Computational Contributions to the Automation of Agriculture

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of crops. Finally, the effects of automating agriculture are briefly considered, including labor, the environment, and direct effects on farmers

Hortibot: Feasibility study of a plant nursing robot performing weeding operations – part IV

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

Analysis and Definition of the close-to-crop Area in Relation to Robotic Weeding

The objective of this paper is to analyse and define the field conditions close to the crop plants of sugar beet (Beta vulgaris L.). The aim is to use this study for the choice and development of new physical weeding methods to target weeds at individual plant scale level. It was found that the close to crop area is like a ring structure, comprising an area between an inner- and outer-circle around the sugar beet seedling. Physical weeding should not be applied to the area within the inner circle. The radius of the inner circle increases with the appearance of young beet leaves during the growth season. It was also found, that no weeds were germinating within 1 cm around individual sugar beet seedlings. Therefore this distance should be added to the radius of the inner circle. The space between the inner and outer circle is termed the close to crop area where physical weeding should be applied. The size of this area is defined by the developmental stage of the sugar beet fibrous root system and foliage. Thus, the determination of the growth stage of individual crop plants is necessary before any physical weeding can take place in the close to crop area. Uprooting, cutting between stem and root or damage of main shoot can do the physical control of most weed species located in the close to crop area. However, the targeting of weeds from above and from different angels above ground is limited in the close to crop area. This is caused by the fact that sugar beet leaves do not leave much space between leaves and ground and that our own study indicate that 26.4% of sugar beet plants at the 4-6 leaf stage are covering the main shoot of weeds. The most problematic weeds are the species, which have their main shoot and leaves located close to ground level. These species can either be controlled by damage of the main shoot or with a combination of shallow surface cutting and burial. Discrimination between weed species is beneficial under certain circumstances. First, the efficiency of the physical control of individual weed species is depending on the timing. Secondly some weeds species do not have significant negative impact on the yield, but instead leaving these species uncontrolled could benefit to an increased bio-diversity and reduced time and energy input for a physical weeding process. This paper is contributing to the ongoing Danish research project Robotic Weeding

Organic Farming Scenarios: Operational Analysis and Costs of implementing Innovative Technologies

The objective of this study has been to design a number of farm scenarios representing future plausible and internally consistent organic farming enterprises based on milk, pig, and plant production and use these farm scenarios as the basis for the generation of generalised knowledge on labour and machinery input and costs. Also, an impact analysis and feasibility study of introducing innovative technologies into the organic production system has been invoked. The labour demand for the production farms ranged from 61 to 253hha1 and from 194 to 396hLU1 (LU is livestock units) for work in the animal houses. Model validation results showed that farm managerial tasks amount to 14–19% of the total labour requirement. The impact of introducing new technologies and work methods related to organic farming was evaluated using two innovative examples of weed control: a weeding robot and an integrated system for band steaming. While these technologies increased the capital investment required, the labour demand was reduced by 83–85% in sugar beet and 60% in carrots, which would improve profitability by 72–85% if fully utilised. Profitability is reduced, if automation efforts result in insufficient weed removal compared to manual weeding. Specifically, the benefit gained by robotic weeding was sensitive to the weed intensity and the initial price of the equipment, but a weeding efficiency of under 25% is required to make it unprofitable. This approach demonstrates the feasibility of applying and testing operational models in organic farming systems in the continued evaluation and documentation of labour and machinery inputs

Vision-based weed identification with farm robots

Robots in agriculture offer new opportunities for real time weed identification and quick removal operations. Weed identification and control remains one of the most challenging task in agriculture, particularly in organic agriculture practices. Considering environmental impacts and food quality, the excess use of chemicals in agriculture for controlling weeds and diseases is decreasing. The cost of herbercides and their field applications must be optimized. As an alternative, a smart weed identification technique followed by the mechanical and thermal weed control can fulfill the organic farmers’ expectations. The smart identification technique works on the concept of ‘shape matching’ and ‘active shape modeling’ of plant and weed leafs. The automated weed detection and control system consists of three major tools. Such as: i) eXcite multispectral camera, ii) LTI image processing library and iii) Hortibot robotic vehicle. The components are combined in Linux interface environment in the eXcite camera associate PC. The laboratory experiments for active shape matching have shown interesting results which will be further enhanced to develop the automated weed detection system. The Hortibot robot will be mounted with the camera unit in the front-end and the mechanical weed remover in the rear-end. The system will be upgraded for intense commercial applications in maize and other row crops

Individual plant care in cropping systems

Individual plant care cropping systems, embodied in precision farming, may lead to new opportunities in agricultural crop management. The objective of the project was to provide high accuracy seed position mapping of a field of sugar beet. An RTK GPS was retrofitted on to a precision seeder to map the seeds as they were planted. The average error between the seed map and the actual plant map was about 32 mm to 59 mm. The results showed that the overall accuracy of the estimated plant positions is acceptable for the guidance of vehicles and implements. For subsequent individual plant care, the deviations were not, in all cases, small enough to ensure accurate individual plant targeting