Precision Irrigation Research Papers - Academia.edu (original) (raw)

2025, Anais do Computer on the Beach

A aquicultura e a producao de organismos aquaticos em cativeiro, tendo como principal atividade a piscicultura, na qual faz-se necessario o monitoramento constante dos niveis ideais de qualidade de agua para reducao de perdas. Neste trabalho buscou-se desenvolver um sistema de analise automatica da agua para aquicultura. O sistema desenvolvido utilizou a plataforma Arduino Mega e sensores de temperatura e pH. Foram realizados testes para verificar a precisao e calibracao dos sensores, bem como para validar a ferramenta desenvolvida. Com base nos resultados obtidos, pode-se constatar a viabilidade do uso do sistema automatizado para analises de pH e temperatura da agua de pisciculturas.

2025

The persistent attacks on farmers by herdsmen, bandits, and unknown gunmen worldwide have significantly disrupted agricultural production and endangered farmers. This paper presents a scalable real-time IoT agricultural farm sensor-based application monitoring system aimed at enhancing agricultural productivity and improving farmer surveillance. Agriculture remains a critical economic driver for many countries. Leveraging the Internet of Things (IoT), this system seeks to boost farm efficiency and yield while minimizing losses and waste. The proposed system comprehensively monitors agricultural farms using smart devices, including sensors, cameras, and internet-enabled technologies. The application was developed using object-oriented languages such as C-Sharp, Python, and Django, integrating data from soil moisture, temperature, and humidity sensors connected to an Arduino microcontroller. Additionally, Django facilitated the development of a crop monitoring system employing sensor data and machine learning algorithms to detect crop diseases and pests. The deployment of this system enhances farmers' confidence by providing real-time monitoring and control of agricultural farms from any location. The application delivers accurate and actionable information to users, particularly farmers, for effective farm management.

2025, How Artificial Intelligence Will Accelerate the Process of Enhancing Sustainability in Agriculture, the Biosphere, and Water Resources within Resource-Limited Environments in the Valencian Community

Presented at the 2023 International Conference on Sustainable Development (ICSD) at Columbia University, this paper introduces an integrated perspective on how artificial intelligence can accelerate the transformation of food systems, water management, and biodiversity in the Valencian Community. It reflects on case studies and interviews with farmers, cooperative leaders, and AI experts, showcasing how local and global synergies are helping bridge digital gaps in small municipalities. The study discusses tools like Digimapa, VisualNAcert, and AgrarIA, and outlines the ethical, environmental, and economic implications of emerging AI applications in agriculture. The work presents a blueprint for inclusive and tech-forward sustainability rooted in local empowerment.

2025, Agronomy

Management zones (MZs) are used in precision agriculture to diversify agronomic management across a field. According to current common practices, MZs are often spatially static: they are developed once and used thereafter. However, the soil-plant relationship often varies over time and space, decreasing the efficiency of static MZ designs. Therefore, we propose a novel workflow for time-specific MZ delineation based on integration of plant and soil sensing data. The workflow includes four steps: (1) geospatial sensor measurements are used to describe soil spatial variability and in-season plant growth status; (2) moving-window regression modelling is used to characterize the sub-field changes of the soil-plant relationship; (3) soil information and sub-field indicator(s) of the soil-plant relationship (i.e., the local regression slope coefficient[s]) are used to delineate time-specific MZs using fuzzy cluster analysis; and (4) MZ delineation is evaluated and interpreted. We illustrate the workflow with an idealized, yet realistic, example using synthetic data and with an experimental example from a 21-ha maize field in Italy using two years of maize growth, soil apparent electrical conductivity and normalized difference vegetation index (NDVI) data. In both examples, the MZs were characterized by unique combinations of soil properties and soil-plant relationships. The proposed approach provides an opportunity to address the spatiotemporal nature of changes in crop genetics × environment × management interactions.

2024, Forests

This study is the beginning of the first long-term study on cork oak irrigation under field conditions, with a structural-functional approach. Cork oaks are currently facing disturbances affecting cork quality and quantity, jeopardizing the future of the economic sector. There is a need for new production techniques that maximize cork oak growth and vitality. In this study, irrigation was implemented in a new intensive cork oak plantations to test the best irrigation volume. The long-term goal is to improve tree growth with minimum water requirements. A 6 ha intensive plantation was installed in Coruche, Portugal. The experimental plot consisted of a subsurface drip fertigation system, buried 40 cm deep; with five independent irrigation treatments. It was tested four irrigation volumes during the dry period-21 weeks in the summer of 2016-ranging from 1.88 mm to 5.62 mm a week. Information on meteorological conditions, soil moisture profile and leaf stomatal conductance were gathered periodically and dendrometric measurements were performed before and after the treatments. Cork oaks' structural and functional parameters were associated with irrigation volume Response to irrigation showed an inflection point in treatment 2, corresponding to a water supply of 3.12 mm per week: below the inflection point, stomatal conductance was reduced by 15% and relative diameter growth at the base was reduced by 10%. Stomatal conductance also showed a positive relationship with soil moisture below the irrigation tubes and with plants' stem diameter. In conclusion, irrigation supply during the period of water stress improved function and structure of cork oaks seedlings under field conditions. These results suggest that irrigation can be a viable alternative to improve cork oak growth in afforestation and reforestation.

2024, Journal of Civil Engineering

With the development of machine learning techniques, agricultural practices have experienced a paradigm change that presents prospects for irrigation strategy optimization. This research uses a machine learning model to provide a novel method of intelligent irrigation scheduling. By utilizing characteristics like crop type, crop days, soil moisture, temperature, and humidity, this model forecasts when irrigation will be required, which helps improve water management in agriculture. We investigated the use of a logistic regression model that was trained on a dataset that includes a variety of crop varieties and environmental factors. The program performs admirably, forecasting irrigation requirements with accuracy and precision. Especially, Crop type's introduction as a categorical variable adds flexibility to different crop-specific irrigation needs. Findings show that irrigation efficiency has significantly increased, proving the model's effectiveness in a variety of situations. This research provides farmers with a data-driven decision support system by illuminating the real-world ramifications of using machine learning into agricultural methods. The results highlight the potential for intelligent irrigation scheduling to be widely used, promoting resource optimization and sustainability in the agriculture industry. Our work advances a robust and flexible strategy for irrigation management as agriculture confronts issues like climate variability and water constraint.

2024, Computers and Electronics in Agriculture

Management of agricultural fields according to spatial and temporal variability is an important aspect of precision agriculture. Precision management relies on division of a field into areas with homogeneous characteristics, management zones (MZs), which are likely affected by multiple, interrelated factors. We present a method, based on machine learning and spatial statistics, to analyze the spatial relationship between a set of variables and determine management zones in a vineyard. The method involves: (1) fitting a model that quantifies the relationship between multiple variables and yield; (2) fitting a model that quantifies the effect of the spatial variability of multiple variables on yield spatial characteristics; and (3) developing a weighted multivariate spatial clustering model as a method to determine MZs. Twelve variables were sampled for 3893 vines in the wine grape vineyard. These variables included soil properties, terrain characteristics, and environmental impact, as well as crop-condition, using indices calculated from remote sensing images. The predictor variables were spatially characterized using hot-spot analysis (Getis Ord Gi * Z-score values) to assess their spatial variability. A gradient boosted regression trees (BRT) algorithm was used to analyze the spatial multivariable effect on yield spatial characteristics. MZs were determined using multivariate K-means clustering, with relative weights given to the predictors, based on their relative influence on yield spatial variability provided by the BRT model. This method was compared to ordinary K-means clustering and K-means with spatial representation of the variables without weights using a dissimilarity index and spatial autocorrelation measures. Model performance was found to be very high and demonstrated that among the evaluated predictors, crop condition indices were the most important regressors for yield and its spatial characteristics. The weighted multivariate spatial clustering was found to perform better in terms of separability of the points and their spatial distribution than the other two clustering techniques. Quantifying yield and its within-field spatial variability, ranking the effects of the predictors and their spatial variabilities, and segmentation of MZs through multivariable spatial analysis, are expected to benefit irrigation management and agricultural decision-making processes. 1. Background Management of agricultural crops is gradually becoming datadriven and data-enabled, with large and diverse amounts of potential information available from crops and soil sampling, sensors, spatial mapping, historical yield measurements, remote sensing products of soils and crops, and weather measurements (Wolfert et al., 2017). These data should be analyzed and synthesized into meaningful information that will facilitate decision-making. The management of agricultural fields, according to spatial and temporal variability of soil, meteorological and crop properties and physiology, is broadly termed precision agriculture (N. Zhang et al., 2002). The basic approach to precision

2024, International journal of engineering research and technology

In most part of the world, water resources are finite and most of the economically viable development has already been implemented. In addition, population growth and the effects of cyclic droughts on irrigated agriculture have put pressure on the available water resources. Such prevailing conditions have the effect of creating an imbalance between the increasing water demand and limited available water supply. Under this perspective, effective planning and management can only be obtained on the basis of reliable information on spatial and temporal patterns of farmer’s water demand, on farming irrigation practices, and on physical and operational features of large-scale irrigation systems. The timely and reliable assessment and monitoring of water resources and systematic exploration and developing new ones is of paramount importance. For this, it is necessary to employ modern methods of surveying, investigations, design, and implementation. Remote sensing and GIS are viewed as an e...

2024, IEEE - International Conference on Disruptive Technologies

This work presents and implements a low-cost irrigation system for smart agriculture that is based on the Internet of Things (IoT). In order to continuously monitor environmental data in real time, the system is equipped with a network of sensors, including pressure, temperature, moisture, and water level sensors. In order to anticipate when irrigation pumps will switch on, machine learning techniques including Artificial Neural Networks (ANN), Decision Trees (DT), Naive Bayes (NB), and Support Vector Machines (SVM) are linked depending on established parameters. The study shows that the ANN model can identify complicated patterns in the agricultural environment with an accuracy of up to 98.33%. Farmers are able to make well-informed decisions quickly thanks to the cloud connectivity and intuitive interface of remote monitoring and control. Because predictive modeling minimizes pump activation delays, it lessens the chance of both over-and under-irrigation. The suggested strategy makes the most use of available water and provides opportunities for precision farming, which is a major step forward for sustainable agriculture. The study's findings demonstrate how well the system uses resources and open the door for the future creation of innovative, scalable agricultural technology.

2024, Research, Society and Development

Instrumento digital para medição de diâmetro florestal usando microcontrolador de baixo custo Digital instrument for diameter forest measurement using low-cost microcontroller Instrumento digital para la medición del diámetro de los... more

2024, Research, Society and Development

Avaliação de desempenho do sensor BH1750FVI (baixo custo) na medida da radiação solar global Performance evaluation of the BH1750FVI sensor (low cost) in the measurement of global solar radiation Evaluación de desempeño del sensor... more

2024

IoT has emerged as one of the technologies that is more vulnerable to assaults due to the rapidly expanding demand and easy connectivity of smart devices and networks. Internet of Things is a key component of smart computing (IoT). The Internet of Things (IoT) is a network of physical devices or things that can communicate and exchange data. A wireless sensor network is a collection of many sensor nodes that communicate wirelessly. It is anticipated that WSN will be included into the "Internet of Things," where sensor nodes join the internet on a dynamic basis and use it for communication and task completion. Adopting smart technologies in agriculture contributes to an increase in crop quality and yield. IoT offers automated systems to assist farmers because they are unable to be present in the agricultural fields around-theclock and because they lack the knowledge necessary to use various equipment to evaluate environmental conditions and keep an eye on activity there. The automated systems run without human interference and alert the farmers to make the proper decisions if they encounter any issues during farming. Intelligent irrigation is a part of safe and smart farming. This application is being considered as follows: With sensors placed all over the ground that can detect temperature, humidity, and soil moisture content, a sizable farming area that is IoT enabled. If an attacker introduces erroneous temperature, humidity, or soil moisture readings into this smart irrigation scenario, it could lead to overwatering or underwatering, which would harm the farm's crop.

2024, 2nd International Conference on Disruptive Technologies (ICDT)

2024, 2024 2nd International Conference on Disruptive Technologies (ICDT)

2024, Book Chapter

Geospatial techniques are being increasingly used in agriculture to optimize production, manage resources, and mitigate climate change impacts. These techniques include GPS, GIS, and remote sensing, which map and monitor crop growth and yield variability, allowing for more efficient application of inputs like fertilizers, water, and pesticides. They also create models that simulate crop growth under different climate scenarios, allowing farmers to plan for future conditions and adapt their practices accordingly. Geospatial data is used to create detailed maps of soil properties, helping farmers make

2024

Geospatial approaches encompass the utilization of advanced technology and instruments for the purpose of acquiring, examining, and administering geographic data. The methodologies encompass the utilization of geographic information systems (GIS), remote sensing, & global positioning systems (GPS) for the purpose of gathering and scrutinizing data pertaining to the Earth's surface. Geospatial approaches play a crucial role in comprehending the effects of climate change on global ecosystems, water resources, as well as meteorological patterns (Kocaman et al., 2019). Climate change is a global phenomenon causing significant changes in Earth's temperature, precipitation, and weather patterns due to human activities like fossil fuel combustion, deforestation, and industrial operations. It impacts the environment, human health, and economy. Geospatial tools are crucial for studying climate change effects, monitoring and analyzing changes in land cover, vegetation, and water resources. The application of geospatial methodologies in climate change studies has led to significant progress in understanding the Earth's consequences (Lu et al., 2021). Importance of Geospatial Techniques in Agriculture Geospatial techniques are increasingly important in agriculture due to their ability to provide accurate data on soil conditions, weather patterns, and crop development. These techniques use satellite images, GPS, and GIS to acquire, analyze, and manage agricultural data, making them crucial in various aspects of the agricultural sector (Ge & Thomasson, 2017). Geospatial tools are being utilized in precision agriculture to allocate resources, improve efficiency, and reduce costs. They enable farmers to monitor crops, identify pests, diseases, and nutrient deficits, and make timely decisions for planting, irrigation, and harvesting. Geospatial methods also help in identifying crop choices resilient to changing weather patterns and assessing regions susceptible to flooding or drought, playing a crucial role in climate change adaptation (Gebbers et al., 2016). This chapter explores the importance of geospatial techniques in the agricultural sector, particularly in the context of climate change. It covers various techniques like remote sensing, GPS, GIS, and UAVs, and their applications in agriculture such as precision farming, crop modeling, soil mapping, water management, climate risk assessment, land use planning, and yield prediction. Geospatial techniques offer operational effectiveness, reduced costs, and increased crop productivity, but also present financial challenges and limited specialized knowledge. The chapter also discusses case studies and future trends in geospatial technology, offering a summary of the benefits and challenges of employing these techniques in agriculture. better decisions about crop selection, planting, and nutrient management. These techniques also help monitor and manage water resources, particularly in areas prone to drought or flooding, by mapping groundwater reserves, analyzing precipitation patterns, and predicting crop water demand. Geospatial data can evaluate climate hazards, guide crop selection and management, and analyze land use patterns, aiding farmers and policymakers in decision-making.

2024, INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING

The agricultural industry has been transformed by the Internet of Things (IoT) revolution, which has brought about cutting-edge solutions to problems relating to crop monitoring and management. This article provides a thorough analysis of a smart agricultural IoT system created for effective crop monitoring of crop moisture and temperature. The objective of this research is to create a comprehensive system that can continuously track temperature and moisture levels in agricultural fields, giving farmers useful information for smarter crop management decisions. To continually monitor and analyse important environmental parameters impacting crop growth, the created system incorporates several IoT components, such as wireless sensors, actuators, and cloud-based data analytics. Temperature and moisture are the two main factors that determine the health, yield, and general quality of the crop. Real-time temperature and moisture data are gathered from various points inside the agricultural field by the use of wireless sensors. The information is subsequently processed and interpreted by sophisticated data analytics algorithms on a cloud-based platform. According to the study, the IoT-based system effectively regulated environmental temperature, resulting in an average decrease to 26.2°C, while concurrently maintaining or improving soil moisture content, evidenced by an increase to 45%. Farmers with this guidance can therefore improve their decision-making processes and ultimately increase agricultural yield, sustainability, and economic consequences by utilising IoT capabilities. Further research and development in this area could revolutionized global agriculture and help ensure food security in the face of shifting climatic circumstances and rising population demands as IoT continues to develop.

2024

Temperature-dependent fecundity and survival data was integrated into a matrix population model to describe relative Drosophila suzukii population increase and age structure based on environmental conditions. This novel modification of the classic Leslie matrix population model is presented as a way to examine how insect populations interact with the environment, and has application as a predictor of population density. As case studies, we examined model predictions in a small fruit production region in Trento province, Italy. In general, patterns of adult D. suzukii trap activity broadly mimicked seasonal population levels predicted by the model using so far only temperature data. The model is advantageous in that it provides stage-specific population estimation, which can potentially guide management strategies and provide unique opportunities to simulate stage-specific management effects such as insecticide applications or the effect of biological control on a specific life-stage. Parole chiave Drosophila suzukii, Modello di sviluppo basato sulla temperatura Keywords Drosophila suzukii, temperature-related population estimation Ringraziamenti Questo lavoro è stato finanziato dalla Provincia Autonoma

2024, Revista Monografias Ambientais

Este trabalho teve por objetivo avaliar e parametrizar os modelos para a estimativa da radiação de onda longa atmosférica (Ld) no Cerrado Mato-grossense, considerando a cobertura do céu nas condições de céu-claro, parcialmente nublado e nublado. Levando-se em conta que medidas da Ld são raras, e que este parâmetro quase sempre é obtido de forma indireta através de vários modelos disponíveis na literatura, optou-se neste trabalho por analisar 107 modelos para a estimativa da Ld. Os dados utilizados neste trabalho foram obtidos de instrumentos instalados em uma torre microclimatológica de 19 metros na fazenda Miranda no município de Santo Antônio de Leverger-MT, entre os meses de junho e julho; e outubro e novembro de 2009 totalizando 5856 medidas. Das estimativas da Ld obtidas a partir destas formulações, aquelas com melhores desempenhos foram as que apresentaram como critérios, os menores erros estatísticos e os maiores índices e coeficientes como os de determinação (R2), de correlação de Pearson (r), de concordância de Wilmott (1982) e o de desempenho de Camargo & Sentelhas (1997). Pode-se observar que, para as condições de céu claro, parcialmente nublado e nublado, os modelos de estimativa da irradiância de ondas longas, em sua formulação original, que apresentaram os melhores índices estatísticos e, portanto, aqueles que melhor se adaptaram ao Cerrado Mato-grossense, para o período estudado foram, respectivamente, Viswanadham & Ramanadham (1970), Idso & Jackson (1969) e Bignami et al. (1995) e após a parametrização os modelos de: Bárbaro et al. (2010); Aubinet (1994)) e Andreas & Ackley (1982).

2024, REVISTA ENGENHARIA NA AGRICULTURA - REVENG

As redes de medições hidrológicas raramente cobrem todos os locais de interesse em uma bacia hidrográfica, havendo, então, uma grande carência de dados ﬂuviométricos. Isso pode ocorrer devido aos custos de aquisição e manutenção dos linígrafos existentes no mercado e/ou por demanda não atendida de pessoal especializado para a observação desses dados. Objetivou-se com este trabalho construir e validar um linígrafo automático de baixo custo, do tipo boia e contrapeso, utilizando a plataforma Arduino. Para isso, utilizou-se, como sensor, um potenciômetro acoplado a um sistema boia e contrapeso; a placa microcontroladora Arduino como sistema de aquisição de dados e o módulo relógio RTC DS1307 para localizar as leituras no tempo. No ambiente Arduino, criou-se um programa com funções de leitura do sinal proveniente do transdutor, conversão do sinal elétrico em nível de água, alocação das leituras no tempo e armazenamento dessas informações em memória de longo prazo. Observou-se que o equi...

2024, WIT Transactions on The Built Environment

Timely and reliable information is critical to organizations managing water resources. Drinking water is one main source of risk when its safety and security is not ensured. Early prediction and mitigation of such risks relies on prediction models that depend on live and historical data. Such data are quite heterogenous in nature, including sensor measurements, satellite imagery and radar readings, unmanned aerial vehicle (UAV) images and videos as well as results of prediction algorithms (flood risk, oil spills etc). AQUA3S is an EU funded project which combines novel technologies in water safety and security, aiming to standardize existing sensor technologies complemented by state-of-the-art detection mechanisms. Sensor networks are deployed in water supply networks and sources, supported by complex sensors for enhanced detection. Sensor measurements are supported by videos from UAVs, satellite images and social media observations from the citizens that report low-quality water in their area also creating social awareness and an interactive knowledge transfer. Semantic representation and data fusion provides intelligent decision support system (DSS) alerts and messages to the public through first responders' mediums. This study presents the data ingestion, integration and harmonization platform that was developed to support the systems of the project, consisting of the necessary APIs, to ingest data, a harmonization layer and a data store layer The data is harmonized and indexed using the NGSI-LD model to make sure information can be indexed and served both is real time through a live context broker, as well as in the form of historical time series through a dedicated historical data service. The data store layer includes provisions for the storage of annotated binary files (images, videos, etc.) as well as georeferenced map layers following OGC protocols such as web feature service (WFS), web map service (WMS), and web coverage service (WCS).

2024, Sensors

This article presents a description of the design, development, and implementation of web-based software and dedicated hardware which allows for the remote monitoring and control of a drip irrigation system. The hardware consists of in-field stations which are strategically distributed in the field and equipped with different sensors and communication devices; a weather station and drip irrigation system complete the setup. The web-based software makes it possible to remotely access and process the information gathered by all the stations and the irrigation controller. The proposed system was implemented in a young olive orchard, located in the province of San Juan, an arid region of Argentina. The system was installed and evaluated during the seasons 2014–2015 and 2015–2016. Four regulated irrigation strategies were proposed in the olive orchard to test its behavior. In this pilot experiment, the precision irrigation system was a useful tool for precisely managing the irrigation pr...

2024, Pesquisa Agropecuaria Brasileira

Resumo Este estudo visou à obtenção das curvas de calibração de um equipamento de TDR (Time Domain Reflectometry) em cinco solos da região de Piracicaba, SP, e testou a adequação da calibração interna do equipamento e das curvas genéricas de calibração. As curvas ajustadas, em cada solo separadamente, apresentaram coeficientes de determinação (R 2) da ordem de 0,99, e a curva ajustada para o conjunto de dados dos cinco solos apresentou R 2 = 0,976. A análise de erros-padrão de estimativa mostrou que as curvas genéricas não se prestam às aplicações mais sensíveis, tais como na determinação absoluta do conteúdo de água do solo. Os testes de comparação entre as curvas ajustadas, a curva genérica e a curva embutida no equipamento mostraram que a primeira é superior às demais. O estudo mostrou, também, que a curva de calibração embutida no equipamento é inadequada para as determinações de umidade nos cinco solos estudados. Termos para indexação: propriedades dielétricas, conteúdo de água do solo, instrumento de medição.

2024, International journal of scientific research and management

The Internet of Things (IoT) allows objects to become producers and users of information generated by themselves, by people or by other systems and also facilitates automating applications. One of the IoT topics is home automation, which allows monitoring and manipulating objects in a house. Because of the growing of IoT, different platforms are emerging to face this technology, such as FIWARE, which provides components and data models that facilitate the development of IoT applications. In this paper, a smart plant prototype based on FIWARE is presented. The plant's environment data is gathered through different sensors, and is sent to a FIWARE component called Orion Context Broker (OCB) in a FIWARE data model structure. The OCB allows to manage and publish the data, so that, the data is available in the cloud ready to be consumed by other users or applications and to automate applications.

2024

Seminário "Metrologia Ambiental aplicada à Instrumentação Meteorológica" UFBA / PEI, Programa de Pós-graduação em Engenharia Industrial. Disciplina: Análise de dados e estimação de parâmetros. Novembro, 2020.

2024

A distribuicao espacial da carga de pressao ao longo dos 432,6m de comprimento de uma linha lateral de um pivo central foi determinada a partir de valores medidos, na entrada e na extremidade da linha lateral, e combinados com dados de altitude proveniente de uma imagem SRTM do rastro das oito torres de sustentacao da linha lateral. Para fins de validacao da estimativa da distribuicao espacial da carga de pressao na linha lateral movel, em 18 diferentes posicoes angulares, foram medidos valores de carga de pressao em seis pontos distintos do seu comprimento. Diferencas significativas, ao nivel de 5% do teste de medias “t de student”, foram observadas quando valores de carga de pressao estimados com dados de altitude do SRTM foram comparados com os valores medidos em campo. Apos a confeccao de mapas tematicos verificou-se coincidencia no posicionamento das regioes de ocorrencia dos valores mais elevados, como tambem dos menores valores de carga de pressao. O estudo demonstrou que o u...

2024

Because there is more demand for freshwater around the world and the world's population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world's current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.

2024, Agriculture

West Tennessee’s supplemental irrigation management at a field level is profoundly affected by the spatial heterogeneity of soil moisture and the temporal variability of weather. The introduction of precision farming techniques has enabled farmers to collect site-specific data that provide valuable quantitative information for effective irrigation management. Consequently, a two-year on-farm irrigation experiment in a 73 ha cotton field in west Tennessee was conducted and a variety of farming data were collected to understand the relationship between crop yields, the spatial heterogeneity of soil water content, and supplemental irrigation management. The soil water content showed higher correlations with soil textural information including sand (r = −0.9), silt (r = 0.85), and clay (r = 0.83) than with soil bulk density (r = −0.27). Spatial statistical analysis of the collected soil samples (i.e., 400 samples: 100 locations at four depths from 0–1 m) showed that soil texture and soi...

2023, Anais do VI Workshop de Computação Urbana (CoUrb 2022)

Sensores de baixo custo de material particulado (MP-BC) vêm sendo estudados ao redor do mundo como alternativa viável às custosas estações de referência para monitoramento de qualidade do ar. Porém, sensores de MP-BC são imprecisos e sujeitos a incertezas, sofrendo com as condições do ambiente em que operam. Este trabalho analisa o impacto da temperatura e umidade em 5 sensores MP-BC durante um período de 4 meses. Os sensores MP-BC são comparados com um equipamento de referência visando propor uma metodologia para selecionar o sensor de MP-BC que comporá uma estação de baixo custo de qualidade do ar no contexto de cidades inteligentes. Resultados demostram que os sensores avaliados apresentam maior correlação com o equipamento de referência quando são retirados da amostra leituras em que temperatura e umidade são críticas.

2023

The modern agriculture is based on the application of techniques, methodologies and equipment that optimize their processes, thus increasing agricultural production, reducing costs and interfering less in nature. An important area of research in Agricultural Engineering is the development and use of equipment and sensors electronic to support increased agricultural productivity. This work presented a contribution to irrigation through the development and use of free software and hardware for direct measurements of soil moisture and temperature values during the plant cycle, thus allowing optimize the use of water in the process. In the system proposal, four moisture sensors were used, one resistive and three capacitive. The research was carried out in the laboratory and the soil used in the experiment was collected at the Experimental Nucleus of Agricultural Engineering of the State University of the West of Paraná. The soil was characterized as typical Distroferric Red Latosol and very clayey texture (66%). The soil was discarded and oven dried, then divided into 20 containers with addition of known water volumes in each. A network of Mesh-type sensors was developed to read and transmit data read to a single Gateway. The sensor node was designed and built with an Arduino Nano, NRF24L01 radio, capacitive sensors of type SHT20 and DHT22 in addition to FC-28 that is resistive. The system also featured a Real Time Clock DS1302, three photovoltaic cells and battery charger circuit. The Gateway circuit that connects the system to the internet was built with an Arduino Uno. Domoticz software was used to store the data and make it available on a server connected to the Internet. The data were obtained from the sensors placed in the containers and one of the results was the cubic modeling of the relationship between each of the sensors, the TDR and the greenhouse method. The values of the coefficient of statistical determination obtained show that the models that best explain the relation between the values obtained by the greenhouse method are the TDR and the resistive sensor, although the other sensors also presented a good coefficient of determination. The consumption of the sensor node board is 168 mW and the distance tested between devices up to 100 m showed that there was no loss of the data packet.

2023

A agricultura moderna esta baseada na aplicacao de tecnicas, metodologias e equipamentos que otimizem os seus processos, aumentando, assim, a producao agricola, reduzindo custos e interferindo menos na natureza. Uma area importante de pesquisa na Engenharia Agricola e o desenvolvimento e a utilizacao de equipamentos e sensores eletronicos para apoiar o aumento da produtividade agricola. Este trabalho apresentou uma contribuicao para a Irrigacao atraves do desenvolvimento e uso de software e hardware livre para medicoes diretas das grandezas de umidade e temperatura no solo durante todo o ciclo da planta, permitindo assim que sistemas otimizem a utilizacao da agua no processo. Na proposta do sistema foram utilizados quatro sensores de umidade, um resistivo e tres capacitivos. A pesquisa foi realizada em laboratorio e o solo utilizado no experimento foi coletado no Nucleo Experimental de Engenharia Agricola da Universidade Estadual do Oeste do Parana. O solo foi caracterizado como Lat...

2023, Applied Engineering in Agriculture

Highlights Unmanned aerial systems (UAS) are able to provide data for precision irrigation management. Improvements are needed regarding UAS platforms, sensors, processing software, and regulations. Integration of multi-scale imagery into scientific irrigation scheduling tools are needed for technology adoption. . Several research institutes, laboratories, academic programs, and service companies around the United States have been developing programs to utilize small unmanned aerial systems (sUAS) as an instrument to improve the efficiency of in-field water and agronomical management. This article describes a decade of efforts on research and development efforts focused on UAS technologies and methodologies developed for irrigation management, including the evolution of aircraft and sensors in contrast to data from satellites. Federal Aviation Administration (FAA) regulations for UAS operation in agriculture have been synthesized along with proposed modifications to enhance UAS cont...

2023, Arabian journal for science and engineering

Agriculture is undoubtedly one of the biggest and most important professions in the world. Optimization of agriculture and aiming gradually and extensively toward smart agriculture are the need of the hour. IOT (Internet of Things) technology has already been successful in easing people's lives with its wide range of applications in almost all arenas. In this paper, our work takes the help of IOT devices, wireless sensor network (WSN) and AI techniques and combines them for faster and effective recommendation of suitable crops to farmers based on a list of factors such as temperature, annual precipitation, total available land size, past crop grown history and other resources. Additionally, detection of unwanted plants on crops, namely weed detection, is implemented with frame-capturing drone and deep learning methods. Naïve Bayes algorithm for crop recommendation based on several factors detected by WSN sensor nodes has been used, resulting in an accuracy of 89.29%, which has proved to be better than several other discussed algorithms in the paper, like regression or support vector machine. Deep learning using neural network successfully identifies weeds present in a specific area of crop growth extending an additional protective measure to farmers. The comprehensive application developed for farmers not only reduces the physical hardship and time spent on different agricultural activities, but also increases the overall land yield, reduces possibility of losses due to failure of crops in a particular soil and lessens the chances of damage caused to crops by weeds.

2023, Engenharia Agrícola

Com o advento da eletrônica e a disponibilidade de "software" de processamento, vários tipos de transdutores têm sido testados, visando à determinação da umidade do solo. O uso desses transdutores tem por objetivo a otimização do consumo de água e o conseqüente retorno econômico da atividade da agricultura irrigada. No Laboratório de Hidráulica do Departamento de Engenharia Agrícola da Universidade Federal do Ceará, construiu-se um dispositivo para calibração de sensores de umidade do solo, a sua calibração e a determinação de suas principais propriedades, como precisão e cargas mínima e máxima. O dispositivo consistiu numa torre em aço 1020, com 3,0 m de altura, no topo da qual se montou uma balança de braços. Em um dos braços da balança, colocou-se uma amostra de solo para a inserção dos sensores de umidade e, no outro, uma célula de carga para medir a variação de massa de água na amostra de solo. Foi implementado um circuito eletrônico para permitir a interface da célul...

2023, Internet of Things and Cloud Computing

The major challenge of irrigation farming is not having full control over the activities on the farmland and its unpredictable environment factors which in most cases, if not well managed, brings about low agricultural productivity. In this paper, as against the traditional manual control procedures which are time consuming, labor expensive, and most time led to taking bad key decisions concerning the three important environmental factors vis: temperature, humidity, and moisture of the farmland, we presents the design and implementation of farm management support system using WSN to sense and sent SMS about these three important farm field parameters to farmers using ATMEGA 328 controller. The system is designed to allow remote tracking of the parameters via an IoT cloud computing platform (ThingSpeak) and perform some Statistical analysis like temperature humidity variation and difference at a particular point in time on the sensed data using matlab. The developed system is scalable to incorporate the tracking of many other parameters and actions such as soil fertility, required soil nutrient, or trigger actions such as releasing water valve or activate alarming unit if certain parameter/s cross defined threshold. This research shows the importance of using wireless sensor network in precision farm field as the system solves the problem of continuous monitoring of data acquisition.

2023, IEEE Access

Wireless sensor networking is being used extensively in agricultural activities to increase productivity and reduce losses in various ways. The greenhouse simplifies the concept of planting, which has several benefits in agriculture. In agricultural models, soil pH sensors and gas sensors are commonly used. These sensors are applicable in various Internet of Things (IoT) integrated agricultural activities. The paper discusses the hardware design and working of the proposed model. In addition, various agricultural models used for evapotranspiration are also explained. The key factors such as congestion control are evaluated using the Penman-Monteith equation. This paper focuses on implementing more than two references parameters like evapotranspiration and humidity under different conditions, which aids in splitting the relationship evenly by the number of sources. Furthermore, the paper shows the implementation done with MATLAB and values are adjusted using the code. The paper claims to achieve similar variations with the same source value, validating the proposed model's efficiency and fairness. In an optimal region, these schemes also demonstrate higher throughput and lower delay rates. The improved packet propagation through the IoT network is demonstrated using visualization tools, and the feedback is computed to determine the overall access amount (A1 + A2) obtained. The experimental results show that the propagation rate is 1.24, more significant than the link capacity value. The claims are verified by showing the improved congestion control as it outperforms different parameters, considering an additive increase condition by 0.3% and multiplicative decrease condition by 1.2 %.

2023, Sustainable Regional Planning [Working Title]

Data-based farming facilitates the zonal management (Smart-FIELD) in the smart farm (SF) vineyards. The classic SCADA for data acquisition and processing system, used especially in rapid industrial processes, can also be adapted for the slow processes (during a vegetation period) of horticultural crops (including the vineyards). The Smart-SCADA core software activities will develop through AI modules the CPS technique for integrating IoT devices with operational applicability in vineyards SF. The Web Smart-SCADA core interconnection with the European platforms in use (UTOPIA, ATLAS, DEMETER, PANTHEON, SmartAgriHubs, …) will add value in the transparency of the traceability of the F2F value chain of grapes.

2023

1. Introdução A curva de retenção da água no solo é uma relação entre a umidade volumétrica e a tensão matricial do solo. Essa relação varia amplamente de solo para solo e tal variação depende de fatores ligados aos valores de tensão superficial. Para baixos valores (0 a 1 bar) a dependência maior é em relação à capilaridade e à distribuição dos tamanhos de poros, portanto, fortemente da estrutura do solo. Para valores maiores, a dependência maior é da adsorção, ou seja, mais da textura e da superfície específica que da estrutura. O formato da curva de retenção é fortemente afetado pela textura do solo. Quanto maior for a fração argila, mais gradual será a inclinação da curva. Isto deve-se à distribuição mais uniforme dos poros, contendo mais água adsorvida. Numa textura mais arenosa, os poros são grandes e para uma dada sucção esvaziam-se rapidamente, fazendo com que a inclinação da curva seja

2023, Engenharia Agrícola

2023, Taylor and Francis group

Irrigation is the artificial application of water to crops to supply moisture. With rising drought indices and rapid population expansion, the need for irrigation water for food production is growing. Nonetheless, irrigated agriculture is battling several issues that have resulted in poor performance, inefficient water usage, and low crop water production. Improving water use efficiency in irrigated agriculture necessitates using technology that decreases water losses, matches available supplies to demand, and tracks performance. Geographical Information Systems (GIS) and Remote Sensing (RS) allow for effectively managing water and land resources for irrigation. Current GIS and RS uses in irrigation systems are covered in this study, covering land suitability for irrigation, crop water needs, irrigation scheduling, performance evaluation, and other related applications. The future potential of GIS and RS applications for sustainable irrigation water management are highlighted. This paper offers relevant information for researchers, irrigators, and policymakers on using GIS and RS in irrigation water management and how technological improvements will change irrigation water management to enhance water usage efficiency.

2023

Irrigation using treated wastewater (TWW) could provoke land degradation and heavy metals’ accumulation. The current study selected two irrigated areas with treated wastewater along Bahr El-Baqar drain in Egypt. The first area (zone A) has been receiving treated wastewater for a period between 10 15 years. The second area (zone B) has been receiving treated wastewater for more than 20 years. Vegetation behavior was monitored using Enhanced Vegetation Index. TWW was of grade “C” according to the Egyptian Code (ECP 501-2015). Zone B suffered from significant loss in fertility and noticeable decrease in vegetation compared to zone A. Crops in zone B had lower heavy metal contents in shoots and roots compared to those of zone A probably due to soil alkalinity. Nevertheless, heavy metal concentrations in rice grains were higher in zone B than in zone A reflecting potential hazard on human health. In conclusion, irrigation using low-quality water negatively affected vegetation performance...

2023, Proceedings of the New Zealand Grassland Association

Recent unprecedented demands on freshwater for irrigation have led to over-allocations and restrictions. Variable rate irrigation (VRI) aims to optimise scheduling according to soil differences using irrigation prescription maps coupled... more

2023, AgriEngineering

Understanding the spatial variability of factors that influence crop yield is essential to apply site-specific management. The present study aimed to evaluate apparent soil electrical conductivity (ECa) in two fields (A = rainfeed; B = central-pivot irrigation), based on delimited management zones (MZs). In each MZ, the soil density (Sd) was characterized at two soil depths, and whether the delimitation of MZs, based on the spatial variability of ECa, was able to identify regions of the field with different Sd was assessed. In general, MZs with the highest mean value of ECa also presented the highest mean values of Sd. The highest Sd values were observed in the 0.1–0.2 m layer, regardless of the studied area. Regardless of soil texture, the proposed ECa was able to detect in-field differences in Sd. The delimitation of MZs, based on the spatial variability of ECa mapping, was able to differentiate the mean values of Sd between MZ 1 (1.53 g cm−3) and MZ 2 (1.67 g cm−3) in field A, in...

2023, International Journal of Healthcare Information Systems and Informatics

Agriculture plays a vital role in India's economy. 44% of the employment in India is engaged in agriculture and allied activities and it also contributes 17% of the gross value added. As most of the country's people are in the agricultural sector and out of them only a few are literate about how to protect their cultivation ultimately gives rise to severe problems like a low economy in the sector and starvation for the nation. The job of this research is to help the farmers to save crops from disease. The authors came with the thought of combining a pattern recognition method and an image processing technique. The system allows a farmer to follow a particular pattern of growing crops so that threats will be analyzed earlier. Combining this with the power of Internet of Things, the authors can automate the process without the need for human resources. This research can ultimately make the agriculture process faster and farmers can cultivate more in a less amount of time.

2023, IRRIGA

Duas sondas de capacitância (PR2/6, Delta-T Devices) foram calibradas para um Latossolo Vermelho Distroférrico, no Campus da UFLA, no município de Lavras-MG. Tubos de acesso foram instalados para monitoramento do conteúdo de água, de modo que abriu-se uma trincheira nas proximidades do tubo para retirada de amostras de solo. Efetuaram-se leituras do equipamento, para intervalos de profundidade de 10, 20, 30, 40, 60 e 100 cm, juntamente com a Para cada uma das profundidades amostradas determinou-se uma equação de calibração. Os maiores ajustes de R 2 obtidos foram 0,877 e 0,793 no sensor de 10 cm, os menores R 2 obtidos foram 0,312 e 0,415 nos sensores de 100 cm. Os sensores das profundidades de 20, 30 e 40 cm nas duas sondas obtiveram valores de R 2 semelhantes nas duas sondas. Os valores dos parâmetros (a 1 e a 0) da equação de calibração variaram de 4,475 a 10 para a 1 e-0,017 a 0,789 para a 0 e de 3,818 a 9,160 para a 1 e-2,849 a 1,148 para a 0 nas sondas 1 e 2 respectivamente. Pode-se concluir que a equação proposta pelo fabricante não se aplica a todos os solos, exigindo assim calibração especifica para cada solo. Palavras-chave: Conteúdo de água, sonda tipo FDR, solos intemperizados.

2023, labplan.ufsc.br

Grain dryers rarely use closed-loop control to measure the moisture content of soybeans grain during the drying process. This is due to the difficulty to obtaining moisture meters that operate in real time and at high temperatures inside the dryer. This paper analyzes the process of measuring the capacitance of the grains in order to determine its moisture and check the influence of external factors such as temperature. It establishes a method to measure the moisture content of grains inside the dryer, based on the dielectric properties of soy. Several measures were obtained to verify the accuracy of this method. The advantage of capacitive measurement method is that it allows the determination of moisture content of soybeans without electrical contact with the grains that are in constant movement inside the dryer.

2023, Arabian Journal for Science and Engineering

2023, IRRIGA

2023, Notas Técnicas

Resumo: Utilizando os dispositivos ESP32 e Raspberry Pi, além dos programas Mosquitto e Node-RED, foi possível elaborar um sistema baseado em IoT capaz de fazer a aquisição de dados, obtidos através de sensores específicos, de variáveis ambientais (temperatura, umidade, pressão e luminosidade) e de energia elétrica (tensão, corrente, potência ativa, fator de potência e frequência). Além disso, o sistema ainda permite que seja feita a atuação remota para modificação do estado de funcionamento do equipamento.