Artificial Neural Network-based Model for Predicting Moisture Content in Rice Using UAV Remote Sensing Data

( Tapash Kumar Sarkar ),( Chan-seok Ryu ),( Jeong-gyun Kang ),( Ye-seong Kang ),( Sae-rom Jun ),( Si-hyeong Jang ),( Jun-woo Park ),( Hye-young Song ) 대한원격탐사학회 2018 大韓遠隔探査學會誌 Vol.34 No.4

The percentage of moisture content in rice before harvest is crucial to reduce the economic loss in terms of yield, quality and drying cost. This paper discusses the application of artificial neural network (ANN) in developing a reliable prediction model using the low altitude fixed-wing unmanned air vehicle (UAV) based reflectance value of green, red, and NIR and statistical moisture content data. A comparison between the actual statistical data and the predicted data was performed to evaluate the performance of the model. The correlation coefficient (R) is 0.862 and the mean absolute percentage error (MAPE) is 0.914% indicate a very good accuracy of the model to predict the moisture content in rice before harvest. The model predicted values are matched well with the measured values (R² = 0.743, and Nash-Sutcliffe Efficiency = 0.730). The model results are very promising and show the reliable potential to predict moisture content with the error of prediction less than 7%. This model might be potentially helpful for the rice production system in the field of precision agriculture (PA).

Integrating UAV Remote Sensing with GIS for Predicting Rice Grain Protein

( Tapash Kumar Sarkar ),( Chan-seok Ryu ),( Ye-seong Kang ),( Seong-heon Kim ),( Sae-rom Jeon ),( Si-hyeong Jang ),( Jun-woo Park ),( Suk-gu Kim ),( Hyun-jin Kim ) 한국농업기계학회 2018 바이오시스템공학 Vol.43 No.2

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with R² (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 (R²) and 0.169% (RMSE) for cloud-free samples and 0.491 (R²) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed R² = 0.553 and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as R² and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

Prediction of Rice Grain Protein with Canopy Spectral Reflectance: Multivariate, Ann and GIS Approach

( Tapash Sarkar ),( Chan-seok Ryu ),( Si-hyeong Jang ),( Sae-rom Jun ),( Ye-seong Kang ),( Jun-woo Park ),( Hye-young Song ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1

Background: Application of remote sensing and GIS has great potential in crop monitoring and retrospectively can set the strategies and management practices as to maximize yield and grain quality. UAV remote sensing was applied to test various vegetation indices and make a prediction model of grain protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Red, Green, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy NIR. Results: ANN showed the precision and accuracy as high as 0.797 (R²), 0.791 (NSE) and 0.164% (RMSE) considering all 54 samples followed by PR, SLR, and PLSR as ANN has the strong advantages to fit the nonlinear problem when an activation function is used in the hidden layer compared to other models established for this study. PLSR calibration models showed almost similar precision and accuracy with PR as 0.663 (R²) and 0.169% (RMSE) for cloud-free samples and 0.491 (R²) and 0.217% (RMSE) for cloud-shadowed samples but, the validation models performed very unsatisfyingly which incurred the models an overfitting. Vegetation index NDVI performed better than other indices calculated from spectral bands of NIR, RE and RGB sensors. For the cloud-free samples, the SLR models showed the precision and accuracy as maximum as the R² = 0.553 and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as R² and 0.225% as RMSE respectively. Conclusion: The result showed that there is a significant correlation between spectral bands and grain protein content. The best fit model produced by ANN showed more promising potential for the protein content monitoring and found the highest precision and accuracy.

Predicting Grain Protein Content of Rice using Remote Sensing Technology

( Tapash Kumar Sarkar ),( Chan-seok Ryu ),( Jeong-gyun Kang ),( Ye-seong Kang ),( Seon-hgeon Kim ),( Sae-rom Jun ),( Won-jun Kim ),( Suk-ku Kim ),( Hyun-jin Kim ) 한국농업기계학회 2016 한국농업기계학회 학술발표논문집 Vol.21 No.2

The ability to estimate and map grain protein content before harvest using remote sensing can help crop growers to set the strategies of harvest and management practices. The objective of the study was to make a prediction model of grain protein content of rice using remote sensing and to test various vegetation indices (normalization) on remote sensing imagery to identify the best vegetation indices for predicting the quality index. Image acquisition was carried out using NIR and RE camera mounted on drone (eBee, senseFly Ltd., Switzerland) on September 11, 2016. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content then dehulled and measured the protein and amylose content of the brown rice by grain analyzer (FOSS Infratech^TM 1241 Analyzer, Hoganas, Sweden). Hereafter, each sample was milled to 92% milling yield by a polishing machine and measured the protein and amylose content again. Total images were differentiated into two groups as cloud free and cloud shadowed. On one hand for the cloud free samples; the vegetation index, NDVI derived from the canopy spectral reflectance at green, red and NIR bands, was significantly correlated to the final grain protein content (R2=0.553, RMSE=0.21, n=14) at 99% significant level. On the other hand, for cloud shadowed samples, the result demonstrated that vegetation index, NDVI was significantly correlated to the final grain protein content(R2=0.479, RMSE=0.225, n=35) at 99.9% significant level. Grain protein content of rice can be possibly forecasted using the canopy or images spectral reflectance of Drone at grain filling stage. These models might not be able to directly apply to different ecological conditions, because this study was carried out under the same ecological conditions in the same district. However, use of remote sensing data to predict quality indices such as protein content of grains can be feasible and realized.

Integrating UAV Remote Sensing with GIS for Predicting Rice Grain Protein

Sarkar, Tapash Kumar,Ryu, Chan-Seok,Kang, Ye-Seong,Kim, Seong-Heon,Jeon, Sae-Rom,Jang, Si-Hyeong,Park, Jun-Woo,Kim, Suk-Gu,Kim, Hyun-Jin Korean Society for Agricultural Machinery 2018 바이오시스템공학 Vol.43 No.2

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

Ensemble of Machine Learning Algorithms for Rice Grain Yield Prediction Using UAV-Based Remote Sensing

Sarkar Tapash Kumar,Roy Dilip Kumar,강예성,전새롬,박준우,류찬석 한국농업기계학회 2024 바이오시스템공학 Vol.49 No.1

Purpose Accurately estimating rice yield before harvesting is crucial for eff ective crop management, food trade assessment, and national food policy planning to ensure food security. Remotely sensed spectral information such as vegetation index (VI)-based approaches for yield prediction are adequate during mid-stage growth but not during ripening due to leaf senescence, canopy coverage, panicle abundance, and other factors. To fi ll this research gap, this study aims to predict rice yield during ripening stage using an ensemble of machine learning (ML) algorithms. Methods A fi xed-wing unmanned aerial vehicle (UAV) was employed to acquire spectral features from red-green-blue, nearinfrared, and red-edge images. In this study, we utilized state-of-the-art ML-based algorithms, such long short term memory (LSTM), bi-directional LSTM (Bi-LSTM), Gaussian process regression (GPR), fuzzy inference system (FIS), adaptive neuro FIS (ANFIS), M5 model tree (M5 Tree), support vector regression (SVR), random forest (RF), and the powerful ensemble techniques based on Bayesian model averaging (BMA), and simple averaging (SA) to aid in improving rice yield prediction more precisely at the ripening stage. Results The fi ndings demonstrate that the ensemble model based on BMA excelled all other models on all evaluation criteria. BMA accomplished the most accurate yield prediction with correlation coeffi cient, root mean squared error (RMSE), normalized RMSE, mean absolute error, median absolute deviation, index of agreement, and a-10 values of 0.958, 0.187 t ha −1 , 0.031, 0.158 t ha −1 , 0.088 t ha −1 , 0.957, and 1.00, respectively. Conclusion Employing a combination of ML algorithms for predicting rice grain yield using UAV-based remote sensing proves to be a powerful and eff ective approach. The ensemble method improves forecast accuracy, mitigates individual algorithm limitations, and produces trustworthy outcomes for smart agricultural decisions by integrating the strengths of multiple algorithms. This comprehensive technique has the potential to adapt rice yield estimation and contribute to sustainable food production systems.

Artificial Neural Network-based Model for Predicting Moisture Content in Rice Using UAV Remote Sensing Data

Sarkar, Tapash Kumar,Ryu, Chan-Seok,Kang, Jeong-Gyun,Kang, Ye-Seong,Jun, Sae-Rom,Jang, Si-Hyeong,Park, Jun-Woo,Song, Hye-Young The Korean Society of Remote Sensing 2018 大韓遠隔探査學會誌 Vol.34 No.4

The percentage of moisture content in rice before harvest is crucial to reduce the economic loss in terms of yield, quality and drying cost. This paper discusses the application of artificial neural network (ANN) in developing a reliable prediction model using the low altitude fixed-wing unmanned air vehicle (UAV) based reflectance value of green, red, and NIR and statistical moisture content data. A comparison between the actual statistical data and the predicted data was performed to evaluate the performance of the model. The correlation coefficient (R) is 0.862 and the mean absolute percentage error (MAPE) is 0.914% indicate a very good accuracy of the model to predict the moisture content in rice before harvest. The model predicted values are matched well with the measured values($R^2=0.743$, and Nash-Sutcliffe Efficiency = 0.730). The model results are very promising and show the reliable potential to predict moisture content with the error of prediction less than 7%. This model might be potentially helpful for the rice production system in the field of precision agriculture (PA).

Silver‑incorporated hydroxyapatite–albumin microspheres with bactericidal effects

Saswati Mishra,Tapash R. Rautray 한국세라믹학회 2020 한국세라믹학회지 Vol.57 No.2

Bacterial adhesion on HA-coated implant is a serious problem that often leads to bone infection such as osteomyelitis on implant sites. Gram-positive bacteria such as Staphylococcus aureus are responsible for such infection-forming biofilm on implant surfaces. Therefore, inclusion of antimicrobial agents especially inorganic ions such as silver has gained vogue due to their strong antibacterial effects. This study reports the fabrication of porous Ag–albumin–HA microspheres by waterin- oil emulsion technique. Varied proportions of porogen with HA ensured enhanced pores in the fabricated microsphere. Further, SEM analysis revealed the presence of microspheres in the range ~ 50–900 μm with interconnected pores ranging between ~ 10 and 50 μm. Antibacterial efficacy of the Ag–HA hybrid disrupting the biofilm was observed. The as-formed microspheres having interconnected pore channels are envisaged to show vascularization as well as fulfilling goals of ideal bone filler ensuring biocompatibility and reducing long-term cytotoxicity at implant site.

Spatio-temporal trend and change point detection of winter temperature of North Bengal, India

Jayanta Das,Tapash Mandal,Piu Saha 대한공간정보학회 2019 Spatial Information Research Vol.27 No.4

The trend of temperature and homogeneity are the most significant issue for climate change allied research. This research aims to identify the long-term trend and change point detection of winter maximum (tmax), minimum (tmin) and average (tmean) temperature of six meteorological stations of North Bengal, India using 102 years’ time series data (1915–2016). To detect the monotonic trend and the rate of change, non-parametric Mann–Kendall (MK) test and Sen’s slope estimator were used. Homogeneity of winter temperature was studied using Buishand’s range test (B test) and Pettit’s test (P test). From the results, it was observed that most of the stations were showed significant (P\0.05) warming trend in winter season. The rate of increasing was highest at station English Bazar in the month of December. On the other hand, significant changed of winter tmax and tmean occurred in around 1959 and 1952 respectively, while for tmin it was quite late, occurred in the year 1988. The populations of North Bengal who are dependent on temperature- related primary economic activities are getting benefitted from this study. In addition, these analyses will be helpful for policymakers and scientist to focus on microlevel planning and sustainable Rabi crops management in this region.

Strontium-substituted biphasic calcium phosphate scaffold for orthopedic applications

Mohapatra Bijayinee,Rautray Tapash R. 한국세라믹학회 2020 한국세라믹학회지 Vol.57 No.4

Strontium ion-substituted calcium phosphate-based ceramic scaff olds enhance osteogenesis. The objective of the present study was to optimize the strontium hydroxyapatite (Sr-HA) and beta tricalcium phosphate (β-TCP) ratio in the fabricated scaff olds to enhance their mechanical strength and bioactivity with controlled biodegradability. Porous polyurethane sponge scaff olds containing Sr-HA and β-TCP in varying concentration were developed by dipping 3D sponge pieces into a slurry containing 10% gelatin, proper concentration of biphasic calcium phosphate (BCP) and 3% poly vinyl alcohol. Four diff erent samples [Sr-BCP, Sr-BCP (20/80), Sr-BCP (30/70), Sr-BCP (40/60)] were prepared and were characterized for biodegradability, water uptake capability and cytotoxicity using various techniques. Pure, crystalline, cytocompatible Sr-BCP Scaff olds possessing both micropores and macropores with porosity greater than 80% and water uptake capability above 100% were obtained. Thus, the eff ective substitution of Sr-HA and β-TCP in varying proportion makes the composite scaff old a distinctive material of bone tissue engineering.