Insights for improving bacterial blight management in coffee field using spatial big data and machine learning

Alves, Marcelo de Carvalho; Pozza, Edson Ampélio; Sanches, Luciana; Belan, Leônidas Leoni; Freitas, Marcelo Loran de Oliveira

doi:10.1007/s40858-021-00474-w

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…(1) Related concepts 1) Spatial big data Big data refers to large-capacity data sets that cannot be satisfied by the level of traditional computing models, and it contains means and technologies for mining and large-capacity data sets [6]. Eighty percent of big data is related to spatial information, so the concept of spatial big data has emerged, which is defined as big data related to spatial location.…”

Section: Design Of Parallel Computing Model Based On Big Data Processingmentioning

confidence: 99%

Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation

Long¹

2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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At present, the parallel computing theory based on spatial big data has problems such as difficult algorithms, difficult operations, and complex formulas, based on this, this paper proposes a p-Dot parallel computing model based on the traditional parallel computing model of BSP (Bulk Synchronous Parallel), and then tests the model effect by setting experiments. The results reveal that: (1) All curves are open up and have a minimum value. (2) The dataset with a capacity of 0.25GB is the benchmark dataset. (3) The expansion rate e(w) of the input data capacity of the model under different test procedures has a linear relationship with the expansion rate e(n* ) of the corresponding optimal number of machines. (4) When 𝑛→∞ in the partition equation p(n), p(n) tends to a certain value.

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Section: Design Of Parallel Computing Model Based On Big Data Processingmentioning

confidence: 99%

Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation

Long¹

2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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“…In this context, proper selection of spectral and spatial image resolutions, as well as the optimal timing, is crucial to achieve satisfactory results ( Peña et al., 2015 ; Khanal et al., 2017 ), which promotes the use of UAVs or manned aircrafts to the detriment of satellites in precision crop protection. Nonetheless, ML and satellite imagery can be useful in broad-scale applications, e.g., for evaluating integrated bacterial blight disease management in coffee plantations with several ecological variables ( Landsat-8 surface reflectance values and VIs, relief morphometry and hydrological attributes) by using RF, SVM and Naïve Bayes ( de Carvalho Alves et al., 2022 ), or for mapping cruciferous weed patches in multiple winter wheat fields with QuickBird satellite imagery by using MLC ( de Castro et al., 2013 ). Thermal and hyper-spectral aerial images with capability to capture slight variations in crop temperature and in narrow spectral bands associated to certain physiological indicators, respectively are commonly used in early detection of crop diseases, such as for identifying bacterial Huanglongbing (HLB) disease in citrus trees with stepwise regression, SVM, LDA and QDA ( Garcia-Ruiz et al., 2013 ), fungal Verticillium wilt (Verticillium dahlia) disease in olive trees with LDA and SVM ( Calderón et al., 2015 ), bacterial Xylella fastidiosa infections in olive trees ( Zarco-Tejada et al., 2018 ), and fungal yellow rust ( Puccinia striiformis ) across crop cycle in wheat with RF and CNN-based Inception-ResNet blocks ( Zhang et al., 2019a ).…”

Section: Scientific Impact and Relevant Contributions Of ML In Precis...mentioning

confidence: 99%

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

et al. 2023

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Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.

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Insights for improving bacterial blight management in coffee field using spatial big data and machine learning

Cited by 2 publications

References 42 publications

Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation

Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

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