Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

There are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors with high spatial, spectral and temporal resolutions. However, transforming these raw data into high-quality datasets that could be used for training AI and specifically deep learning models are technically challenging. This paper describes the process and results of synthesizing labelled-datasets that could be used for training AI (specifically Convolutional Neural Networks) models for determining agricultural land use pattern to support decisions for sustainable farming. In our opinion, this work is a significant step forward in addressing the paucity of usable datasets for developing scalable GeoAI models for sustainable agriculture

Detecting Sugarcane Crop Yield using Decision Tree Classifier in the District of Muzaffarnagar

The district of Muzaffarnagar is the highest sugarcane producing district in Uttar Pradesh and therefore is an important industrial district as well. The district is part of Western UP and it shares the problems of the sugar industry elsewhere in the state: unpredictable demands and crop failures. In this context, predicting sugarcane demand and informing its production can turn to be just the key to solve some of the problems the industry faces. The existing crop forecasting method for the cultivation of sugarcane used in UP relies, to a large degree, on subjective details, centred on the expertise of engineers in the sugar and alcohol field and on information on input demand in the supply chain. The measurement of the utility of the sample detection using NDVI images from the SPOT sensor used in the sensor's determination over the ECMWF model was possible to infer the official productivity data reported in the previously selected municipalities and harvest. Significant features of the municipal productivity of a given village is listed in a decision tree, and out of the combinations of attributes the corresponding municipal productivity is rated as "Normal" on the average urban productivity scale. Using data from the NDVI time-series between 2013 to 2020, we can discern the three classes of productivity in the meanwhile. Findings indicate that productivity in January ranked as less than mean, mean, and more than mean. The findings were more successful for the class Vegetation, the participants of which were permitted to conclude about the pattern of the average federal productivity prior to

Application of new science tools in integrated watershed management for enhancing impacts.

Not AvailableApplication of new science tools in rainfed agriculture opens up new vistas for development through IWMPs. These tools can help in improving the rural livelihoods and contributing substantially to meet the millennium development goals of halving the number of hungry people by 2015 and achieving food security through enhanced use efficiency of scarce natural resources such as land and water in the tropical countries. Till now rainfed areas of the SAT did not get much benefit of new science tools but the recent research using these tools such as simulation modeling, remote sensing, GIS as well as satellite-based monitoring of the natural resources in the SAT has shown that not only the effectiveness of the research is enhanced substantially but also the cost efficiency and impact are enhanced. The remarkable developments in space technology currently offer satellites which provide better spatial and spectral resolutions, more frequent revisits, stereo viewing, and on-board recording capabilities. Thus, the high spatial and temporal resolution satellite data could be effectively used for watershed management and monitoring activities at land ownership level. By using crop simulation modeling approach, yield gap analyses for the major crops in Asia, Africa, and WANA regions revealed that the yields could be doubled with the existing technologies if the improved crop land, nutrient, and water management options are scaled-out. Similarly, technology application domains could be easily identified for better success and greater adoption of the particular technologies considering the biophysical as well as socioeconomic situations. GIS helped in speedy analysis of voluminous data and more rationale decision in less time to target the investments as well as to monitor the large number of interventions in the SAT. The satellite-based techniques along with GIS helped in identifying the vast fallow areas (2 million ha) in Madhya Pradesh during the rainy season. Similarly, 14 million ha rice-fallows in the Indo-Gangetic Plain offer excellent potential to grow second crop on residual soil moisture by using shortduration chickpea cultivars and simple seed priming technology. These techniques are also successfully used for preparing detailed thematic maps, watershed development plans, and continuous monitoring of the natural resources in the country in rainfed areas. Further, such data could be of immense help in tracking the implementation, applying midcourse corrections, and for assessing long-term effectiveness of the program implemented. The synergy of GIS and Web Technology allows access to dynamic geospatial watershed information without burdening the users with complicated and expensive software. Further, these web-based technologies help the field data collection and analysis in a collaborative way. However the availability of suitable software for watershed studies and their management in open GIS platform is very limited. Hence, there is a requirement to strengthen this area through collaborative efforts between various line organizations. Use of ICT in IWMP can bridge the existing gap to reach millions of small farm holders who have no access to new technologies for enhancing agricultural productivity on their farms. Use of smart sensor network along with GIS, remote sensing, Wani Ch006.tex 8/7/2011 19: 41 Page 201 Application of new science tools in integrated watershed management 201 simulation modeling and ICT opens up new opportunities for developing intelligent watershed management information systems. However, it calls for a new partnership involving corporates, development agencies, researchers from various disciplines and most importantly to reach millions of small farm holders in rainfed areas of the world. Application of new science tools in IWMP have helped to substantially enhance productivity as well as income from rainfed agriculture and improved livelihoods of the rural people.Not Availabl

Agricultural land use and associated nutrient flows in peri-urban production systems

Urban and peri-urban agriculture could play a pivotal role as a recipient of organic waste. But recycling and reuse of solid and liquid organic waste in peri-urban agriculture requires planning tools flexible enough to capture the diversity of farming systems and to assess their nutrient status over spatial and temporal scales. This work aims at developing a methodology to determine nutrient flows and budgets at farm, village and communal level of peri-urban agricultural systems of Hanoi, Vietnam, by taking into account spatial and temporal variability of crop and nutrient manageme

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Mehitamata õhusõiduki rakendamine põllukultuuride saagikuse ja maa harimisviiside tuvastamisel

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.This thesis aims to examine how machine learning (ML) technologies have aided significant advancements in image analysis in the area of precision agriculture. These multimodal computing technologies extend the use of machine learning to a broader spectrum of data collecting and selection for the advancement of agricultural practices (Nawar et al., 2017) These techniques will assist complicated cropping systems with more informed decisions with less human intervention, and provide a scalable framework for incorporating expert knowledge of the PA system. (Chlingaryan et al., 2018). Complexity, on the other hand, can be seen as a disadvantage in crop trials, as machine learning models require training/testing databases, limited areas with insignificant sampling sizes, time and space-specificity, and environmental factor interventions, all of which complicate parameter selection and make using a single empirical model for an entire region impractical. During the early stages of writing this thesis, we used a relatively traditional machine learning method to address the regression problem of crop yield and biomass prediction [(i.e., random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] to predicted dry matter (DM) yields of red clover. It obtained favourable results, however, the choosing of hyperparameters, the lengthy algorithms selection process, data cleaning, and redundant collinearity issues significantly limited the way of the machine learning application. We will further discuss the recent trend of automated machine learning (AutoML) that has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unravelling substance problems. However, a present knowledge gap exists in the integration of machine learning (ML) technology with unmanned aerial systems (UAS) and hyperspectral-based imaging data categorization and regression applications. In this thesis, we explored a state-of-the-art (SOTA) and entirely open-source AutoML framework, Auto-sklearn, which was built on one of the most frequently used machine learning systems, Scikit-learn. It was integrated with two unique AutoML visualization tools to examine the recognition and acceptance of multispectral vegetation indices (VI) data collected from UAS and hyperspectral narrow-band VIs across a varied spectrum of agricultural management practices (AMP). These procedures incorporate soil tillage method (STM), cultivation method (CM), and manure application (MA), and are classified as four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Additionally, they have not been thoroughly evaluated and lack characteristics that are accessible in agriculture remote sensing applications. This thesis further explores the existing gaps in the knowledge base for several critical crop categories and cultivation management methods referring to biomass and yield analysis, as well as to gain a better understanding of the potential for remotely sensed solutions to field-based and multifunctional platforms to meet precision agriculture demands. To overcome these knowledge gaps, this research introduces a rapid, non-destructive, and low-cost framework for field-based biomass and grain yield modelling, as well as the identification of agricultural management practices. The results may aid agronomists and farmers in establishing more accurate agricultural methods and in monitoring environmental conditions more effectively.Doktoritöö eesmärk oli uurida, kuidas masinõppe (MÕ) tehnoloogiad võimaldavad edusamme täppispõllumajanduse valdkonna pildianalüüsis. Multimodaalsed arvutustehnoloogiad laiendavad masinõppe kasutamist põllumajanduses andmete kogumisel ja valimisel (Nawar et al., 2017). Selline täpsemal informatsioonil põhinev tehnoloogia võimaldab keerukate viljelussüsteemide puhul teha otsuseid inimese vähema sekkumisega, ja loob skaleeritava raamistiku täppispõllumajanduse jaoks (Chlingaryan et al., 2018). Põllukultuuride katsete korral on komplekssete masinõppemudelite kasutamine keerukas, sest alad on piiratud ning valimi suurus ei ole piisav; vaja on testandmebaase, kindlaid aja- ja ruumitingimusi ning keskkonnategureid. See komplitseerib parameetrite valikut ning muudab ebapraktiliseks ühe empiirilise mudeli kasutamise terves piirkonnas. Siinse uurimuse algetapis rakendati suhteliselt traditsioonilist masinõppemeetodit, et lahendada saagikuse ja biomassi prognoosimise regressiooniprobleem (otsustusmetsa regression, tugivektori regressioon ja tehisnärvivõrk) punase ristiku prognoositava kuivaine saagikuse suhtes. Saadi sobivaid tulemusi, kuid hüperparameetrite valimine, pikk algoritmide valimisprotsess, andmete puhastamine ja kollineaarsusprobleemid takistasid masinõpet oluliselt. Automatiseeritud masinõppe (AMÕ) uusimate suundumustena rakendatakse tehisintellekti, et lahendada põhiprobleemid automatiseeritud algoritmi valiku ja rakendatava pipeline-mudeli hüperparameetrite optimeerimise abil. Seni napib teadmisi MÕ tehnoloogia integreerimiseks mehitamata õhusõidukite ning hüperspektripõhiste pildiandmete kategoriseerimise ja regressioonirakendustega. Väitekirjas uuriti nüüdisaegset ja avatud lähtekoodiga AMÕ tehnoloogiat Auto-sklearn, mis on ühe enimkasutatava masinõppesüsteemi Scikit-learn edasiarendus. Süsteemiga liideti kaks unikaalset AMÕ visualiseerimisrakendust, et uurida mehitamata õhusõidukiga kogutud andmete multispektraalsete taimkatteindeksite ja hüperspektraalsete kitsaribaandmete taimkatteindeksite tuvastamist ja rakendamist põllumajanduses. Neid võtteid kasutatakse mullaharimisel, kultiveerimisel ja sõnnikuga väetamisel nelja kultuuriga põldudel (punase ristiku rohusegu, suvinisu, herne-kaera segu, suvioder). Neid ei ole põhjalikult hinnatud, samuti ei hõlma need omadusi, mida kasutatatakse põllumajanduses kaugseire rakendustes. Uurimus käsitleb biomassi ja saagikuse seni uurimata analüüsivõimalusi oluliste põllukultuuride ja viljelusmeetodite näitel. Hinnatakse ka kaugseirelahenduste potentsiaali põllupõhiste ja multifunktsionaalsete platvormide kasutamisel täppispõllumajanduses. Uurimus tutvustab kiiret, keskkonna suhtes kahjutut ja mõõduka hinnaga tehnoloogiat põllupõhise biomassi ja teraviljasaagi modelleerimiseks, et leida sobiv viljelusviis. Töö tulemused võimaldavad põllumajandustootjatel ja agronoomidel tõhusamalt valida põllundustehnoloogiaid ning arvestada täpsemalt keskkonnatingimustega.Publication of this thesis is supported by the Estonian University of Life Scieces and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.Instituto de Clima y AguaFil: Waldner, François. Université catholique de Louvain. Earth and Life Institute - Environment, Croix du Sud; BelgicaFil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Veron, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Zhang, Miao. Chinese Academy of Science. Institute of Remote Sensing and Digital Earth; ChinaFil: Wu, Bingfang. Chinese Academy of Science. Institute of Remote Sensing and Digital Earth; ChinaFil: Plotnikov, Dmitry. Russian Academy of Sciences. Space Research Institute. Terrestrial Ecosystems Monitoring Laboratory; RusiaFil: Bartalev, Sergey. Russian Academy of Sciences. Space Research Institute. Terrestrial Ecosystems Monitoring Laboratory; RusiaFil: Lavreniuk, Mykola. Space Research Institute NAS and SSA. Department of Space Information Technologies; UcraniaFil: Skakun, Sergii. Space Research Institute NAS and SSA. Department of Space Information Technologies; Ucrania. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Kussul, Nataliia. Space Research Institute NAS and SSA. Department of Space Information Technologies; UcraniaFil: Le Maire, Guerric. UMR Eco&Sols, CIRAD; Francia. Empresa Brasileira de Pesquisa Agropecuária. Meio Ambiante; BrasilFil: Dupuy, Stéphane. Centre de Coopération Internationale en Recherche Agronomique pour le Développement. Territoires, Environnement, Télédétection et Information Spatiale; FranciaFil: Jarvis, Ian. Agriculture and Agri-Food Canada. Science and Technology Branch. Agri-Climate, Geomatics and Earth Observation; CanadáFil: Defourny, Pierre. Université Catholique de Louvain. Earth and Life Institute - Environment, Croix du Sud; Belgic

Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data

Over 90 % of the world's rice is produced in the Asia–Pacific region. Synthetic-aperture radar (SAR) enables all-day and all-weather observations of rice distribution in tropical and subtropical regions. The complexity of rice cultivation patterns in tropical and subtropical regions makes it difficult to construct a representative data-relevant rice crop model, increasing the difficulty in extracting rice distributions from SAR data. To address this problem, a rice area mapping method for large regional tropical or subtropical areas based on time-series Sentinel-1 SAR data is proposed in this study. Based on the analysis of rice backscattering characteristics in mainland Southeast Asia, the combination of spatiotemporal statistical features with good generalization ability was selected and then input into the U-Net semantic segmentation model, combined with WorldCover data to reduce false alarms, finally the 20 m resolution rice area map of five countries in mainland Southeast Asia in 2019 was obtained. The proposed method achieved an accuracy of 92.20 % on the validation sample set, and the good agreement was obtained when comparing our rice area map with statistical data and other rice area maps at the national and provincial levels. The maximum coefficient of determination R2 was 0.93 at the national level and 0.97 at the provincial level. These results demonstrate the advantages of the proposed method in rice area mapping with complex cropping patterns and the reliability of the generated rice area maps. The 20 m annual paddy rice area map for mainland Southeast Asia is available at https://doi.org/10.5281/zenodo.7315076 (Sun et al., 2022b).</p