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首页> 外文会议>Conference on remote sensing and modeling of ecosystems for sustainability >Using Artificial Neural Network and Satellite data to predict Rice yield in Bangladesh
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Using Artificial Neural Network and Satellite data to predict Rice yield in Bangladesh

机译：使用人工神经网络和卫星数据预测孟加拉国的稻米产量

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Rice production in Bangladesh is a crucial part of the national economy and providing about 70 percent of an average citizen's total calorie intake. The demand for rice is constantly rising as the new populations are added in every year in Bangladesh. Due to the increase in population, the cultivation land decreases. In addition, Bangladesh is faced with production constraints such as drought, flooding, salinity, lack of irrigation facilities and lack of modern technology. To maintain self sufficiency in rice, Bangladesh will have to continue to expand rice production by increasing yield at a rate that is at least equal to the population growth until the demand of rice has stabilized. Accurate rice yield prediction is one of the most important challenges in managing supply and demand of rice as well as decision making processes. Artificial Neural Network (ANN) is used to construct a model to predict Aus rice yield in Bangladesh. Advanced Very High Resolution Radiometer (AVHRR)-based remote sensing satellite data vegetation health (VH) indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) are used as input variables and official statistics of Aus rice yield is used as target variable for ANN prediction model. The result obtained with ANN method is encouraging and the error of prediction is less than 10%. Therefore, prediction can play an important role in planning and storing of sufficient rice to face in any future uncertainty.
机译：孟加拉国的稻米产量是国民经济的重要组成部分，提供了大约70％的公民总卡路里的摄入量。随着新的人群在孟加拉国每年加入新的人口，对稻米的需求不断上升。由于人口增加，栽培土地减少。此外，孟加拉国面临生产限制，如干旱，洪水，盐度，缺乏灌溉设施和缺乏现代技术。为了保持稻米的自给自足，孟加拉国将不得不通过增加产量以至少等于人口增长的速度来继续扩大大米产量，直到水稻的需求稳定。准确的水稻产量预测是管理水稻供需以及决策过程中最重要的挑战之一。人工神经网络（ANN）用于构建模型，以预测孟加拉国的净水稻产量。基于高分辨率辐射计（AVHRR）的遥感卫星数据植被健康（VH）指数（植被状况指数（VCI）和温度条件指数（TCI））用作输入变量，使用澳大利亚产量的官方统计数据ANN预测模型的目标变量。用ANN方法获得的结果令人鼓舞，预测误差小于10％。因此，预测可以在任何未来的不确定性中规划和储存足够的大米来发挥重要作用。

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