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.