238 research outputs found
Learning from Data to Optimize Control in Precision Farming
Precision farming is one way of many to meet a 70 percent increase in global
demand for agricultural products on current agricultural land by 2050 at
reduced need of fertilizers and efficient use of water resources. The catalyst
for the emergence of precision farming has been satellite positioning and
navigation followed by Internet-of-Things, generating vast information that can
be used to optimize farming processes in real-time. Statistical tools from data
mining, predictive modeling, and machine learning analyze pattern in historical
data, to make predictions about future events as well as intelligent actions.
This special issue presents the latest development in statistical inference,
machine learning and optimum control for precision farming.Comment: Editorial of "Statistical Tools in Precision Farming", MDPI/Stat
Energy consumption forecasting: a proposed framework
With the development of underdeveloped countries and the digitization of societies,
energy consumption is expected to continue to show high growth in the coming
decades. While there is still a strong focus on fossil fuels for energy generation, the
implementation of energy policies is crucial to gradually shift to renewable sources
and the consequent reduction in CO2 emissions. Buildings are currently the sector that
consumes the most energy.
To contribute for a better energy consumption efficiency, it was proposed a
framework, to be applied to buildings or households, to allow users to know their
energy consumption and the possibility to forecast it.
Different data analysis techniques for time series were used to provide information
to the user about their energy consumption as well as to validate important data
characteristics, namely stationarity and the existence of seasonality, which can have
an impact in the forecasting models.
For the definition of the forecasting models, state of the art was done to identify
used models for energy consumption forecasting, and three models were tested for
both types of data, univariate and multivariate. For the univariate data, the tested
models were SARIMA, Holt-Winters and LSTM as for the multivariate data,
SARIMA with exogenous variables, Support Vector Regression and LSTM. After the
first execution of each model, hyperparameter tuning was done to conclude on the
improvement of the results and the robustness of the models for later application to the
framework.Com o desenvolvimento de países subdesenvolvidos e a digitalização das
sociedades, é esperado que o consumo de energia continue a apresentar um
crescimento elevado nas próximas décadas. Existindo ainda um grande foco em fontes
fósseis para a geração de energia, a implementação de políticas energéticas são cruciais
para a mudança gradual para energias renováveis e consequente redução de emissões
de CO2. Edifícios são atualmente o sector que mais energia consomem.
De forma a contribuir para uma melhor eficiência no consumo de energia foi
proposta uma framework, a aplicar em edifícios ou apartamentos, para possibilitar aos
utilizadores ter um conhecimento do seu consumo de energia bem como a previsão
desse mesmo consumo.
Diferentes técnicas de análise de dados para séries temporais foram utilizadas para
proporcionar informação ao utilizador sobre o seu consumo de energia bem como a
validação de caraterísticas importantes dos dados, nomeadamente a verificação da
estacionariedade e a existência da sazonalidade, que terão impacto no modelo de
previsão.
Para a definição dos modelos preditivos, foi feita uma revisão de literatura sobre
modelos utilizados atualmente para previsão do consumo de energia e testados três
modelos para os dois tipos de dados, univariados e multivariados. Para os dados
univariados os modelos testados foram SARIMA, Holt-Winters e LSTM e para os
dados multivariados SARIMA com variáveis exógenas, Support Vector Regression e
LSTM. Após a primeira execução de cada modelo, foi feita uma otimização dos
modelos para concluir na melhoria dos resultados previstos e na robustez dos modelos
para posterior aplicação na framework
딥러닝 방법론을 이용한 높은 적용성을 가진 수경재배 파프리카 대상 절차 기반 모델 개발
학위논문(박사) -- 서울대학교대학원 : 농업생명과학대학 농림생물자원학부, 2022. 8. 손정익.Many agricultural challenges are entangled in a complex interaction between crops and the environment. As a simplifying tool, crop modeling is a process of abstracting and interpreting agricultural phenomena. Understanding based on this interpretation can play a role in supporting academic and social decisions in agriculture. Process-based crop models have solved the challenges for decades to enhance the productivity and quality of crop production; the remaining objectives have led to demand for crop models handling multidirectional analyses with multidimensional information. As a possible milestone to satisfy this goal, deep learning algorithms have been introduced to the complicated tasks in agriculture. However, the algorithms could not replace existing crop models because of the research fragmentation and low accessibility of the crop models. This study established a developmental protocol for a process-based crop model with deep learning methodology. Literature Review introduced deep learning and crop modeling, and it explained the reasons for the necessity of this protocol despite numerous deep learning applications for agriculture. Base studies were conducted with several greenhouse data in Chapters 1 and 2: transfer learning and U-Net structure were utilized to construct an infrastructure for the deep learning application; HyperOpt, a Bayesian optimization method, was tested to calibrate crop models to compare the existing crop models with the developed model. Finally, the process-based crop model with full deep neural networks, DeepCrop, was developed with an attention mechanism and multitask decoders for hydroponic sweet peppers (Capsicum annuum var. annuum) in Chapter 3. The methodology for data integrity showed adequate accuracy, so it was applied to the data in all chapters. HyperOpt was able to calibrate food and feed crop models for sweet peppers. Therefore, the compared models in the final chapter were optimized using HyperOpt. DeepCrop was trained to simulate several growth factors with environment data. The trained DeepCrop was evaluated with unseen data, and it showed the highest modeling efficiency (=0.76) and the lowest normalized root mean squared error (=0.18) than the compared models. With the high adaptability of DeepCrop, it can be used for studies on various scales and purposes. Since all methods adequately solved the given tasks and underlay the DeepCrop development, the established protocol can be a high throughput for enhancing accessibility of crop models, resulting in unifying crop modeling studies.농업 시스템에서 발생하는 문제들은 작물과 환경의 상호작용 하에 복잡하게 얽혀 있다. 작물 모델링은 대상을 단순화하는 방법으로써, 농업에서 일어나는 현상을 추상화하고 해석하는 과정이다. 모델링을 통해 대상을 이해하는 것은 농업 분야의 학술적 및 사회적 결정을 지원할 수 있다. 지난 수년 간 절차 기반 작물 모델은 농업의 문제들을 해결하여 작물 생산성 및 품질을 증진시켰으며, 현재 작물 모델링에 남아있는 과제들은 다차원 정보를 다방향에서 분석할 수 있는 작물 모델을 필요로 하게 되었다. 이를 만족시킬 수 있는 지침으로써, 복잡한 농업적 과제들을 목표로 딥러닝 알고리즘이 도입되었다. 그러나, 이 알고리즘들은 낮은 데이터 완결성 및 높은 연구 다양성 때문에 기존의 작물 모델들을 대체하지는 못했다. 본 연구에서는 딥러닝 방법론을 이용하여 절차 기반 작물 모델을 구축하는 개발 프로토콜을 확립하였다. Literature Review에서는 딥러닝과 작물 모델에 대해 소개하고, 농업으로의 딥러닝 적용 연구가 많음에도 이 프로토콜이 필요한 이유를 설명하였다. 제1장과 2장에서는 국내 여러 지역의 데이터를 이용하여 전이 학습 및 U-Net 구조를 활용하여 딥러닝 모델 적용을 위한 기반을 마련하고, 베이지안 최적화 방법인 HyperOpt를 사용하여 기존 모델과 딥러닝 기반 모델을 비교하기 위해 시험적으로 WOFOST 작물 모델을 보정하는 등 모델 개발을 위한 기반 연구를 수행하였다. 마지막으로, 제3장에서는 주의 메커니즘 및 다중 작업 디코더를 가진 완전 심층 신경망 절차 기반 작물 모델인 DeepCrop을 수경재배 파프리카(Capsicum annuum var. annuum) 대상으로 개발하였다. 데이터 완결성을 위한 기술들은 적합한 정확도를 보여주었으며, 전체 챕터 데이터에 적용하였다. HyperOpt는 식량 및 사료 작물 모델들을 파프리카 대상으로 보정할 수 있었다. 따라서, 제3장의 비교 대상 모델들에 대해 HyperOpt를 사용하였다. DeepCrop은 환경 데이터를 이용하고 여러 생육 지표를 예측하도록 학습되었다. 학습에 사용하지 않은 데이터를 이용하여 학습된 DeepCrop를 평가하였으며, 이 때 비교 모델들 중 가장 높은 모형 효율(EF=0.76)과 가장 낮은 표준화 평균 제곱근 오차(NRMSE=0.18)를 보여주었다. DeepCrop은 높은 적용성을 기반으로 다양한 범위와 목적을 가진 연구에 사용될 수 있을 것이다. 모든 방법들이 주어진 작업을 적절히 풀어냈고 DeepCrop 개발의 근거가 되었으므로, 본 논문에서 확립한 프로토콜은 작물 모델의 접근성을 향상시킬 수 있는 획기적인 방향을 제시하였고, 작물 모델 연구의 통합에 기여할 수 있을 것으로 기대한다.LITERATURE REVIEW 1
ABSTRACT 1
BACKGROUND 3
REMARKABLE APPLICABILITY AND ACCESSIBILITY OF DEEP LEARNING 12
DEEP LEARNING APPLICATIONS FOR CROP PRODUCTION 17
THRESHOLDS TO APPLY DEEP LEARNING TO CROP MODELS 18
NECESSITY TO PRIORITIZE DEEP-LEARNING-BASED CROP MODELS 20
REQUIREMENTS OF THE DEEP-LEARNING-BASED CROP MODELS 21
OPENING REMARKS AND THESIS OBJECTIVES 22
LITERATURE CITED 23
Chapter 1 34
Chapter 1-1 35
ABSTRACT 35
INTRODUCTION 37
MATERIALS AND METHODS 40
RESULTS 50
DISCUSSION 59
CONCLUSION 63
LITERATURE CITED 64
Chapter 1-2 71
ABSTRACT 71
INTRODUCTION 73
MATERIALS AND METHODS 75
RESULTS 84
DISCUSSION 92
CONCLUSION 101
LITERATURE CITED 102
Chapter 2 108
ABSTRACT 108
NOMENCLATURE 110
INTRODUCTION 112
MATERIALS AND METHODS 115
RESULTS 124
DISCUSSION 133
CONCLUSION 137
LITERATURE CITED 138
Chapter 3 144
ABSTRACT 144
INTRODUCTION 146
MATERIALS AND METHODS 149
RESULTS 169
DISCUSSION 182
CONCLUSION 187
LITERATURE CITED 188
GENERAL DISCUSSION 196
GENERAL CONCLUSION 201
ABSTRACT IN KOREAN 203
APPENDIX 204박
Definition of the CAPRI Core Modelling System and Interfaces with other Components of SEAMLESS-IF
Environmental Economics and Policy,
Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model
This study presents a novel hybrid model that combines two different algorithms to increase
the accuracy of short-term berry yield prediction using only previous yield data. The model integrates both autoregressive integrated moving average (ARIMA) with Kalman filter refinement and neural network techniques, specifically support vector regression (SVR), and nonlinear autoregressive (NAR) neural networks, to improve prediction accuracy by correcting the errors generated by the system. In order to enhance the prediction performance of the ARIMA model, an innovative method is introduced that reduces randomness and incorporates only observed variables and system errors into the state-space system. The results indicate that the proposed hybrid models exhibit greater accuracy in predicting weekly production, with a goodness-of-fit value above 0.95 and lower root mean square error (RMSE) and mean absolute error (MAE) values compared with non-hybrid models. The study highlights several implications, including the potential for small growers to use digital strategies that offer crop forecasts to increase sales and promote loyalty in relationships with large food retail chains. Additionally, accurate yield forecasting can help berry growers plan their production schedules and optimize resource use, leading to increased efficiency and profitability. The proposed model may serve as a valuable information source for European food retailers, enabling growers to form strategic alliances with their customers
Influence of climate change and variability on Coffea arabica in the East African highlands
A thesis submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy (Agroclimatology) at the University of Witwatersrand, 2017.Plant development is inherently linked to meteorological variability. The phenology, distribution and production of crops and wild relatives has already altered in response to climate change. Recent years have produced the warmest mean annual global temperatures since 1880, with 2016 setting the highest record thus far. Such profound changes have sparked investigations into the impact of temperature and rainfall on crop development, particularly those with profound economic importance such as coffee (C. arabica). The crop is a fundamental source of income for smallholder farming communities and governments throughout the tropical highlands. However, the impact of climate change on C. arabica has yet to be quantified using empirical data in East Africa, leaving uncertainty in the cultivable future of the crop. Therefore, the objective of this thesis is to investigate the influence of climate change and variability on C. arabica yields and phenology in East Africa.
Using a spatio-temporal approach, trends and relationships between coffee performance and meteorological variables were analysed at different scales and time periods ranging from the macroclimatic national scale (49 year), to the meso- and microclimatic farm level (3 year) scale, and finally to the microclimatic canopy and leaf level (hourly) scales. Data from all three climatic continua reveal for the first time that temperatures, and particularly rapidly advancing night time temperatures, are having a substantial negative impact on C. arabica yields. Forecasting models based on these biophysical relationships indicate that by the year 2050, smallholder farmers would on average harvest approximately 50% of the yield they are achieving today. Warming night time temperatures are also responsible for advancing ripening and harvest phenology. As a result, bean filling and development time is reduced, thereby potentially resulting in lower quality coffee. Trends in precipitation do not appear to have any substantial impact on C. arabica yields or harvest phenology, however, it is proposed that rainfall would act synergistically with temperatures to influence plant development and other phenological phases such as flowering. Finally, thermography is introduced as a novel complementary technique to rapidly analyse the suitability of different agroecological systems on coffee physiology at the leaf level. High temporal resolution (hourly) data, illustrate the success of the method in variable meteorological and environmental conditions. The findings contribute to advancing the protocol for use at the canopy and plantation level on coffee, so that appropriate microenvironment designs and adaptation mechanisms be put in place to accommodate climatic change.
Avoiding increments in night time temperatures is key to maintaining or improving yields and fruiting development. Farming at higher altitudes and novel agroforestry systems may assist in achieving lower night time temperatures. Importantly, data reveal that careful analysis of various cropping systems, particularly at lower altitudes, is critical for providing suitable microenvironments for the crop.XL201
Big Data Analysis application in the renewable energy market: wind power
Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías
mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar
mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición
de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais
e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos
diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía
de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións.
Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para
proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management
Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling
Deep learning has already been successfully used in the development of decision support
systems in various domains. Therefore, there is an incentive to apply it in other important domains
such as agriculture. Fertilizers, electricity, chemicals, human labor, and water are the components
of total energy consumption in agriculture. Yield estimates are critical for food security, crop
management, irrigation scheduling, and estimating labor requirements for harvesting and storage.
Therefore, estimating product yield can reduce energy consumption. Two deep learning models,
Long Short-Term Memory and Gated Recurrent Units, have been developed for the analysis of
time-series data such as agricultural datasets. In this paper, the capabilities of these models and their
extensions, called Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Units,
to predict end-of-season yields are investigated. The models use historical data, including climate
data, irrigation scheduling, and soil water content, to estimate end-of-season yield. The application
of this technique was tested for tomato and potato yields at a site in Portugal. The Bidirectional
Long Short-Term memory outperformed the Gated Recurrent Units network, the Long Short-Term
Memory, and the Bidirectional Gated Recurrent Units network on the validation dataset. The model
was able to capture the nonlinear relationship between irrigation amount, climate data, and soil
water content and predict yield with an MSE of 0.017 to 0.039. The performance of the Bidirectional
Long Short-Term Memory in the test was compared with the most commonly used deep learning
method, the Convolutional Neural Network, and machine learning methods including a Multi-Layer
Perceptrons model and Random Forest Regression. The Bidirectional Long Short-Term Memory
outperformed the other models with an R2 score between 0.97 and 0.99. The results show that
analyzing agricultural data with the Long Short-Term Memory model improves the performance of
the model in terms of accuracy. The Convolutional Neural Network model achieved the second-best
performance. Therefore, the deep learning model has a remarkable ability to predict the yield at
the end of the season.Project Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).info:eu-repo/semantics/publishedVersio
Smart models to improve agrometeorological estimations and predictions
La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional.
Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural.
Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave.
Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país.
Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural.
Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas.
This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest
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