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O pêssego é um fruto de caroço que apresenta uma polpa carnuda muito suculenta, uma pele suave e um sabor que satisfaz o paladar mais exigente. Em Portugal, a principal região produtora de pêssego é a Beira Interior, devido às suas condições edafoclimáticas que são particularmente favoráveis a esta cultura. Sendo o pêssego um fruto que se deteriora e amadurece rapidamente à temperatura ambiente, existe a necessidade de conservar e armazenar o fruto em condições controladas e modificadas, prolongando a vida útil e preservando as suas características organoléticas. A conservação pelo frio é o método mais utilizado permitindo prolongar o período de oferta e comercialização. No entanto, pode provocar alterações internas no fruto, vulgarmente designadas por “dano por frio”. Este distúrbio fisiológico é induzido por baixas temperaturas e caracteriza-se por alterações da textura da polpa que altera significativamente as características organoléticas, sem ser visível exteriormente. Segundo diversos autores a temperatura ideal de conservação de pêssegos situa-se entre 0ºC e 2,2ºC e 85% a 95% de humidade. Neste artigo são apresentados resultados de uma análise experimental realizada com a cultivar ‘Royal Time’ produzida na região da Beira Interior, avaliando o efeito da conservação em câmaras de refrigeração de três produtores distintos da região, durante um período de 42 dias, sendo retirada do frio uma subamostra de 24 frutos a cada 7 dias, dos quais 50% foram analisados no dia de saída do frio e 50% após 2 dias em ambiente doméstico. Este estudo permitiu verificar que nas condições de frio dos produtores, o dano por frio de escala 1 (dano ligeiro) é visível em 25% frutos aos 21 dias de conservação. O dano por frio de escala 3 (dano moderado grave) é visível com maior destaque em 25% frutos aos 42 dias de conservação + 2 dias em ambiente doméstico.

The concept of Precision Agriculture is usually associated with the usage of high-end technology equipment (hardware or software) to evaluate or monitor the conditions of a determined portion of land, adjusting afterwards the production factors, like seeds, fertilizers, pesticides, growing regulators, water, according to differential detected characteristics. This paper describes an algorithm developed to analyze and process images to recognize fruits, particularly peaches, and calculate it dimensions, like volume and weight. The recognition of peaches on their natural conditions on trees depends on several spatial- and time-variable parameters and requires complex segmentation algorithms. The proposed algorithm applies image segmentation for extraction of characteristics such as color and shape. These characteristics were used to train a classification method through a Support Vector Machine (SVM) to improve the recognition rate of fruits. The algorithm is designed to acquire images with a high-resolution camera installed in a drone that will fly between the tree lines. The production prediction of 29.3 tons per hectare was obtained based on volume and relation weight/volume calculated for the recognized peaches. An overall precision of 72% was achieved for the prediction rate of peaches in orchards (808 trees/ha). This is the first study regarding the application of these concepts under orchard trees aiming the production prediction along the fruit maturation. Other useful future applications are foreseen in orchard trees, related not only to production prediction, for this type of algorithm.

The peach is a stone fruit with a very juicy flesh, smooth skin, and a taste that satisfies the most demanding palate. The quality of this fruit is usually determined by texture, appearance, scent, flavor, and nutritional value. Peaches are very sensitive fruits, which deteriorate and ripen very quickly at environment temperature. Cold conservation is the usual method for delaying the product deterioration, but this process may cause chilling injury (CI). This damage is a physiological low temperaturedriven disturbance, which affects the fruit quality, reducing its storage and shelf life and impairing its organoleptic characteristics. The characteristics of the main techniques and treatments to mitigate the CI such as: temperature-dependent, controlled and/or modified atmosphere, intermittent warming (IW), ethylene inhibitors, Glycine Betaine (GB), Methyl Jasmonate (MeJA) treatments, exposure to ultraviolet radiation (UV) and genetic determination are reviewed. Its consequences are evaluated and the ideal conservation air temperature and humidity are set to enhance organoleptic characteristics.

The agro-industrial sector consumes a significant amount of natural resources for farming and meat production. By 2050, population growth is expected, generating more demand and, consequently, more consumption of scarce resources. This challenging scenario is a concern of the European Commission, revealed in the Green Deal commitment and by the United Nations’ 12th goal of sustainable development. Thus, organizations must increase productivity and be more sustainable as soon as possible. Internet of Things (IoT) is introduced as a solution to facilitate agro-food companies to be more eco-efficient, mainly facing difficulties on farms, such as food loss and waste, best efficiency in management of resources, and production. The deployment of this technology depends on the stage of maturity and potential of implementation. To assess and characterize companies, with respect of IoT implementation, a survey was applied in 21 micro, small and medium agro-food companies, belonging to milk, honey, olive oil, jams, fruticulture, bakery and pastry, meat, coffee, and wine sectors, in the central region of Portugal. As results, this paper reveals the stage of maturity, level of sophistication, potential, opportunities, solutions, and barriers for implementation of IoT. Additionally, suggestions and recommendations to improve practices are discussed.

This paper presents the performance evaluation of a wireless sensor mesh network, for monitoring temperature and humidity in horticultural products when transported in truck galleys. For this purpose a software solution was proposed using ESP8266 devices powered by batteries. The mesh network was managed by the painlessMesh library. The proposed solution aims to minimize the energy consumption of the sensor nodes. The validation of the solution was performed in an area simulating a galley of a truck, where five sensor nodes and a root node were distributed. The tests were developed considering four different models involving variations in messages delivery confirmation, number of attempts until successful delivery and duty cycle duration of the nodes. The performance evaluation of the solution aimed to determine, connectivity rate, sending rate after connection and delivery rates of the first and second attempts. The results obtained show that the message delivery confirmation does not bring added value to the solution, contributing only to increase energy consumption. The use of synchronous duty cycles also showed worse results than the asynchronous use. These results allow the creation of a knowledge base for the use of this solution in a real context.

The continuous rise in the world’s population has increased the need for food, resulting in a rise of agricultural holdings to ensure the supply of these goods directly to the populations and indirectly to all processing industries in the food business. This situation has led agriculture to reinvent itself and introduce new technics and tools to ensure tighter control of the crops and increase yields in food production. However, the lack of labor coupled with the evolution of weeds resistant to herbicides created a crisis in agricultural food production. However, with the growing evolution in electronics, automation, and robotics, new paths are emerging to solve these problems. A robotic rover was designed to optimize the tasks of weed control and collection of fallen fruits of an orchard. In weed control, a localized spraying system is proposed, therefore reducing the amount of applied herbicides. With fruit collection, it is possible to direct fallen fruits for animal feeding and possible to reduce microbial activity on the next campaign crops, therefore avoiding damage. This study proposes the simulation of this robotic rover on robotic simulation software. It also proposes the replication of a similar environment of an orchard to generate an algorithm that controls the rover on the tasks of localized spraying and fallen fruit collection. Creating and testing these algorithms by using a robotic simulator speed up and ease the evaluation of different scenarios and hypotheses, with the added benefit of being able to test two tasks simultaneously. This method also allows greater freedom and creativity because there are no concerns about hardware damage. It should also be noted that development costs are very low.

Precision irrigation and optimization of water use have become essential factors in agricul- ture because water is critical for crop growth. The proper management of an irrigation system should enable the farmer to use water efficiently to increase productivity, reduce production costs, and maxi- mize the return on investment. Efficient water application techniques are essential prerequisites for sustainable agricultural development based on the conservation of water resources and preservation of the environment. In a previous work, an off-policy deep reinforcement learning model, Deep Q-Network, was implemented to optimize irrigation. The performance of the model was tested for tomato crop at a site in Portugal. In this paper, an on-policy model, Advantage Actor–Critic, is implemented to compare irrigation scheduling with Deep Q-Network for the same tomato crop. The results show that the on-policy model Advantage Actor–Critic reduced water consumption by 20% compared to Deep Q-Network with a slight change in the net reward. These models can be developed to be applied to other cultures with high production in Portugal, such as fruit, cereals, and wine, which also have large water requirements.

The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors of production such as seeds, fertilizer, water, etc., accordingly. Vine trunk detection can help create an accurate map of the vineyard that the agricultural robot can rely on to safely navigate and perform a variety of agricultural tasks such as harvesting, pruning, etc. In this work, the state-of-the-art single-shot multibox detector (SSD) with MobileDet Edge TPU and MobileNet Edge TPU models as the backbone was used to detect the tree trunks in the vineyard. Compared to the SSD with MobileNet-V1, MobileNet-V2, and MobileDet as backbone, the SSD with MobileNet Edge TPU was more accurate in inference on the Raspberrypi, with almost the same inference time on the TPU. The SSD with MobileDet Edge TPU achieved the second-best accurate model. Additionally, this work examines the effects of some features, including the size of the input model, the quantity of training data, and the diversity of the training dataset. Increasing the size of the input model and the training dataset increased the performance of the model.

A cereja é muito apreciada e apresenta um tempo de comercialização muito curto devido a ser um fruto altamente perecível. Técnicas de conservação pós-colheita são essenciais para manter a qualidade da cereja até serem consumidas. Baixas temperaturas são utilizadas para retardar o processo de deterioração da fruta e como complemento a aplicação de atmosferas controladas permite retardar o processo de amadurecimento e envelhecimento. A diminuição de oxigénio e o aumento de dióxido de carbono e azoto inibe o amadurecimento, mantendo o sabor e a qualidade da fruta. Neste trabalho experimental, cerejas da cultivar Satin foram conservadas em câmaras de refrigeração no produtor e nas instalações do CATAA com equipamento de atmosferas controladas. Quatro atmosferas controladas com diferentes combinações de oxigénio e dióxido de carbono foram testadas e o seu efeito na qualidade das cerejas foi avaliado. Ao longo do tempo de conservação as cerejas foram analisadas a diferentes níveis: qualidade (peso, dureza, cor e sólidos solúveis totais), microbiológico e organolético. Os resultados de temperatura e humidade no produtor e no CATAA, foram comparados e indicam que ambas as situações apresentam ótimas condições de conservação. No entanto, complementar a conservação com atmosferas controladas sugere que a qualidade da cereja é mantida por mais tempo, através da minimização do envelhecimento e processo de amadurecimento.