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- Resultados preliminares de deteção de imagens de pêssegos aplicando o método Faster R-CNNPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Veiros, André; Proença, HugoO modelo Faster R-CNN tem grande potencial de aplicação na deteção de pêssegos e poderá vir a ser uma boa ferramenta para estimar a producão em pomares, ajudando no planeamento da colheita e do armazenamento da fruta
- A Novel Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for Bird Monitoring and RepellingPublication . Mesquita, Ricardo; Gaspar, Pedro DinisBird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A novel path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this paper. This path planning optimization algorithm aims to manage the drone’s distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm’s performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The field tests were also essential to understand the algorithm’s behavior of the path planning algorithm in the UAV, showing that there is less efficiency with fewer points of interest, but this does not correlate with the flight time. In addition, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted. Thus, the proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time, being suited for this and other applications.
- Resultados preliminares de deteção de imagens de pêssegos aplicando o método Faster R-CNNPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Veiros, André; Proença, H.A deteção de frutos é de fundamental importância em sistemas de estimação de produção. Neste trabalho, são apresentados os resultados preliminares da utilização do método de deteção de objetos Faster R-CNN na deteção de imagens de pêssegos. O estudo consiste na avaliação do desempenho do método em imagens RGB obtidas em ambiente real num pomar. Embora este método de deteção tenha sido aplicado noutros trabalhos com o objetivo de detetar frutos, ainda não foi utilizado na deteção de pêssegos. A cor, a sua distribuição na árvore e a clusterização são características intrínsecas aos pêssegos. Os resultados obtidos, ainda que preliminares, mostram um elevado potencial da utilização do método na deteção destes frutos. Todavia, os resultados também mostram a necessidade de melhoria no desempenho. Isso pode ser alcançado com o aumento na quantidade de imagens de treino e também por definir um melhor critério de anotação dos frutos oclusos.
- Peaches Detection Using a Deep Learning Technique - A Contribution to Yield Estimation, Resources Management, and Circular EconomyPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Simões, Maria Paula; Ramos, António; Proença, H.; Inácio, Pedro R. M.Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.
- Preliminary results of peach detection in images applying convolutional neuronal networkPublication . Assunção, Eduardo Timóteo; Proença, H.; Veiros, André; Mesquita, Ricardo; Gaspar, Pedro DinisThe 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.
- Bird monitoring and dispersion systemPublication . Mesquita, Ricardo; Veiros, André; Gaspar, Pedro DinisBirds 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.
- Current status and future trends in agricultural roboticsPublication . Veiros, André; Mesquita, Ricardo; Gaspar, Pedro DinisThis 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.
- Automated Weed Detection Systems: A ReviewPublication . Shanmugam, Saraswathi; Assunção, Eduardo Timóteo; Mesquita, Ricardo; Veiros, André; Gaspar, Pedro DinisA 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.
- A Novel Path Planning Optimization Algorithm for Semi-Autonomous UAV in Bird Repellent Systems Based in Particle Swarm OptimizationPublication . Mesquita, Ricardo Jorge Mendes; Gaspar, Pedro Miguel de Figueiredo Dinis OliveiraBird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting became inefficient in the long run, keeping high maintenance and reduced mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this dissertation. This technique was used due to the need for an easy implementation optimization algorithm to start the initial tests. The PSO algorithm is simple and has few control parameters while maintaining a good performance. This path planning optimization algorithm aims to manage the drone's distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm's performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time. However, it is necessary to point out that the path planning optimization algorithm may have difficulty finding a suitable solution if there is a bad ratio between the total distance for path planning and points of interest. The field tests were also essential to understand the algorithm's behavior of the path planning algorithm in the UAV, showing that there is less energy discharged with fewer points of interest, but that do not correlates with the flight time. Also, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted.
- Aplicação de modelos empíricos na avaliação do desempenho do pessegueiro ‘Catherine’ em dois anos consecutivosPublication . Ramos, António; Ferreira, Dora; Barateiro, Anabela; Ramos, Cristina; Fragoso, Preciosa; Lopes, Sandra; Amado, Carlos; Mesquita, Ricardo; Simões, Maria Paula; Gaspar, Pedro DinisA carga, ou seja, onúmero de frutos que permanece na árvore árvore após a fecundação e o vingamento do fruto, influencia o crescimento vegetativo, vegetativo,a produção, o tamanho e a qualidade dos frutos, o rendimento económico e a regularidade das produções (Johnson e Rasmussen ,1990). Em trabalho anterior anterior com o pessegueiro ‘Catherine’ em 2015 e2016 ,Ramos (2017 ) apresentou as bases do desenvolvimento de modelos empíricos para avaliar a eficiência produtiva e propôs a sua aplicação para estudar o efeito da carga no crescimento do fruto e, desse modo, estimar a capacidade produtiva a partir da medição do volume da árvore .