Identification of symptoms related to potato Verticillium wilt from UAV-based multispectral imagery using an ensemble of gradient boosting machines

Lizarazo, Ivan; Rodriguez, Jorge L; Cristancho, Omar; Olaya, Felipe; Duarte, Marlon; Prieto, Flavio

doi:10.1016/j.atech.2022.100138

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…Unmanned aerial vehicle (UAV) platforms were used for the measurement of various traits in horticultural crops. For example, UAV-based remote sensing coupled with different machine learning approaches was used for disease detection and classification in potato, tomato, banana, pear, and apple [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ], for tree detection in orchards such as banana and citrus [ 23 , 24 , 25 ], for aboveground biomass estimation in onion, potato, tomato, and strawberry [ 26 , 27 , 28 , 29 ], and other traits of fruits and vegetables [ 23 , 30 , 31 ].…”

Section: High-throughput Phenotyping Platformsmentioning

confidence: 99%

Image-Based High-Throughput Phenotyping in Horticultural Crops

Abebe

Kim

et al. 2023

Plants

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Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

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Section: High-throughput Phenotyping Platformsmentioning

confidence: 99%

Image-Based High-Throughput Phenotyping in Horticultural Crops

Abebe

Kim

et al. 2023

Plants

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“…Notably, researchers have addressed diverse agricultural challenges using DAI-powered applications. For instance, studies have focused on tomato disease detection [26], [27], potato cultivation [28]- [30], wheat yield prediction [31], [32], corn leaf detection and counting [33], apple tree yield prediction [34], white leaf disease detection in sugarcane [35], stress detection in rice plants [36], and soil water content prediction [37]. These groundbreaking studies reveal a rising need for using DAI technology to transform numerous facets of agriculture.…”

Section: Introductionmentioning

confidence: 99%

Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study

Slimani,

El Mhamdi,

Jilbab

2024

IJECE

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Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.

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“…In recent years, the agricultural sector was able to adopt the main technological innovations relying on artificial intelligence (AI), artificial neural networks (NN) and machine learning (ML). The goal is to digitize itself and increase the autonomy of many processes by making better data-driven decisions, reducing the workload, inputs and increase the quality of the final product [16][17][18][19][20]. The multi-view spectral information from unmanned aerial vehicles (UAV) based color-infrared images combined with machine learning algorithms was used to improve the estimation of nitrogen nutrition status in winter wheat and optimize the fertilization [17].…”

Section: Introductionmentioning

confidence: 99%

“…Decision trees, support vector machines or k-means together with information from foliage of the crop were used in precision agriculture and the effective detection, identification and quantification of plant diseases [21,22]. In the case of potato crop, ML algorithms were recently applied for monitoring diseases through image-based techniques [20,[23][24][25][26][27]. Sugiura et al [24] proposed a phenotyping system for mapping late blight on potato crop by analyzing pixel change between consecutive images.…”

Section: Introductionmentioning

confidence: 99%

Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions

Meno

Escuredo

Abuley

et al. 2023

Sensors

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Late blight, caused by Phytophthora infestans, is a major disease of the potato crop with a strong negative impact on tuber yield and tuber quality. The control of late blight in conventional potato production systems is often through weekly application of prophylactic fungicides, moving away from a sustainable production system. In support of integrated pest management practices, machine learning algorithms were proposed as tools to forecast aerobiological risk level (ARL) of Phytophthora infestans (>10 sporangia/m3) as inoculum to new infections. For this, meteorological and aerobiological data were monitored during five potato crop seasons in Galicia (northwest Spain). Mild temperatures (T) and high relative humidity (RH) were predominant during the foliar development (FD), coinciding with higher presence of sporangia in this phenological stage. The infection pressure (IP), wind, escape or leaf wetness (LW) of the same day also were significantly correlated with sporangia according to Spearman’s correlation test. ML algorithms such as random forest (RF) and C5.0 decision tree (C5.0) were successfully used to predict daily sporangia levels, with an accuracy of the models of 87% and 85%, respectively. Currently, existing late blight forecasting systems assume a constant presence of critical inoculum. Therefore, ML algorithms offer the possibility of predicting critical levels of Phytophthora infestans concentration. The inclusion of this type of information in forecasting systems would increase the exactitude in the estimation of the sporangia of this potato pathogen.

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Identification of symptoms related to potato Verticillium wilt from UAV-based multispectral imagery using an ensemble of gradient boosting machines

Cited by 16 publications

References 15 publications

Image-Based High-Throughput Phenotyping in Horticultural Crops

Image-Based High-Throughput Phenotyping in Horticultural Crops

Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study

Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions

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