Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets dagger

In This Paper, A Monitoring System Of Agricultural Production Is Modeled As A Data Fusion System (Data From Local Fairs And Meteorological Data). The Proposal Considers The Particular Information Of Sales In Agricultural Markets For Knowledge Extraction About The Associations Among Them. This Association Knowledge Is Employed To Improve Predictions Of Sales Using A Spatial Prediction Technique, As Shown With Data Collected From Local Markets Of The Andean Region Of Ecuador. The Commercial Activity In These Markets Uses Alternative Marketing Circuits (Cialco). This Market Platform Establishes A Direct Relationship Between Producer And Consumer Prices And Promotes Direct Commercial Interaction Among Family Groups. The Problem Is Presented First As A General Fusion Problem With A Network Of Spatially Distributed Heterogeneous Data Sources, And Is Then Applied To The Prediction Of Products Sales Based On Association Rules Mined In Available Sales Data. First, Transactional Data Is Used As The Base To Extract The Best Association Rules Between Products Sold In Different Local Markets, Knowledge That Allows The System To Gain A Significant Improvement In Prediction Accuracy In The Spatial Region Considered.This work was supported in part by Project MINECO TEC2017-88048-C2–2-R, Salesian Polytechnic University of Quito-Ecuador and by Commercial Coordination Network, Ministry of Agriculture and Livestock, Ecuado

Spatiotemporal analysis of gapfilled high spatial resolution time series for crop monitoring.

[ES] La obtención de mapas fiables de clasificación de cultivos es importante para muchas aplicaciones agrícolas, como el monitoreo de los campos y la seguridad alimentaria. Hoy en día existen distintas bases de datos de cobertura terrestre con diferentes escalas espaciales y temporales cubriendo diferentes regiones terrestres (por ejemplo, Corine Land cover (CORINE) en Europa o Cropland Data Layer (CDL) en Estados Unidos (EE.UU.)). Sin embargo, estas bases de datos son mapas históricos y por lo tanto no reflejan los estados fenológicos actuales de los cultivos. Normalmente estos mapas requieren un tiempo específico (anual) para generarse basándose en las diferentes fenologías de cada cultivo. Los objetivos de este trabajo son dos: 1- analizar la distribución espacial de los cultivos a diferentes regiones espaciales para identificar las áreas más representativas. 2- analizar el rango temporal utilizado para acelerar la generación de mapas de clasificación. El análisis se realiza sobre el contiguo de Estados Unidos (CONUS, de sus siglas en inglés) en 2019. Para abordar estos objetivos, se utilizan diferentes fuentes de datos. La capa CDL, una base de datos robusta y completa de mapas de cultivo en el CONUS, que proporciona datos anuales de cobertura terrestre rasterizados y georeferenciados. Así como, datos multiespectrales a 30 metros de resolución espacial, preprocesados para rellenar los posibles huecos debido a la presencia de nubes y aerosoles en los datos. Este conjunto de datos ha sido generado por la fusión de sensores Landsat y Moderate Resolution Imaging Spectroradiometer (MODIS). Para procesar tal elevada cantidad de datos se utilizará Google Earth Engine (GEE), que es una aplicación que procesa la información en la nube y está especializada en el procesamiento geoespacial. GEE se puede utilizar para obtener mapas de cultivos a nivel mundial, pero requiere algoritmos eficientes. En este estudio se analizarán diferentes algoritmos de aprendizaje de máquina (machine learning) para analizar la posible aceleración de la obtención de los mapas de clasificación de cultivo. En GEE hay diferentes tipos de algoritmos de clasificación disponibles, desde simples árboles de decisión (decision trees) hasta algoritmos más complejos como máquinas de vectores soporte (SVM) o redes neuronales (neural networks). Este estudio presentará los primeros resultados para la generación de mapas de clasificación de cultivos utilizando la menor cantidad posible de información, a nivel temporal, con una resolución espacial de 30 metros.[EN] Reliable crop classification maps are important for many agricultural applications, such as field monitoring and food security. Nowadays there are already several crop cover databases at different scales and temporal resolutions for different parts of the world (e. g. Corine Land cover in Europe (CORINE) or Cropland Data Layer (CDL) in the United States (US)). However, these databases are historical crop cover maps and hence do not reflect the actual crops on the ground. Usually, these maps require a specific time (annually) to be generated based on the diversity of the different crop phenologies. The aims of this work are two: 1- analyzing the multi-scale spatial crop distribution to identify the most representative areas. 2- analyzing the temporal range used to generate crop cover maps to build maps promptly. The analysis is done over the contiguous US (CONUS) in 2019. To address these objectives, different types of data are used. The CDL, a robust and complete cropland mapping in the CONUS, which provides annual land cover data raster geo-referenced. And, multispectral high-resolution gap-filled data at 30 meter spatial resolution used to avoid the presence of clouds and aerosols in the data. This dataset has been generated by the fusion of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). To process this large amount of data will be used Google Earth Engine (GEE) which is a cloud-based application specialized in geospatial processing. GEE can be used to map crops globally, but it requires efficient algorithms. In this study, different machine learning algorithms will be analyzed to generate the promptest classification crop maps. Several options are available in GEE from simple decision trees to more complex algorithms like support vector machines or neural networks. This study will present the first results and the potential to generate crop classification maps using as less possible temporal range information at 30 meters spatial resolution.Rajadel Lambistos, C. (2020). Análisis espaciotemporal de series temporales sin huecos de alta resolución espacial. Universitat Politècnica de València. http://hdl.handle.net/10251/155879TFG

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems

Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable

Satellite Earth observation to support sustainable rural development

Traditional survey and census data are not sufficient for measuring poverty and progress towards achieving the Sustainable Development Goals (SDGs). Satellite Earth Observation (EO) is a novel data source that has considerable potential to augment data for sustainable rural development. To realise the full potential of EO data as a proxy for socioeconomic conditions, end-users – both expert and non-expert – must be able to make the right decisions about what data to use and how to use it. In this review, we present an outline of what needs to be done to operationalise, and increase confidence in, EO data for sustainable rural development and monitoring the socioeconomic targets of the SDGs. We find that most approaches developed so far operate at a single spatial scale, for a single point in time, and proxy only one socioeconomic metric. Moreover, research has been geographically focused across three main regions: West Africa, East Africa, and the Indian Subcontinent, which underscores a need to conduct research into the utility of EO for monitoring poverty across more regions, to identify transferable EO proxies and methods. A variety of data from different EO platforms have been integrated into such analyses, with Landsat and MODIS datasets proving to be the most utilised to-date. Meanwhile, there is an apparent underutilisation of fusion capabilities with disparate datasets, in terms of (i) other EO datasets such as RADAR data, and (ii) non-traditional datasets such as geospatial population layers. We identify five key areas requiring further development to encourage operational uptake of EO for proxying socioeconomic conditions and conclude by linking these with the technical and implementational challenges identified across the review to make explicit recommendations. This review contributes towards developing transparent data systems to assemble the high-quality data required to monitor socioeconomic conditions across rural spaces at fine temporal and spatial scales

Nondestructive Multivariate Classification of Codling Moth Infested Apples Using Machine Learning and Sensor Fusion

Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested apples. A novel approach was developed to correlate the larval activities to low-frequency vibro-acoustic signals, by capturing the larval activities using a digital camera while simultaneously registering the signal patterns observed in the contact piezoelectric sensors on apple surface. While the larva crawling was characterized by the low amplitude and higher frequency (around 4 Hz) signals, the chewing signals had greater amplitude and lower frequency (around 1 Hz). In objective two and three, vibro-acoustic and acoustic impulse methods were developed to classify CM-infested and healthy apples. In the first approach, the identified vibro-acoustic patterns from the infested apples were used for the classification of the CM-infested and healthy signal data. The classification accuracy was as high as 95.94% for 5 s signaling time. For the acoustic impulse method, a knocking test was performed to measure the vibration/acoustic response of the infested apple fruit to a pre-defined impulse in comparison to that of a healthy sample. The classification rate obtained was 99% for a short signaling time of 60-80 ms. In objective four, shortwave near infrared hyperspectral imaging (SWNIR HSI) in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for the three apple cultivars reaching an accuracy of up to 97.4%. In objective five, the physicochemical characteristics of apples were predicted using HSI method. The results showed the correlation coefficients of prediction (Rp) up to 0.90, 0.93, 0.97, and 0.91 for SSC, firmness, pH and moisture content, respectively. Furthermore, the effect of long-term storage (20 weeks) at three different storage conditions (0 °C, 4 °C, and 10 °C) on CM infestation and the detectability of the infested apples was studied. At a constant storage temperature the detectability of infested samples remained the same for the first three months then improved in the fourth month followed by a decrease until the end of the storage. Finally, a sensor data fusion method was developed which showed an improvement in the classification performance compared to the individual methods. These findings indicated there is a high potential of acoustic and NIR HSI methods for detecting and classifying CM infestation in different apple cultivars

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Advances in Deep Learning Algorithms for Agricultural Monitoring and Management

This study examines the transformative role of deep learning algorithms in agricultural monitoring and management. Deep learning has shown remarkable progress in predicting crop yields based on historical weather, soil, and crop data, thereby enabling optimized planting and harvesting strategies. In disease and pest detection, image recognition technologies such as Convolutional Neural Networks (CNNs) can analyze high-resolution images of crops to identify early signs of diseases or pest infestations, allowing for swift and effective interventions. In the context of precision agriculture, these advanced techniques offer resource efficiency by enabling targeted treatments within specific field areas, significantly reducing waste. The paper also sheds light on the application of deep learning in analyzing vast amounts of remote sensing and satellite imagery data, aiding in real-time monitoring of crop growth, soil moisture, and other critical environmental factors. In the face of climate change, advanced algorithms provide valuable insights into its potential impact on agriculture, thereby aiding the formulation of effective adaptation strategies. Automated harvesting and sorting, facilitated by robotics powered by deep learning, are also investigated, as they promise increased efficiency and reduced labor costs. Moreover, machine learning models have shown potential in optimizing the entire agricultural supply chain, ensuring minimal waste and optimum product quality. Lastly, the study highlights the power of deep learning in integrating multi-source data, from weather stations to satellites, to form comprehensive monitoring systems that allow real-time decision-making

A Machine Learning Framework for the Classification of Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface Reflectance Data

Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types