The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

Results in Engineering, Год журнала: 2023, Номер 20, С. 101566 - 101566

Опубликована: Ноя. 3, 2023

The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches resource (WRM) struggle with real-time data acquisition, analysis, intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) Big Data Analytics (BDA) at the forefront have potential revolutionize way managed. This paper reviews current applications AI BDA in WRM, highlighting their capacity overcome existing limitations. It includes investigation technologies, such as machine learning deep learning, diverse quality monitoring, allocation, demand forecasting. In addition, review explores role resources, elaborating on various sources that can be used, remote sensing, IoT devices, social media. conclusion, study synthesizes key insights outlines prospective directions for leveraging optimal allocation.

Язык: Английский

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Temperature‐smart plants: A new horizon with omics‐driven plant breeding

Physiologia Plantarum, Год журнала: 2024, Номер 176(1)

Опубликована: Янв. 1, 2024

Abstract The adverse effects of mounting environmental challenges, including extreme temperatures, threaten the global food supply due to their impact on plant growth and productivity. Temperature extremes disrupt genetics, leading significant issues eventually damaging phenotypes. Plants have developed complex signaling networks respond tolerate temperature stimuli, genetic, physiological, biochemical, molecular adaptations. In recent decades, omics tools other strategies rapidly advanced, offering crucial insights a wealth information about how plants adapt stress. This review explores potential an integrated omics‐driven approach understanding temperatures. By leveraging cutting‐edge methods, genomics, transcriptomics, proteomics, metabolomics, miRNAomics, epigenomics, phenomics, ionomics, alongside power machine learning speed breeding data, we can revolutionize practices. These advanced techniques offer promising pathway developing climate‐proof varieties that withstand fluctuations, addressing increasing demand for high‐quality in face changing climate.

Язык: Английский

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Harnessing quantum computing for smart agriculture: Empowering sustainable crop management and yield optimization

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 218, С. 108680 - 108680

Опубликована: Фев. 10, 2024

Язык: Английский

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Data-driven agriculture and sustainable farming: friends or foes?

Precision Agriculture, Год журнала: 2023, Номер 25(1), С. 520 - 531

Опубликована: Авг. 15, 2023

Abstract Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It more likely to be achieved by using all the knowledge, technology, resources available, including data-driven agricultural technology precision methods, than relying entirely on human powers of observation, analysis, memory following practical experience. Data collected sensors digested artificial intelligence (AI) can help farmers learn about synergies between domains natural that are key simultaneously achieve sustainability security. In quest for sustainability, some high-payoff research areas suggested resolve critical legal technical barriers as well economic social constraints. These include: development holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop pasture models, extension methods agriculture, exploiting naturally occurring Genotype x Environment Management experiments, innovation business models data sharing regulation reinforcing trust. Public funding needed several identified this paper enable sustainable innovation.

Язык: Английский

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An extensive review on agricultural robots with a focus on their perception systems

Computers and Electronics in Agriculture, Год журнала: 2023, Номер 212, С. 108146 - 108146

Опубликована: Авг. 14, 2023

Язык: Английский

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Application of non-parametric learning method in soil suitability assessment in present day economy

Journal of Infrastructure Policy and Development, Год журнала: 2024, Номер 8(7), С. 4074 - 4074

Опубликована: Авг. 1, 2024

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric algorithms, we explore assessment of soil suitability use under conditions drought stress. Through detailed examination varied datasets, which include parameters like toxicity, terrain characteristics, quality scores, our study offers new insights complexities predicting crops. Our findings underline effectiveness various models, with decision tree approach standing out its accuracy, despite need comprehensive data gathering. Moreover, emphasizes promise merging techniques conventional practices in science, paving way novel contributions to studies practical implementations.

Язык: Английский

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Potato Leaf Disease Classification Using Optimized Machine Learning Models and Feature Selection Techniques

Potato Research, Год журнала: 2024, Номер unknown

Опубликована: Июль 24, 2024

Abstract The diseases that particularly affect potato leaves are early blight and the late blight, they dangerous as reduce yield quality of potatoes. In this paper, different machine learning (ML) models for predicting these analysed based on a detailed database more than 4000 records weather conditions. Some critical factors have been investigated to determine correlations with disease prevalence include temperature, humidity, wind speed, atmospheric pressure. These types data relationships were comprehensively identified through sophisticated means analysis such K -means clustering, PCA, copula analysis. To achieve this, several used in study: logistic regression, gradient boosting, multilayer perceptron (MLP), support vector (SVM), well -nearest neighbor (KNN) both without feature selection. Feature selection methods binary Greylag Goose Optimization (bGGO) applied improve predictive performance by identifying sets pertinent models. Results demonstrated MLP model, selection, achieved an accuracy 98.3%, underscoring role improving model performance. findings highlight importance optimized ML proactive agricultural management, aiming minimize crop loss promote sustainable farming practices.

Язык: Английский

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Smart farming prediction models for precision agriculture: a comprehensive survey

Artificial Intelligence Review, Год журнала: 2022, Номер 56(6), С. 5729 - 5772

Опубликована: Ноя. 9, 2022

Язык: Английский

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Peanut leaf disease identification with deep learning algorithms

Molecular Breeding, Год журнала: 2023, Номер 43(4)

Опубликована: Март 27, 2023

Язык: Английский

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Wasting petabytes: A survey of the Sentinel-2 UTM tiling grid and its spatial overhead

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2023, Номер 202, С. 682 - 690

Опубликована: Июль 27, 2023

Following Landsat's practice, Sentinel-2 multispectral satellite products are delivered as raster images projected onto the Universal Transversal Mercator (UTM) spatial reference system, which divides Earth into 60 longitudinal zones. Locally, this guarantees high accuracy, while also easing interoperability with many regional and governmental datums. On top, product grid uses Military Grid Reference System (MGRS) tiling scheme to facilitate manageable data slices straightforward multitemporal image stacking. Although most convenient for small-area applications, activities a larger geographic scope suffer from approach its overhead, both duplication ambiguity appear along UTM zone overlaps MGRS tile borders. In such areas that covered by multiple incongruent pixels known but just tolerated, their degree has not been measured so far. paper, we illuminate nature patterns of these overlaps, calculate resulting redundancy over global land surface. We found total area is enlarged in definition 33%, value similar simple single-zoned Plate Carrée projection. The number co-located single location ranges 1 up 6, on average more at mid- high-latitudes. With regard archives times big increased energy costs, examined appears suboptimal choice, inducing complexity overhead an unreasonable level. Owing design, e.g., yearly user volume (Level-1C -2A) inflated petabyte, entailing cascading downstream costs storage, bandwidth, computing.

Язык: Английский

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