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The purpose of this paper is to investigate the issue of low yields in the packinghouses of green ornamentals and cut flowers due to the high rate of crops waste. Waste may be caused by pests, diseases and extreme weather or environmental conditions that are not under the farmer’s control. Other causes may relate to work processes as follows: irrigation, spraying, harvesting, handling, transportation, sorting, bundling and packaging.

The farm under study is a private owned business managed by the owner’s family members with growing area of 22 ha and eight daily workers. The farm produces about 2.5m units (flower stems) per year. The farm represents a typical flower farm in Israel. A costing model and decision rules were developed to identify the critical waste rate that will consider being economic to ship to the market. The model takes into account the production process, the production yield, the operational costs and sales price and calculates the breakeven point. A simulation model was developed to verify the relationships between waste rate to the total process time per stem and flow time.

Results show that the critical waste rate for Ruscus, Antirrhinum, Aralia and Aspidistra crops is 16, 74, 22 and 39 percent, respectively. The total process time per harvested stem decreases as the waste rate increases.

A working model was developed to determine the waste threshold rate and support the farmer in day-to-day economic decisions regarding shipment to the market and effective management of his workers.

The purpose of this paper is to describe a methodology for characterization of the robot environment to help solve such problem as designing an optimal agricultural robot for a specific agricultural task.

Defining and characterizing a task is a crucial step in the optimization of a task-specific robot. It is especially difficult in the agricultural domain because of the complexity and unstructured nature of the environment. In this research, trees are modeled from orchards and are used as the robot working environment, the geometrical features of an agricultural task are investigated and a method for designing an optimal agricultural robot is developed. Using this method, a simplified characteristic environment, representing the actual environment, is developed and used.

Case studies showing that the optimal robot, which is designed based on the characteristic environment, is similar to the optimal robot, which is designed based on the actual environment (less than 4 per cent error), is presented, while the optimization run time is significantly shorter (up to 22 times) when using the characteristic environment.

This paper proposes a new concept for solving the robot task-based optimization by the analysis of the task environment and characterizing it by a simpler artificial task environment. The methodology decreases the time of the optimal robot design, allowing to take into account more details in an acceptable time.

Labor is the largest single cost contributor in agriculture. Accurate estimation of labor requirements is, thus, a key to cost reduction. Work measurement is the professional discipline for this type of estimations, in the industrial engineering domain. Horticulture, however, lays a substantial barrier to work and, thus, to work measurement. Till harvesting, its products – fruits/flowers, are in fixed positions, and for all tasks the workers have to arrive at the plant. The purpose of this paper is to develop, test and analyze a system to perform horticultural work study in agricultural environments in order to accurately estimate the required amount of labor for each activity and to improve productivity.

In this paper, the development of an advanced system for work study on a hand-held computer (HHC) platform for horticultural work measurement is presented and an experimental study was conducted. The methodology consists of characteristics of the system platform including hardware, interface and software, development of a dedicated measuring software, a controlled experiment in agricultural environment and a statistical analysis.

The study shows that a single surveyor who uses traditional tools is subject to measurement errors, which can be reduced only with the assistance of a second surveyor. The study further shows that the HHC platform enables to avoid this second surveyor – a single surveyor who uses the HHC platform performs as accurate as two surveyors who use traditional tools. Of course, being computers, the HHC platform maintains the advantage of error free data transfer, in practically negligible time.

This paper presents a unique approach to perform work study in agricultural environment and contributes to minimize the errors accumulated in the process and the manpower required to perform the measurements.

Automatic target recognition in agricultural harvesting robots is characterized by low detection rates and high false alarm rates due to the unstructured nature of both the environment and the objects. To improve detection human‐robot collaboration levels were defined and implemented. The collaboration level is defined as the level of system autonomy or the level at which the human operator (HO) interacts with the system. Experimental results on images taken in the field indicate that collaboration of HO and robot increases detection and reduces the time required for detection.