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Statistics on the machinery performance are essential for farm managers to make better decisions. In this paper, the performance of all machineries in five sequential operations, namely bed forming, stone separation, planting, spraying and harvesting in the potato production system, were investigated during one growing season. In order to analyse and decompose the recorded GPS data into various time and distance elements for estimation of the machinery performance, an automatic GPS analysis tool was developed. The field efficiency and field capacity were estimated for each operation. Specifically, the measured average field efficiency was 71.3% for bed forming, 68.5% for stone separation, 40.3% for planting, 69.7% for spraying, and 67.4% for harvesting. The measured average field capacities were 1.46 ha/h, 0.53 ha/h, 0.47 ha/h, 10.21 ha/h, 0.51 ha/h, for the bed forming, stone separation, planting, spraying, and harvesting operations, respectively. These results deviate from the corresponding estimations calculated based on norm data from the American Society of Agricultural and Biological Engineers (ASABE). The deviations indicate that norms provided by ASABE cannot be used directly for the prediction of performance of the machinery used in this work. Moreover, the measured data of bed forming and stone separation could be used as supplementary data for the ASABE which does not provide performance norms for these two operations. The gained results can help farm managers to make better management and operational decisions that result in potential improvement in productivity and profitability as well as in potential environmental benefits.

A flexible high-level control language is an important element in the ongoing task of introducing automated guided vehicles (AGV) to new application domains. A new application domain is row crops, where small AGV’s will perform weed control around individual crop plants. This paper defines the requirements and scope of a process- and behaviour-based scripting language needed to control the weeding AGV in an agricultural row crop. The goal is to traverse and cover the whole field with no human auxiliary input during the field operation. The basis is the transparent and tactical real-time control language (CL) for small mobile robots (SMR). This SMR-CL has been modified to include some necessary motion commands and a supplemental supervisory function to monitor and record the progressive coverage of the field. The control language was then tested by applying it to a scenario representing typical field conditions for row crops. The construction of a suitable SMR-CL script for use in a field clearly demonstrates the feasibility of adapting behaviour-based control systems to field structures. The conducted case study indicated the importance of including goal-directing modules. Such a module is described here as the ‘supervisory field coverage monitor’ (SFCM), which acts to coordinate the behaviours. The applicability of this modified SMR-CL has been successfully demonstrated using a vehicle test in a specially designed artificial row crop field. The analysis of the operational performance verified that it is possible to cover all rows in a field without conducting time-consuming planning procedures.

Based on the development of a robotic tool carrier (Hortibot) equipped with weeding tools, a feasibility study was carried out to evaluate the viability of this innovative technology. The feasibility was demonstrated through a targeted evaluation adapted to the obtainable knowledge on the system performance in horticulture. A usage scenario was designed to set the implementation of the robotic system in a row crop of seeded bulb onions considering operational and functional constraints in organic crop, production. This usage scenario together with the technical specifications of the implemented system provided the basis for the feasibility analysis, including a comparison with a conventional weeding system. Preliminary results show that the automation of the weeding tasks within a row crop has the potential of significantly reducing the costs and still fulfill the operational requirements set forth. The potential benefits in terms of operational capabilities and economic viability have been quantified. Profitability gains ranging from 20 to 50% are achievable through targeted applications. In general, the analyses demonstrate the operational and economic feasibility of using small automated vehicles and targeted tools in specialized production settings.