Study on Time and Space Prediction Model About Rice Yield in Hei Longjiang Province

Computer and Computing Technologies in Agriculture IX - IFIP Advances in Information and Communication Technology ◽

10.1007/978-3-319-48357-3_50 ◽

2016 ◽

pp. 536-545

Author(s):

Guowei Wang ◽

Hongyan Hu ◽

Hao Zhang ◽

Yu Chen

Keyword(s):

Prediction Model ◽

Rice Yield ◽

Time And Space

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Establishment of Rice Quality Prediction Model for Intermittent Drying

Transactions of the ASABE ◽

10.13031/trans.14243 ◽

2021 ◽

Vol 64 (4) ◽

pp. 1355-1363

Author(s):

Qi Song ◽

Xinhua Wei

Keyword(s):

Moisture Content ◽

Prediction Model ◽

Intelligent Control ◽

Evaluation Method ◽

Rice Yield ◽

Drying Process ◽

Rice Quality ◽

Head Rice ◽

Head Rice Yield ◽

Rice Drying

HighlightsThis study explored the feasibility of developing an evaluation method for rice quality.A unified quality scale for different drying cycles facilitates evaluation of rice quality after drying.A head rice yield (HRY) prediction model was established that fit well with the actual HRY.The established HRY prediction model can be used as a performance index for optimization of rice drying.Abstract. Intelligent control of the drying process is important to achieve better rice quality. An effective quality evaluation method is the basis for intelligent control of rice drying. To study the effects of intermittent drying on the quality of paddy rice and explore the feasibility of establishing a quality evaluation method, intermittent drying experiments were conducted with variety Nanjing 9108 (Oryza sativa L.). The paddy samples were dried from an initial moisture content of 23.10% to 14% wet basis (w.b.). The paddy samples were initially dried at 60°C to various moisture contents without tempering. These pre-dried samples were then dried using different drying temperatures to obtain specific moisture content reductions, tempered, and then dried again at 60°C to the final moisture content of 14% w.b. without tempering. After drying, the quality parameters of the paddy samples were measured and analyzed. The R2 values of the head rice yield (HRY) prediction model, chalkiness prediction model, and protein prediction model established in this study were 0.75, 0.44, and 0.26, respectively. The HRY prediction model was shown to accurately predict HRY in the intermittent drying experiments. Within the range of the model parameters, the effectiveness of the HRY prediction model was explored by constant-temperature intermittent drying and variable-temperature intermittent drying. The results showed that if the summation of the predicted changes in HRY is large, then the measured HRY will be large. Therefore, the HRY prediction model can be used as a performance index for rolling optimization of the paddy drying process. Keywords: Head rice yield, Intermittent drying, Prediction model, Rice quality.

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