Germany), when 139 soil samples were collected and analyzed for soil pH, organic carbon, available- phosphorous (P), potassium, magnesium (Mg), calcium, sodium, moisture content (MC) and cation exchange capacity. A minimum dataset was developed comprising the fertility attributes that showed pairwise correlation (r) smaller than 0.75. This was followed by a principal component analysis to calculate the weight factor of each parameter to be used in the SFI formulation using a double-weighted additive function. The data matrix consisting of the SFI and soil spectra was divided into calibration (70 %) and prediction (30 %) datasets. The former set was subjected to a partial least squares regression to calibrate SFI model, whose accuracy was validated using the prediction set. Results showed that the derived SFI was moderately to highly correlated with P (r = 0.57), pH (r = 0.75), and Mg (r = 0.74) and weakly correlated with MC (r = 0.26). The on- line vis-NIR sensor predicted SFI with very good accuracy [coefficient of determination (R2) = 0.75 and ratio of prediction to deviation (RPD) = 2.01]. Therefore, it is concluded that the vis-NIR can accurately predict SFI directly from on-line scanned soil spectra, which can effectively assess soil fertility and manage in-field variability
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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