scholarly journals Prediksi Kadar Salinitas, pH dan C-Organik Tanah Menggunakan Near Infrared Kecamatan Baitussalam Kabupaten Aceh Besar

2020 ◽  
Vol 4 (4) ◽  
pp. 542-551
Author(s):  
Riska Nurul Saputri ◽  
Ichwana Ichwana ◽  
Agus Arip Munawar
Keyword(s):  
Prediction Model ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Least Square ◽  
Organic Soils ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Spectrum Correction

Abstrak. Akuisisi spektrum Near Infrared Reflectance Spectroscopy (NIRS) terkait kualitas dan kondisi tanah telah banyak dilakukan dalam berbagai penelitian. Pada penelitian ini menggunakan model prediksi Partileal Least Squares (PLS) dengan metode koreksi spektrum Mean Normalization (MN), Savitzky-Golay Smoothing, dan kombinasi Mean Normalization (MN) dan Savitzky-Golay Smoothing. Sampel tanah yang digunakan berasal dari Kecamatan Baitussalam Kabupaten Aceh Besar karena dianggap sesuai untuk prediksi kadar salinitas, pH dan C-Organik tanah. Hasil dari penelitian menunjukkan adanya korelasi antara prediksi Near Infrared Reflectance Spectroscopy (NIRS) dengan hasil aktual laboratorium setelah dilakukan pembangunan model prediksi Partileal Least Square (PLS) dan dievaluasi dengan parameter statistika; penggunaan pretreatment Mean Normalization (MN) merupakan metode terbaik atau pilihan, dimana dapat meningkatkan keakuratan hasil prediksi kadar salinitas, pH dan C-Organik tanah.Prediction of Salinity, pH and C-Organic Soils Level Using Near  in Baitussalam Regency, Aceh Besar RegencyAbstract. Near Infrared Reflectance Spectroscopy (NIRS) spectrum acquisition related to soil quality and condition has been carried out in various studies. This study used prediction model Partileal Least Squares (PLS) with the spectrum correction methods used are Mean Normalization (MN), Savitzky-Golay Smoothing, and Combination of Mean Normalization (MN) and Savitzky-Golay Smoothing. The soil samples used were from Baitussalam regency, Aceh Besar regency because they were considered suitable for the prediction of salinity, pH and C-Organic soils. The results of this study showed a correlation between the prediction of Near Infrared Reflectance Spectroscopy (NIRS) with the actual results of the laboratory after the construction of the prediction model Partileal Least Square (PLS) and and evaluated with statistical parameters; the use of pretreatment Mean Normalization (MN) is the best or preferred spectrum correction method, which can improve the accuracy of the predicted results of salinity, pH and C-Organic soil.

scholarly journals Dry Matter Estimation of Standing Corn with Near-Infrared Reflectance Spectroscopy

2021 ◽  
Vol 37 (5) ◽  
pp. 775-781
Author(s):  
Matthew F. Digman ◽  
Jerry H. Cherney ◽  
Debbie J. Cherney
Keyword(s):  
Moisture Content ◽  
Prediction Model ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Whole Plant ◽  
Plant Moisture

HIGHLIGHTSQuadratic relationships were established to relate ear moisture or stover moisture to whole plant moisture, and they explained 90% and 84% of whole plant moisture, respectively. Based on our observations, the moisture content of a corn field can be estimated within +1% w.b. in 19 out of 20 fields by sampling 5-10 plants. The calibration offered by SCiO was successful at predicting oven-dried moisture content based on traditional NIRS metrics of R2 = 0.92, RMSE = 3.6, RPD = 3.2, and RER = 15. However, the 95% prediction bands were +6.9% w.b., which would indicate little utility in estimating ear moisture content. Based on a prediction model that was developed using the data collected for this study, a significant instrument-to-instrument bias was observed, indicating the necessity of including multiple SCiO devices in calibration spectra collection. ABSTRACT. Determining the appropriate time to harvest whole-plant corn is an essential factor driving the successful preservation via anaerobic fermentation (ensiling). The current options for timely on-farm monitoring of corn moisture in the field include selecting a set of representative plants, chopping and drying a subsample, or harvesting a portion of the field using a harvester equipped with an on-board moisture sensing system. Both methods are time-consuming and expensive, limiting their practicality for harvest decision-making. This work’s objective was to develop a practical solution that utilizes the moisture content of the ear to estimate whole-plant moisture. An improvement of this method was also considered that utilized a hand-held near-infrared reflectance spectroscopy (NIRS) device to predict ear moisture in situ. Based on the data collected during this work, a quadratic relationship was developed where ear moisture explained 90% of the variability in whole-plant corn moisture. However, based on our observations, the hand-held NIRS evaluated would have little utility in predicting whole-plant corn moisture with either the calibration developed here or provided by the manufacturer. The manufacturer’s prediction model yielded the best result with an R2 of 0.92, and a ratio of performance to deviation of 3.19. However, the 95% prediction band was +6.85% w.b. Finally, we determined that for a corn field uniform in appearance, sampling five to ten plants is likely to provide a reasonable estimate of field moisture. Keywords: Corn silage, Forage analysis, Harvest timing, Moisture content, NIRS.

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

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