Plant diseases are major causes of production losses and may have a significant impact on the agricultural sector. Detecting pests as early as possible can help increase crop yields and production efficiency. Several robotic monitoring systems have been developed allowing to collect data and provide a greater understanding of environmental processes. An agricultural robot can enable accurate timely detection of pests, by traversing the field autonomously and monitoring the entire cropped area within a field. However, in many cases it is impossible to sample all plants due to resource limitations. In this thesis, the development and evaluation of several sampling algorithms are presented to address the challenge of an agriculture-monitoring ground robot designed to locate insects in an agricultural field, where complete sampling of all the plants is infeasible. Two situations were investigated in simulation models that were specially developed as part of this thesis: where no a-priori information on the insects is available and where prior information on the insects distributions within the field is known. For the first situation, seven algorithms were tested, each utilizing an approach to sample the field without prior knowledge of it. For the second situation, we present the development and evaluation of a dynamic sampling algorithm which utilizes real-time information to prioritize sampling at suspected points, locate hot spots and adapt sampling plans accordingly. The algorithm's performance was compared to two existing algorithms using Tetranychidae insect data from previous research. Analyses revealed that the dynamic algorithm outperformed the others.

翻译：植物病害是导致产量损失的主要原因之一，可能对农业部门产生重大影响。尽早检测虫害有助于提高作物产量和生产效率。目前已开发出多种机器人监测系统，用于收集数据并加深对环境过程的理解。农业机器人能够自主穿越农田并监测整个种植区域，从而实现精准及时的虫害检测。然而，由于资源限制，在许多情况下无法对所有植株进行采样。本文针对用于检测农业田间昆虫的地面监测机器人所面临的挑战——即难以对所有植株进行完全采样——提出了多种采样算法的开发与评估。研究基于本论文专门开发的仿真模型，探讨了两种情况：无昆虫先验信息和无昆虫分布先验信息。对于第一种情况，测试了七种算法，每种算法都采用不依赖先验知识的方法对田地进行采样。对于第二种情况，本文提出并评估了一种动态采样算法，该算法利用实时信息优先对可疑点进行采样，定位热点区域并相应调整采样计划。将该算法的性能与两种现有算法进行比较，使用了既往研究中收集的叶螨科昆虫数据。分析结果表明，动态算法优于其他算法。