摘要
A study of the soil characteristics, weather conditions, and effect of management skills on the yield of the agricultural crop requires site-specific details, which involves large amount of labor and resources, compared to the traditional whole field based analysis. This thesis discusses the design and implemention of yield monitor for sweetpotatoes grown in heavy clay soil. A data acquisition system is built and image segmentation algorithms are implemented. The system performed with an R2 value of 0.80 in estimating the yield. The other main contribution of this thesis is to investigate the effectiveness of statistical methods and neural networks to correlate image-based size and shape to the grade and weight of the sweetpotatoes. An R2 value of 0.88 and 0.63 are obtained for weight and grade estimations respectively using neural networks. This performance is better compared to statistical methods with an R2 value of 0.84 weight analysis and 0.61 in grade estimation.
摘要译文
的土壤特性，气候条件，以及对农作物的产量管理技能影响的研究需要的位点特异性的细节，这涉及大量的劳力和资源，相对于传统的整场为基础的分析。本文讨论了设计和测产的FPGA实现在粘重土壤中生长sweetpotatoes 。数据采集系统是建立和图像分割算法的实现。用0.80在估计产率R2的值来进行该系统。本文的另一主要贡献是调查统计方法和神经网络的有效性，关联基于图像的尺寸和形状的sweetpotatoes的等级和重量。分别采用神经网络的重量和品位估计得到的0.88和0.63 R2值。这一业绩要好相比，具有重量0.84 0.61分析在等级评定R2值和统计方法。
Gogineni, Swapna. The design and implementation of a yield monitor for sweetpotatoes[D]. US: Mississippi State University, 2002