Intelligent diagnosis of diseases in plants using a hybrid Multi-Criteria decision making technique

Goodridge, Wayne; Bernard, Margaret; Jordan, Ren; Rampersad, Reanne

doi:10.1016/j.compag.2016.12.003

Cited by 27 publications

(6 citation statements)

References 12 publications

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“…MCDM provides a systematic framework representing technical information and requiring expert judgement to integrate multiple criteria and address uncertain information. MCDM methods have been widely applied to many fields that require handling large amounts of information and knowledge, such as business performance [30], agricultural diagnosis [31], industrial management [32], biological systems [33] and construction processes. As a compendium of various tasks, processes and requirements, the construction process usually involves many factors and aspects that need to be considered [9].…”

Section: Mcdm Process and Methodsmentioning

confidence: 99%

Review fuzzy multi-criteria decision-making in construction management using a network approach

Chen

Pan

2021

Applied Soft Computing

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Section: Mcdm Process and Methodsmentioning

confidence: 99%

Review fuzzy multi-criteria decision-making in construction management using a network approach

Chen

Pan

2021

Applied Soft Computing

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“…In some cases, image processing is complemented with information retrieved by sensors [35][36][37] or other inputs [38]. Scarcer are the approaches relying on other evidence, such as odor [39,40], weather [41,42], or rule-based systems triggered by symptoms introduced manually in natural language [43][44][45][46][47]. While some solutions focus on a specific crop or a single condition (throughout this manuscript the term "condition" is used as a synonym for "pest or disease".)…”

Section: Pests and Diseases Recognitionmentioning

confidence: 99%

Knowledge-Based System for Crop Pests and Diseases Recognition

2021

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With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.

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“…Model-based tools, deployed for example on smart phones, have been proposed to aid non-experts in diagnosis. These have used fuzzy expert systems (Awoyelu and Adebisi 2015) and multi-criteria decision making (Goodridge et al 2017). Similarly, image-based detection methods have been proposed and proliferated during the last years using approaches such as machine learning, deep learning (Barbedo 2017;Ramcharan et al 2017Ramcharan et al , 2018Ferentinos 2018;Segun et al 2019;Arnal Barbedo 2019;Tusubira et al 2020), and image processing (Powbunthorn et al 2012;Majumdar et al 2014;Ninsiima et al 2018).…”

Section: Surveillance and Detectionmentioning

confidence: 99%

Modelling cassava production and pest management under biotic and abiotic constraints

et al. 2021

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Key message We summarise modelling studies of the most economically important cassava diseases and arthropods, highlighting research gaps where modelling can contribute to the better management of these in the areas of surveillance, control, and host-pest dynamics understanding the effects of climate change and future challenges in modelling. Abstract For over 30 years, experimental and theoretical studies have sought to better understand the epidemiology of cassava diseases and arthropods that affect production and lead to considerable yield loss, to detect and control them more effectively. In this review, we consider the contribution of modelling studies to that understanding. We summarise studies of the most economically important cassava pests, including cassava mosaic disease, cassava brown streak disease, the cassava mealybug, and the cassava green mite. We focus on conceptual models of system dynamics rather than statistical methods. Through our analysis we identified areas where modelling has contributed and areas where modelling can improve and further contribute. Firstly, we identify research challenges in the modelling developed for the surveillance, detection and control of cassava pests, and propose approaches to overcome these. We then look at the contributions that modelling has accomplished in the understanding of the interaction and dynamics of cassava and its’ pests, highlighting success stories and areas where improvement is needed. Thirdly, we look at the possibility that novel modelling applications can achieve to provide insights into the impacts and uncertainties of climate change. Finally, we identify research gaps, challenges, and opportunities where modelling can develop and contribute for the management of cassava pests, highlighting the recent advances in understanding molecular mechanisms of plant defence.

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Intelligent diagnosis of diseases in plants using a hybrid Multi-Criteria decision making technique

Cited by 27 publications

References 12 publications

Review fuzzy multi-criteria decision-making in construction management using a network approach

Review fuzzy multi-criteria decision-making in construction management using a network approach

Knowledge-Based System for Crop Pests and Diseases Recognition

Modelling cassava production and pest management under biotic and abiotic constraints

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