Using a GIS technology to plan an agroforestry sustainable system in Sardinia

This study was conducted with the aim to quantify the spread of livestock agroforestry in a Mediterranean ecosystem (island of Sardinia, Italy) and evaluate its sustainability in terms of grazing impact. By using GIS software ArcMap 10.2.2, the map of Sardinia vegetal landscape, obtained by information of Sardinia nature map based on the classification of habitat according to CORINE-Biotopes system, have been overplayed with the map of livestock grazing impact map CAIA developed by INTREGA (spin-off ENEA), to obtain for Meriagos (local agro-silvo-pastoral systems; classified “Dehesa 84.6” according to CORINE-Biotopes system), bushlands and woodlands, the surfaces under grazing and evaluate the extension of overgrazing for each of them

Designing and Implementing a Classification Model for Mangoes Based on Size and Ripeness using Image Processing

Image processing is an advanced technology that significantly supports production, identification, and quality control for fruits. This paper uses image processing techniques to develop a mango classification system based on size and ripeness. The system integrates hardware, including an Arduino microcontroller, camera, sensors, actuators, and a user-friendly computer interface for monitoring and control. The classification algorithm extracts key features of the mangoes, such as their color and shape, to categorize them into predefined quality classes. Experimental results demonstrate that the system achieves an accuracy exceeding 90% for both ripeness and size classification, with a productivity level of 300 kg/hour, surpassing the initial target of 250 kg/hour. Furthermore, the system operates reliably under varying lighting conditions, ensuring flexibility and continuous productivity. These advancements highlight the system’s potential to enhance efficiency and quality in fruit processing industries

Development and Evaluation of an Interactive AI-Powered Robotic Assistant for Language Learning

Accessibility and quality of educational resources remain significant challenges, particularly in resource-limited contexts. This study presents an AI-powered interactive robotic assistant designed to enhance language learning. The assistant integrates vocal, visual, and gestural interactions using a Raspberry Pi, Arduino, a TFT display, servo motors for motion, and a natural language processing system. In addition, the system incorporates mobile-based interaction for remote control and progress monitoring. The assistant dynamically adjusts content to student needs using educational personalization algorithms and provides automated assessments to adapt to the difficulty of activities. The solution is designed with low-cost hardware, such as cardboard enclosures and basic peripherals, and prioritizes sustainability and scalability. Preliminary results indicate improved student engagement and motivation by combining auditory, visual, and kinesthetic elements. The study concludes that the robotic assistant has significant potential to transform language learning through an accessible, mobile-enhanced interactive solution

Experimental analysis of the conservation conditions and development of a traceability device for fruit products

Fruit and vegetables are often subject to conditions during storage and transport which in no way promote the maintenance of their biological properties. As a result of this loss of quality, there is a decrease in product appreciation. But more important than the loss of economic value is safety and food waste. This dissertation involved an experimental study characterized by the evaluation of the conservation conditions inside the conservation chambers of fruit, namely peach and cherry producers in the Beira Interior region, Portugal. This study consisted of monitoring during two campaigns, 2018 and 2019, the temperature and relative humidity of the conservation air using several “LASCAR ELECTRONICS” dataloggers, arranged in different boxes located at different positions and heights on a pallet. With the results, it was made the characterization of the conservation environment to which the fruit products were subjected, as well as the dimensioning of a monitoring system. However, the monitoring system used does not allow real-time traceability of parameters influencing product quality. Remote monitoring systems, whose fundamental requirements relate to range and autonomy, make use of communications technologies to map characteristic crop parameters to reduce the unnecessary application of resources or materials. A prototype was developed with the function of monitoring the boxes of fruit products during transport between the producer and the distribution center. This device is composed of an ARDUINO UNO Rev3 microcontroller that acquires every 5 minutes of temperature and relative humidity through a DHT 11 sensor. It has a SIM800L module which gives it the ability to communicate in real-time via GSM. It also incorporates a 3.7 V - 2600mAh battery which gives you an approximately 60-hour power range. By acquiring temperature and relative humidity values, the system allows the producer to remotely control their product during the most critical stages of the cold chain, transportation, and storage at the distribution center. Although temperature and humidity data can be used by the producer to ensure the quality of their product upon delivery to the distribution center, location history allows for the optimization of transportation routes to extend product life., which, in turn, is reflected in the increased economic revenue of SMEs and the reduction of food waste.Os produtos hortofrutícolas encontram-se muitas das vezes sujeitos a condições durante a conservação e o transporte que em nada promovem a manutenção das suas propriedades biológicas. Como resultado desta perda de qualidade, ocorre um decréscimo da valorização do produto. Mas mais importante que a perda do valor económico, é a segurança e o desperdicio alimentar. Esta dissertação envolveu um estudo experimental caracterizado pela avaliação das condições de conservação no interior de câmaras de conservação de produtores de frutícolas, nomeadamente de pêssego e de cereja da região da Beira Interior, Portugal. Este estudo consistiu na monitorização durante duas campanhas, a de 2018 e a 2019, da temperature e humidade relativa do ar de conservaçao fazendo uso de vários dataloggers da “LASCAR ELETRONICS”, dispostos em diferentes caixas localizadas a diferentes posições e alturas numa palete. Com os resultados, foi feita a caracterização do ambiente de conservação a que os produtos frutícolas estiveram sujeitos, bem como ao dimensionamento de um sistema de monitorização. Todavia, o sistema de monitorização utilizado não permite realizar uma rastreabilidade em tempo real dos parâmetros influentes na qualidade dos produtos. Os sistemas de monitorização remota, cujos requisitos fundamentais se prendem com a autonomia e alcance, fazem uso de tecnologias de comunicações para formar mapas dos parâmetros característicos de culturas com o intuito de reduzir a aplicação desnecessária de recursos ou materiais. Foi assim desenvolvido um protótipo com a função acompanhar as caixas de produtos frutícolas durante o transporte entre o produtor e o centro de distribuição. Este dispositivo é composto por um microcontrolador ARDUINO UNO Rev3 que faz a aquisição a cada 5 minutos da temperatura e humidade relativa do ar por intermédio de um sensor DHT 11, dispõe de um módulo SIM800L que o dota da capacidade de comunicação em tempo real via GSM. Incorpora ainda uma bateria de 3.7 V - 2600mAh que lhe proporciona uma autonomia energética aproximada de 60 horas. O sistema, ao proceder à aquisição, permite ao produtor um controlo remoto do seu produto durante as etapas mais críticas da cadeia de frio, o transporte e o armazenamento no centro de distribuição. Enquanto que os dados de temperatura e humidade podem ser utilizados pelo produtor para assegurar a qualidade do seu produto no momento de entrega no centro de distribuição, o histórico de localizações permite a otimização das rotas de transporte, visando a extensão do tempo de vida do produto, que por sua vez, se reflete num aumento da receita económica das PMEs e numa redução do alimento desperdiçado

Food Recognition and Ingredient Detection Using Electrical Impedance Spectroscopy With Deep Learning Techniques to Facilitate Human-food Interactions

Food is a vital component of our everyday lives closely related to our health, well-being, and human behavior. The recent advancements of Spatial Computing technologies, particularly in Human-Food interactive (HFI) technologies have enabled novel eating and drinking experiences, including digital dietary assessments, augmented flavors, and virtual and augmented dining experiences. When designing novel HFI technologies, it is essential to recognize different food and beverages and their internal attributes (i.e., food sensing), such as volume and ingredients. As a result, contemporary research employs image analysis techniques to identify food items, notably in digital dietary assessments. These techniques, often combined with AI algorithms, analyze digital food images to extract various information about food items and quantities. However, these visual food analyzing methods are ineffective when: 1) identifying food’s internal attributes, 2) discriminating visually similar food and beverages, and 3) seamlessly integrating with people’s natural interactions while consuming food (e.g., automatically detecting the food when using a spoon to eat). This thesis presents a novel approach to digitally recognize beverages and their attributes, an essential step towards facilitating novel human-food interactions. The proposed technology has an electrical impedance measurement unit and a recognition method based on deep learning techniques. The electrical impedance measurement unit consists of the following components: 1) a 3D printed module with electrodes that can be attached to a paper cup, 2) an impedance analyzer to perform Electrical Impedance Spectroscopy (EIS) across two electrodes to acquire measurements such as a beverage’s real part of impedances, imaginary part of impedances, phase angles, and 3) a control module to configure the impedance analyzer and send measurements to a computer that has the deep learning framework to conduct the analysis. Two types of multi-task learning models (hard parameter sharing multi-task network and multi-task network cascade) and their variations (with principal component analysis and different combinations of features) were employed to develop a proof-of-concept prototype to recognize eight different beverage types with various volume levels and sugar concentrations: two types of black tea (LiptonTM and TwiningsTM English-Breakfast), two types of coffee (StarbucksTM dark roasted and medium roasted), and four types of soda (regular and diet coca-cola, and regular and diet Pepsi). Measurements were acquired from these beverages while changing volume levels and sugar concentrations to construct training and test datasets. Both types of networks were trained using the training dataset while validated with the test dataset. Results show that the multi-task network cascades outperformed the hard parameter sharing multi-task networks in discriminating against a limited number of drinks (accuracy = 96.32%), volumes (root mean square error = 13.74ml), and sugar content (root mean square error = 7.99gdm3). Future work will extend this approach to include additional beverage types and their attributes to improve the robustness and performance of the system and develop a methodology to recognize solid foods with their attributes. The findings of this thesis will contribute to enable a new avenue for human-food interactive technology developments, such as automatic food journaling, virtual flavors, and wearable devices for non-invasive quality assessment

Strawberry Cultivation Techniques

Among the berries, strawberries are the most commercially produced and consumed and their production and consumption are increasing in the world due to their enthusiastic aroma, taste, and biochemical properties. Strawberry is belonging to the genus Fragaria, from the family Rosaceae. It is indicated that the homeland of the strawberry is South America (Chile). It is well-known that people living in Asia, Europe, and America commonly use the wild F. vesca. In other regions such as Japan, North China and Manchuria, Europe-Siberia, and America there are different ecogeographic zones where alternative species are clustered. Despite its origins in the Pacific Northwest region of North America, F. ananassa is now grown all over the world. Strawberry is one of the most widespread berry species grown in almost every country including high altitudes of tropical regions, and subtropical and temperate areas. In this chapter, we aimed to offer new perspectives on the future of strawberry cultivation techniques by analyzing recent academic studies on strawberry production

Applications of AI and IoT for Advancing Date Palm Cultivation in Saudi Arabia

Date palm cultivation is an essential part of Saudi Arabia’s economy. However, it faces several challenges: water scarcity, improper farm management, pests and diseases, inadequate farming practices, processing and marketing, and labor shortages. Artificial intelligence (AI) and the Internet of Things (IoT) can help enrich crop management, enable predictive analytics, increase efficiency, and promote sustainability in date palm cultivation. Recently, interest in this sector has begun by applying the latest precision engineering technologies integrated with AI and IoT techniques to address these challenges. This chapter aims to provide an overview of the applications of AI and IoT-based technologies, such as sensors, ML algorithms, and data analytics, and their potential benefits and challenges in supporting date palm cultivation in Saudi Arabia. Specifically, the applications of AI and IoT in smart precision irrigation, smart systems, cold storage management, pest infestation prediction, and date fruit quality optimization. In addition, the potential economic and environmental benefits of using AI and IoT in date palm cultivation in Saudi Arabia and the challenges that need to be addressed to realize these benefits fully. The chapter provides insight into the latest developments and future directions for AI and IoT in date palm cultivation, providing valuable information for researchers and policymakers

Visualizing anthocyanins: Colorimertic analysis of blue maize

Anthocyanin, vibrant pigments found in a wide range of plants, including maize, contribute to the red, blue, and purple hues observed in fruits, vegetables, and grains. The inherent color variations in maize, including natural shades of purple, red, blue, and even rainbow colors, pose a significant challenge in accurately assessing maize maturity. This study recognizes the importance of visualizing the distinct blue purplish anthocyanin coloration to determine the optimal harvest time for blue maize, particularly among small-scale producers. To address this crucial need, this research project presents the development of the MaizeMeter, an advanced colorimeter specifically designed to analyze maize color based on anthocyanin pigmentation. Leveraging the power of Internet of Things (IoT) implementation, the MaizeMeter provides real-time monitoring and interpretation of anthocyanin color values. The proposed methodology encompasses the calibration of the color sensor and the prototyping of the MaizeMeter, culminating in the establishment of a comprehensive database of anthocyanin color profiles in blue maize. The generated anthocyanin color database by the MaizeMeter will serve as a vital tool for small-scale farmers and researchers, enabling more efficient and accurate assessment of maize maturity in the future

Visualizing anthocyanins : Colorimertic analysis of blue maize

Anthocyanin, vibrant pigments found in a wide range of plants, including maize, contribute to the red, blue, and purple hues observed in fruits, vegetables, and grains. The inherent color variations in maize, including natural shades of purple, red, blue, and even rainbow colors, pose a significant challenge in accurately assessing maize maturity. This study recognizes the importance of visualizing the distinct blue purplish anthocyanin coloration to determine the optimal harvest time for blue maize, particularly among small-scale producers. To address this crucial need, this research project presents the development of the MaizeMeter, an advanced colorimeter specifically designed to analyze maize color based on anthocyanin pigmentation. Leveraging the power of Internet of Things (IoT) implementation, the MaizeMeter provides real-time monitoring and interpretation of anthocyanin color values. The proposed methodology encompasses the calibration of the color sensor and the prototyping of the MaizeMeter, culminating in the establishment of a comprehensive database of anthocyanin color profiles in blue maize. The generated anthocyanin color database by the MaizeMeter will serve as a vital tool for small-scale farmers and researchers, enabling more efficient and accurate assessment of maize maturity in the future

Adaptive physiological responses of tomato plants to combined abiotic stress and biostimulant application.

Tomato is one of the most cultivated crops in the world. Due to the antioxidant and anti-cancer properties of lycopene and other compounds, tomato consumption and production is still on the rise. However, its productivity is greatly compromised by a wide range of abiotic stresses and, therefore, the production of stress-tolerant tomato lines and the identification of novel strategies to increase stress tolerance are key challenges for modern agriculture. The presence of adverse environmental factors such as extreme temperatures, salinity or drought causes morphological, physiological and biochemical changes in tomato plants. The biotechnological and agronomical methods used to increase tomato tolerance to various abiotic stresses include the selection of tolerant genotypes and the use of management practices, such as the application of biostimulants. An in-depth study of the physiological responses of tomato plants to abiotic stress and to biostimulant application was performed. The first aim of this research was to investigate the mechanisms that control plant physiological responses to high temperature stress, drought and combined stresses in different tomato genotypes in order to select those tolerant to abiotic stress. A second aim was the identification of strategies to increase tomato growth and final yield under stress. To this aim, we focused on the protein hydrolysate-based biostimulant and investigated its ability to induce better performances in plants under heat, drought and combined stresses in different environmental conditions. As for the first aim, the first part of this research focused on the eco-physiological screening of several tomato genotypes under elevated temperatures (Chapter 2) that allowed the selection of two genotypes potentially tolerant to heat stress (LA3120, E42). The response of the selected genotypes was further tested in a growth chamber to better investigate their responses to combined stresses, such as high temperatures and water shortage (50% of water requirements) (Chapter 3). As for the second aim of this thesis, the response of different genotypes grown in open field under elevated temperatures after application of a protein hydrolysate-based biostimulant was analysed. This additional analysis allowed to demonstrate that the use of the biostimulant by fertigation led to better plant performances under elevated temperatures (Chapter 4). The adaptive physiological response to single and combined stresses and biostimulant treatment was also investigated under controlled conditions in the selected genotypes E42 and LA3120 (Chapter 5). Considering that plants grown in open field are subjected to a higher number of different variables compared with controlled environments, in the final part of this work, the performances of the genotype E42 exposed to water deficit and treated with the novel protein hydrolysate biostimulant were evaluated under open field conditions. This final experiment allowed to demonstrate the positive effect of the biostimulant on final yields under water deficit and in different field trails (Chapter 6). Our findings contributed to a better understanding of the morphological and physiological effects of combined abiotic stresses on tomato crop. Additionally, the results obtained in this thesis further demonstrate the effects of protein hydrolysate-based biostimulants on improving plant performances under abiotic stresses. Altogether, results obtained in this thesis provide novel solutions to increase final yields in plants facing the future climate changes