Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques

Abdollahpour, Shamsollah; Kosari-Moghaddam, Armaghan; Bannayan, Mohammad

doi:10.1016/j.inpa.2020.01.003

Cited by 23 publications

(10 citation statements)

References 34 publications

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“…Some advances have been developed in methods based on the dielectric behavior of grains [55] and several developments have been made to measure moisture content using low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as the wireless sensor network (WSN) and radio frequency identification (RFID) [56].…”

Section: New Techniques For Determining Moisture In Grainsmentioning

confidence: 99%

“…The results show that the Random Forest method with an input feature (RSSI/WSN) provides the highest accuracy of 87% compared to the other four models. A combination of one of the other models with the two input features (RSSI/WSN and RSSI/RFID) using RSSI with the wireless WSN sensor network and RFDI radio frequency identification provides an accuracy of more than 98% [56].…”

Section: Rf-based Moisture Content and Artificial Neural Networkmentioning

confidence: 99%

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New Sensing Technologies for Grain Moisture

et al. 2022

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In this review, we present a description of conventional technologies and new advances for the estimation and sense of moisture content in grains. The operating principles, accuracies and response times are described. The review considers an exhaustive search of scientific developments and patent registrations. It was concluded that most of the new developments correspond to methods of which the measurement principles are based on the analysis of the electrical characteristics of the grains. In addition, new methods of image analysis have been implemented that provide measurements with reduced response times and with precisions of utility for its application in the agro-industrial field. In addition to this, wireless communication technologies have been implemented that allow the implementation of moisture measurement methods in moving grains within processing chains.

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Section: New Techniques For Determining Moisture In Grainsmentioning

confidence: 99%

Section: Rf-based Moisture Content and Artificial Neural Networkmentioning

confidence: 99%

New Sensing Technologies for Grain Moisture

et al. 2022

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“…In a study conducted by Abdollahpour et al [58], they reported that the ANN model has a greater ability than the SVR model to predict the moisture content of wheat at harvest time. In another study, Taheri et al [59] modeled the moisture content for drying the lentil seeds in a microwave fluidized bed dryer using the SVR and ANN.…”

Section: Comparison Of Ann Anfis and Svrmentioning

confidence: 99%

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Sharabiani

Kaveh²,

Taghinezhad

et al. 2022

Applied Sciences

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Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

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“…The control treatment (0kg NPSZnB ha -1 ), was statistically equal to (100 kg NPSZnB ha -1 ) and had the lowest grain moisture content (9.43%) (Table 19). The variation between treatments on moisture content would be the environmental condition at harvesting [55].…”

Section: Grain Moisture Content (%)mentioning

confidence: 99%

“…Moisture level of grains needed to be adjusted to low enough to inactivate the enzymes involved in seed germination as well as to prevent heat damage and the growth of disease causing microorganisms. Wheat harvesting begins when the crop reaches physiological maturity and the moisture content drops to 14%, but this can vary depending on management methods [55]. Along with management measures that might help increase agricultural output, the variability of grain yields is mostly determined by climatic conditions rather than fertilization or varieties [40].…”

Section: Grain Moisture Content (%)mentioning

confidence: 99%

Effect of Blended Npsznb Fertilizer Rates on Yield and Yield Components of Bread Wheat (<i>Triticum aestivum</i> L.) Varieties in Mao-komo, Benshangule Gumuz Regional State

Misgana¹,

Ashagre²,

Debele³

2022

IJAAS

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Bread wheat (Triticum aestivum L.) is the most important cereal crop in the Poaceae (Gramineae) family originated in the Near-Eastern Fertile Crescent in the Middle East. It is the second most influential staple food crop after rice, grown in 89 countries with climates ranging from temperate to tropical but the production did not meet demand. The field experiment was conducted during 2020 cropping season in Mao-Komo District, western Ethiopia to evaluate the effects of blended NPSZnB fertilizer on bread wheat varieties. The experiment consisted of five levels of NPSZnB (0, 100, 150, 200, 250 kg ha -1 ) and four bread wheat varieties Kingbird, Danda, Shorima and Ogolcho combined factorially and laid out in Randomized Complete Block Design with three replications. Days to 90% maturity were delayed with increased rates of fertilizer for all varieties. The highest mean plant height (81.4cm), number of grains per spike (52.5), and harvest index (39%), leaf area (30.15cm 2 ), total tillers (6.29), productive tillers (6.1), grain yield (3.54), thousand grain weight (33.83g) and hectoliter weight (81.2) were obtained at 200 kg ha -1 NPSZnB with Kingbird variety. Moreover, the highest straw yield (7.53t ha -1 ) and spike length (8.2 cm) were recorded at 200 kg NPSZnB ha -1 with Danda variety. The highest biomass (9.97 tha -1 ) and hectoliter weight (81.2) were obtained at 250 kg ha -1 NPSZnB with Ogolcho and Kingbird varieties, respectively. The maximum net benefit (85047ETB ha -1 ) was obtained due to use of 200 kg ha -1 NPSZnB with Kingbird bread wheat variety. Due to soil acidity of the study area, use of integrated soil fertility management practices is important to improve the production and productivity of bread wheat.

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Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques

Cited by 23 publications

References 34 publications

New Sensing Technologies for Grain Moisture

New Sensing Technologies for Grain Moisture

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Effect of Blended Npsznb Fertilizer Rates on Yield and Yield Components of Bread Wheat (<i>Triticum aestivum</i> L.) Varieties in Mao-komo, Benshangule Gumuz Regional State

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Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques

Cited by 23 publications

References 34 publications

New Sensing Technologies for Grain Moisture

New Sensing Technologies for Grain Moisture

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Effect of Blended Npsznb Fertilizer Rates on Yield and Yield Components of Bread Wheat (&lt;i&gt;Triticum aestivum&lt;/i&gt; L.) Varieties in Mao-komo, Benshangule Gumuz Regional State

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Effect of Blended Npsznb Fertilizer Rates on Yield and Yield Components of Bread Wheat (<i>Triticum aestivum</i> L.) Varieties in Mao-komo, Benshangule Gumuz Regional State