Aspects of automation of selective cleaning

Cleaning (pre-commercial thinning) is a silvicultural operation, primarily used to improve growing conditions of remaining trees in young stands (ca. 3 - 5 m of height). Cleaning costs are considered high in Sweden and the work is laborious. Selective cleaning with autonomous artificial agents (robots) may rationalise the work, but requires new knowledge. This thesis aims to analyse key issues regarding automation of cleaning; suggesting general solutions and focusing on automatic selection of main-stems. The essential requests put on cleaning robots are to render acceptable results and to be cost competitive. They must be safe and be able to operate independently and unattended for several hours in a dynamic and non-deterministic environment. Machine vision, radar, and laser scanners are promising techniques for obstacle avoidance, tree identification, and tool control. Horizontal laser scannings were made, demonstrating the possibility to find stems and make estimations regarding their height and diameter. Knowledge regarding stem selections was retrieved through qualitative interviews with persons performing cleaning. They consider similar attributes of trees, and these findings and current cleaning manuals were used in combination with a field inventory in the development of a decision support system (DSS). The DSS selects stems by the attributes species, position, diameter, and damage. It was used to run computer-based simulations in a variety of young forests. A general follow-up showed that the DSS produced acceptable results. The DSS was further evaluated by comparing its selections with those made by experienced cleaners, and by a test in which laymen performed cleanings following the system. The DSS seems to be useful and flexible, since it can be adjusted in accordance with the cleaners’ results. The laymen’s results implied that the DSS is robust and that it could be used as a training tool. Using the DSS in automatic, or semi-automatic, cleaning operations should be possible if and when selected attributes can be automatically perceived. A suitable base-machine and thorough research, regarding e.g. safety, obstacle avoidance, and target identification, is needed to develop competitive robots. However, using the DSS as a training-tool for inexperienced cleaners could be an interesting option as of today

Advanced Non-Chemical and Close to Plant Weed Control system for Organic Agriculture

Use of chemical has been reduced in agriculture for controlling weeds emergence. The use of alternative systems, such as cultural practices (mulching, flame, intercropping etc.) and mechanical system (hoe, tine etc.) has been introduced by various researchers. Automation technique based on sensors controlled system has enhanced the efficiency of the mechanical system for weed control. Mostly, low cost image acquisition sensors and optical sensor to detect the plant ensuring swift operation of vehicles close the crop plants to remove competitive weeds. The available system need to be evaluated to get best possible system for close to plant (CTP) weed removal. In the study various non-chemical weed control measures has been explored and 30 mechanical tools for CTP were evaluated. High precision tillage solutions and thermal weed control by pulsed lasers for eradication of stem or main shoot were found to be the most promising weed control concepts for CTP operation

Machine vision applications in agriculture

Keynote paper. [Abstract]: With the trend of computers towards convergence with multimedia entertainment, tools for vision processing are becoming commonplace. This has led to the pursuit of a host of unusual applications in the National Centre for Engineering in Agriculture, in addition to work on vision guidance. These range from the identification of animal species, through the location of macadamia nuts as they are harvested and visual tracking for behaviour analysis of small marsupials to the measurement of the volume of dingo teeth

Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment

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

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

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