Sistema robótico terrestre para apoio a atividades de manutenção de solo em pomares de prunóideas

Fruto do crescimento da população mundial, a agricultura teve de se reinventar, introduzindo novas técnicas e ferramentas, conseguindo assim mais controlo sobre as colheitas, por forma a otimizá-las e assim aumentar os rendimentos na produção alimentar. Este é um setor que cada vez mais carece de mão-de-obra, devido à desertificação populacional dos meios rurais e como não pode parar, dada a necessidade de bens alimentares ser constante, assim como as tarefas a realizar, a introdução da robótica na agricultura poderá ajudar a resolver este constrangimento. Já existem algumas soluções automatizadas e robóticas para executar certos trabalhos agrícolas, mas ainda existe um longo caminho a percorrer até existirem soluções capazes de dar resposta a todas as tarefas realizadas na agricultura. Apesar de toda a evolução da robótica nesta área, a aplicação de muitos dos sistemas desenvolvidos não é rentável, pois o volume de trabalho existente não justifica o investimento inicial necessário. Nesta dissertação apresenta-se o dimensionamento e todas as etapas de construção de um robô agrícola para o controlo de infestantes e recolha de frutos caídos no chão do pomar. O robô foi projetado para ser aplicado na cultura do pêssego, podendo ser facilmente adaptável a outras culturas frutícolas. Esta proposta pretende contribuir para a redução da quantidade de herbicida utilizado no controlo de infestantes, reduzindo assim o impacto ambiental associado a esta tarefa, bem como minorar a atividade microbiana nas culturas da campanha seguinte através da recolha dos frutos caídos, evitando assim danos nos frutos das futuras culturas. Para tal, foi projetada e construída a estrutura de um robô terrestre tendo em consideração os requisitos dimensionais para a adequada locomoção na entrelinha de pomares de prunóideas. Tratando-se de um robô destinado a duas tarefas agrícolas a realizar em momentos diferentes da campanha, o projeto do braço robótico cartesiano com três graus de liberdade que incorpora foi fundamental. Aquando da aplicação particularizada de herbicida para controlo de infestantes e consequente redução da carga de produtos fitofarmacêuticos no solo, foi projetado um sistema de pulverização. Para a atividade de recolha de frutos caídos no solo, com destino à alimentação animal, e consequente promoção de economia circular e da redução da atividade microbiana e da multiplicação de insetos, com promoção do impacte ambiental por redução de aplicação de herbicidas e pesticidas, foi projetada uma garra flexível. Os componentes mecânicos, elétricos e eletrónicos de controlo, regulação e comando foram projetados, alguns desenvolvidos, montados e programados para o desenvolvimento do sistema robótico. Este sistema pretende ser um contributo para a nova revolução agrícola que se começa a desenhar, constituída pela automação e robotização de atividades agrícolas com o intuito de melhorar a eficácia e eficiência das produções (Agricultura 4.0), face à procura crescente de produtos agrícolas, condicionada pela disponibilidade de mão-de-obra, pela influência das alterações climáticas e pela necessidade cada vez mais premente de assegurar sustentabilidade ambiental na agricultura.As a result of the growth of the world population, agriculture had to reinvent itself by introducing new techniques and tools and thus achieving more control over the crops in order to optimize and increase yields in food production. This is a sector that every day increases the lack of human labour due to desertification of rural areas and it can’t stop, because of the constant need for food as well the work to be carried out, the introduction of robotics into agriculture could help to solve this problem. There are already some solutions to perform specific agricultural work, but there is still a long way to go before finding solutions capable of answering to all tasks performed in agriculture. Despite all the evolution of robotics in this area, the application of many of the systems developed is not profitable because the existing workload doesn’t justify the initial investment required. This dissertation presents the design and all the stages of construction of an agricultural robot for weed control and for collect fallen fruits on the orchard floor. The robot is designed to be applied in the peach culture but at the same time to be easily adaptable to other fruit crops. This proposal aims to contribute to the reduction of the herbicide used in weed control, thus reducing the environmental impact associated with that task, as well as reducing the microbial activity in the crops of the following year through the collection of fallen fruits, avoiding damages in fruits of future cultures. Thus, the structure of a terrestrial robot was designed and built considering the dimensional requirements for adequate locomotion between the rows of peach orchards. As it is a robot designed for two agricultural tasks to be carried out at different times during the campaign, the design of the cartesian robotic arm with three degrees of freedom that it incorporates was fundamental. For spraying herbicides for weed control and reducing the load of plant protection products on the soil, a spraying system was designed. For the activity of collecting fallen fruits on the ground for animal feed, and the consequent promotion of circular economy and the reduction of microbial activity and the multiplication of insects, with the promotion of the environmental impact by reducing the application of herbicides and pesticides, it was designed a flexible gripper. The mechanical, electrical and electronic components of the control, regulation and command system were designed, some of them constructed, assembled and programmed for the development of the robotic system. This system is intended to be a contribution to the new agricultural revolution that is arising, consisting in the automation and robotization of agricultural activities in order to improve the efficiency and efficiency of production (Agriculture 4.0), in view of the growing demand for agricultural products, restricted by the availability of human resources, the influence of climate change and the increasingly need to ensure environmental sustainability in agriculture

Automated Weed Detection Systems: A Review

A weed plant can be described as a plant that is unwanted at a specific location at a given time. Farmers have fought against the weed populations for as long as land has been used for food production. In conventional agriculture this weed control contributes a considerable amount to the overall cost of the produce. Automatic weed detection is one of the viable solutions for efficient reduction or exclusion of chemicals in crop production. Research studies have been focusing and combining modern approaches and proposed techniques which automatically analyze and evaluate segmented weed images. This study discusses and compares the weed control methods and gives special attention in describing the current research in automating the weed detection and control. Keywords: Detection, Weed, Agriculture 4.0, Computational vision, Robotic

Current status and future trends in agricultural robotics

This paper analyzes some of the innovations in agricultural robotics, specifically for weed control, harvesting and monitoring, taking into account the challenges of introducing robotics in this sector, such as fruit detection, orchard navigation, task planning algorithms, or sensors optimization. One of the trends in agriculture 4.0 is the introduction of swarm robotics, allowing collaboration between robots. Another trend is in aerial imagery acquisition for ground analysis as well as environmental reconstruction, complemented by field-mounted sensors. Although robots are becoming quite important in the evolution of agriculture, it is still unlikely that all tasks will be automated in the near future due to the complexity arised by the overall variability of cultures.Este trabalho de investigação é financiado pelo projeto PrunusBot - Sistema robótico aéreo autónomo de pulverização controlada e previsão de produção frutícola, Operação n.º PDR2020- 101-031358 (líder), Consórcio n.º 340, Iniciativa n.º 140, promovido pelo PDR2020 e cofinanciado pelo FEADER e União Europeia no âmbito do Programa Portugal 2020.info:eu-repo/semantics/publishedVersio

Bird monitoring and dispersion system

Birds continue to be one of the main factors of loss by producers in the region of Beira Interior. Fruits such as peaches and cherries continue to be damaged and their trees destroyed due to bird crop attacks. There are several methods to disperse birds, but all have low effects in the long-term as they demonstrate low variability and high maintenance. Drones are systems that are capable of dispersing birds due to their high mobility. Together with the use of audiovisual technologies, increase the effectiveness of the bird dispersion. However, to get the most out of each flight it is required to understand birds’ movements. Thus, a monitoring system is required. In this article, a technological solution is proposed that uses drones and aggregates the monitoring and dispersion systems so maximum effectiveness in bird dispersal is achieved.info:eu-repo/semantics/publishedVersio

Preliminary results of peach detection in images applying convolutional neuronal network

The fruit detection part is very important for a good performance in a yield estimation system. This paper presents the preliminary results using the object detection Faster R-CNN method in the peaches images. The aim is evaluate the method performance in the detection of peach RGB images. Images acquired in an orchard were used. Although this method of object detection has been applied in other studies to detect fruits, according to the literature, it has not been used to detect peaches. The results, although preliminary, show a great potential of using the method to detect peach.Este trabalho de investigação é financiado pelo projeto PrunusBot - Sistema robótico aéreo autónomo de pulverização controlada e previsão de produção frutícola, Operação n.º PDR2020-101-031358 (líder), Consórcio n.º 340, Iniciativa n.º 140, promovido pelo PDR2020 e co-financiado pelo FEADER e União Europeia no âmbito do Programa Portugal 2020.info:eu-repo/semantics/publishedVersio

Pseudo-Valid Cutting Planes for Two-Stage Mixed-Integer Stochastic Programs with Right-Hand-Side Uncertainty

We propose a novel way of applying cutting plane techniques to two-stage mixed-integer stochastic programs with uncertainty in the right-hand side. Instead of using cutting planes that are always valid, our idea is to apply pseudo-valid cutting planes to the second-stage feasible regions that may cut away feasible integer second-stage solutions for some scenarios and may be overly conservative for others. The advantage is that it allows us to use cutting planes that are affine in the first-stage decision variables, so that the approximation is convex and can be efficiently solved using techniques from convex optimization. We derive tight performance guarantees for using particular types of pseudo-valid cutting planes for simple integer recourse models. Moreover, we show in general that using pseudo-valid cutting planes leads to good first-stage solutions if the total variations of the one-dimensional conditional probability density functions of the random variables in the model converge to zero

PrunusBOT - Sistema robótico aéreo autónomo de pulverização controlada e previsão de produção frutícola

A robótica autónoma destinada a operações de análise e atuação nas culturas agrícolas tem vindo a evoluir, existindo exemplos de monitorização de culturas, de rega automatizada (que integra diferente informação edáfica e meteorológica), de aplicação localizada de fertilizantes e de herbicidas, de colheita automatizada e de manuseamento/transporte de cargas. Este projeto pretendeu conceber um sistema robotizado para previsão da colheita e aplicação particularizada de herbicida para controlo de infestantes. O sistema descrito neste artigo é composto por robôs autónomos, terrestre e aéreo com visão computacional por câmaras RGB e multiespectrais, que possibilitam i) deteção e reconhecimento de infestantes para aplicação precisa de produtos fitofarmacêuticos, ii) deteção e reconhecimento de frutos em árvores e copas destas, para caracterização das plantas e estimativa de produção. O reconhecimento, quer seja de infestantes como de frutos, é realizado através do método de inteligência artificial Faster R-CNN, aplicado aos datasets de imagens recolhidas em campo. No reconhecimento das infestantes é calculado o seu centróide, para onde é deslocado o bico de pulverização, anexo ao braço robótico cartesiano incorporado no robô terrestre autónomo. A função de reconhecimento dos frutos conduz à sua contagem, permitindo uma previsão da produção. Esta deteção é dificultada pela variação da iluminação natural, oclusão de frutos causada por folhas, ramos e outros frutos e múltiplas deteções da mesma fruta em imagens sequenciais. Os resultados experimentais de deteção de infestantes e de frutos em imagem de vídeo indicam uma precisão média de 85%. A estimativa da produção é complementada pela previsão do volume da copa da árvore obtido por aquisição de imagem captada via câmara montada em drone, destinada a suportar modelos empíricos de carga das árvores. A função de pulverização de infestantes é complementada com a capacidade de apanha de frutos caídos no chão. Pretende-se assim contribuir para um sistema de produção mais sustentável através da redução de utilização de produtos fitofarmacêuticos e simultaneamente apoiar o produtor na gestão da carga do pomar com reflexos na gestão da cadeia comercial.info:eu-repo/semantics/publishedVersio

Real-Time Weed Control Application Using a Jetson Nano Edge Device and a Spray Mechanism

Portable devices play an essential role where edge computing is necessary and mobility is required (e.g., robots in agriculture within remote-sensing applications). With the increasing applications of deep neural networks (DNNs) and accelerators for edge devices, several methods and applications have been proposed for simultaneous crop and weed detection. Although preliminary studies have investigated the performance of inference time for semantic segmentation of crops and weeds in edge devices, performance degradation has not been evaluated in detail when the required optimization is applied to the model for operation in such edge devices. This paper investigates the relationship between model tuning hyperparameters to improve inference time and its effect on segmentation performance. The study was conducted using semantic segmentation model DeeplabV3 with a MobileNet backbone. Different datasets (Cityscapes, PASCAL and ADE20K) were analyzed for a transfer learning strategy. The results show that, when using a model hyperparameter depth multiplier (DM) of 0.5 and the TensorRT framework, segmentation performance mean intersection over union (mIOU) decreased by 14.7% compared to that of a DM of 1.0 and no TensorRT. However, inference time accelerated dramatically by a factor of 14.8. At an image resolution of 1296×966, segmentation performance of 64% mIOU and inference of 5.9 frames per second (FPS) was achieved in Jetson Nano’s device. With an input image resolution of 513×513, and hyperparameters output stride OS = 32 and DM = 0.5, an inference time of 0.04 s was achieved resulting in 25 FPS. The results presented in this paper provide a deeper insight into how the performance of the semantic segmentation model of crops and weeds degrades when optimization is applied to adapt the model to run on edge devices. Lastly, an application is described for the semantic segmentation of weeds embedded in the edge device (Jetson Nano) and integrated with the robotic orchard. The results show good spraying accuracy and feasibility of the method

Automated Weed Detection Systems: A Review

A weed plant can be described as a plant that is unwanted at a specific location at a given time. Farmers have fought against the weed populations for as long as land has been used for food production. In conventional agriculture this weed control contributes a considerable amount to the overall cost of the produce. Automatic weed detection is one of the viable solutions for efficient reduction or exclusion of chemicals in crop production. Research studies have been focusing and combining modern approaches and proposed techniques which automatically analyze and evaluate segmented weed images. This study discusses and compares the weed control methods and gives special attention in describing the current research in automating the weed detection and control. Keywords: Detection, Weed, Agriculture 4.0, Computational vision, Robotic