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Plant diseases are major causes of production losses and may have asignificant impact on the agricultural sector. Detecting pests as early aspossible can help increase crop yields and production efficiency. Severalrobotic monitoring systems have been developed allowing to collect data andprovide a greater understanding of environmental processes. An agriculturalrobot can enable accurate timely detection of pests, by traversing the fieldautonomously and monitoring the entire cropped area within a field. However, inmany cases it is impossible to sample all plants due to resource limitations.In this thesis, the development and evaluation of several sampling algorithmsare presented to address the challenge of an agriculture-monitoring groundrobot designed to locate insects in an agricultural field, where completesampling of all the plants is infeasible. Two situations were investigated insimulation models that were specially developed as part of this thesis: whereno a-priori information on the insects is available and where prior informationon the insects distributions within the field is known. For the firstsituation, seven algorithms were tested, each utilizing an approach to samplethe field without prior knowledge of it. For the second situation, we presentthe development and evaluation of a dynamic sampling algorithm which utilizesreal-time information to prioritize sampling at suspected points, locate hotspots and adapt sampling plans accordingly. The algorithm's performance wascompared to two existing algorithms using Tetranychidae insect data fromprevious research. Analyses revealed that the dynamic algorithm outperformedthe others.

Mobile robots sampling algorithms for monitoring of insects populations in agricultural fields