Plant identification: man vs. machine

Bonnet, Pierre; Joly, Alexis; Goëau, Hervé; Champ, Julien; Vignau, Christel; Molino, Jean‐François; Barthélémy, Daniel; Boujemaa, Nozha

doi:10.1007/s11042-015-2607-4

Cited by 34 publications

(11 citation statements)

References 20 publications

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“…Automatic recognition of crops has gained considerable attention in several scientific fields, such as plant taxonomy, botanical gardens, and new species discovery. Plant species can be recognized and classified via analysis of various organs, including leaves, stems, fruits, flowers, roots, and seeds [ 61 , 62 ]. Using leaf-based plant recognition seems to be the most common approach by examining specific leaf’s characteristics like color, shape, and texture [ 63 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

424

146

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

424

146

View full text Add to dashboard Cite

show abstract

“…On the other hand, the proposed approach is rotation invariant because the energy amount of texture is independent of rotation. One interesting future research direction is to use the proposed approach to other computer vision applications such as texture segmentation [26], object tracking [27], visual inspection systems [28,29], plant identification [30,31]. Also future research direction is to use other operators such as Wavelet filters [32].…”

Section: Resultsmentioning

confidence: 99%

Energy analysis of image descriptors in texture classification

Kiani

2021

Preprint

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Texture play important role in image description process. Texture classification is one of the problems which have been paid much attention on by computer vision scientists in last decade. If texture classification is done accurately, it can be used in many problems such as skin detection, surface defect detection, medical image analysis, gender identification, human identification, etc. Since now, many approaches are proposed to perform it. Most of them have tried to extract discriminative features to separate different texture types accurately. This paper has proposed an approach based on energy analysis of some efficient image descriptors such as median binary pattern, Local binary pattern and Gray Level Co-occurrence matrix. Next, by concatenating extracted features, a discriminative feature vector is defined. Finally, classifier is used to classify texture types. Although, this approach is a general one and is could be used in different applications. In the result part the proposed approach has been evaluated on some benchmark dataset. Next, the results have been compared with some of state-of-the-art approaches to prove the quality of the proposed approach.

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“…Using specific social media to engage participants in contributing their observations over a long time-period (Deng et al 2012). Bonnet et al 2016).…”

Section: Using Social Media For Collaborative Species Identification mentioning

confidence: 99%

“…You could then point your phone to the mushroom, snap, and it would instantly tell you everything there is to know about it, including whether cooking it is a wise choice. Some organisations are working on exactly this (Bonnet et al 2016), training their AI algorithms on the huge amounts of past data and observations collected by scientists and citizens worldwide. AI tools that can instantly classify species could be valuable in other ways.…”

Section: Validating Outputs Through Automatic Proceduresmentioning

confidence: 99%

Opportunities and Risks for Citizen Science in the Age of Artificial Intelligence

Ceccaroni

Bibby

Roger

et al. 2019

Citizen Science: Theory and Practice

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Introduction Technologies that allow computers and machines to perform tasks normally requiring human intelligence are often referred to as artificial intelligence (AI). These technologies allow machines to complete tasks with traits or capabilities ordinarily associated with human cognition, such as reasoning, problem solving, common-sense knowledge management, planning, learning, translation, perception, vision, speech recognition, and social intelligence (Kaplan and Haenlein 2019). Research in AI is rapidly increasing, as indicated when c omparing the annual publishing rate of papers focused on AI between 1996 and 2017 against the publishing rates of papers focused on any topic or against the publishing rates of papers in the field of computer science (see the growth of annually published papers by topic in Shoham et al. [2018; p. 9]). This growth in AI publications has prompted researchers to critically explore the potential promises and risks of AI (Scherer 2016; Webb 2019; Yudkowsky 2008) as well as ethics and responsibilities (Miller 2019; Cowls and Floridi 2018; Scherer 2016; Dawson et al. 2019). AI has been used in citizen science projects for about 20 years. It was first used in this context in 2000, in collaborative AI databases such as the Generic Artificial Consciousness (GAC)/Mindpixel Digital Mind Modeling Project (McKinstry 2009) and the Open Mind Common Sense project (Singh et al. 2002). In these models, usersubmitted propositions were meant to create a database of common-sense knowledge that could function as a kind of digital brain. This relationship between collective knowledge and algorithmic processing evolved in many directions and, in 2019, is predominantly represented by machine learning, especially applied to computer vision, which includes diverse methods of automatically identifying objects from digital photographs. For example, the iNaturalist platform, a citizen science project and online social network, is designed to enable citizen scientists and ecologists alike to upload observations from the natural world, such as images of animals and plants (Van Horn et al. 2018). The platform is one among many (Wäldchen et al. 2018) that include an automated

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Plant identification: man vs. machine

Cited by 34 publications

References 20 publications

Machine Learning in Agriculture: A Comprehensive Updated Review

Machine Learning in Agriculture: A Comprehensive Updated Review

Energy analysis of image descriptors in texture classification

Opportunities and Risks for Citizen Science in the Age of Artificial Intelligence

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