Technological support for detection and prediction of plant diseases: A systematic mapping study

Bischoff, Vinícius; Farias, Kleinner; Menzen, Juliano Paulo; Pessin, Gustavo

doi:10.1016/j.compag.2020.105922

Cited by 31 publications

(12 citation statements)

References 35 publications

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“…This protocol addresses essential steps defined in well established guidelines [58] to plan and create a systematic mapping of the literature. In addition, our study protocol was also defined based on previously validated systematic mapping studies [9,52,10,34,33,7,74,67]…”

Section: Methodsmentioning

confidence: 99%

Usage of Psychophysiological Data as an Improvement in the Context of Software Engineering: A Systematic Mapping Study

Vieira

Farias

2020

XVI Brazilian Symposium on Information Systems

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In recent years, many studies have applied wearable devices to capture psychophysiological data from software developers. However, the current literature lacks investigations that classify the studies and point out gaps to be explored. This article, therefore, seeks to present a comprehensive overview of the literature by classifying and creating a systematic map of the works. Besides, it seeks to pinpoint research gaps, challenges, and trends. Based on widely known guidelines, a systematic mapping of the literature was designed and run to answer eight research questions. After applying a careful filtering process, we selected 27 representative studies from a sample of 2,084 potentially relevant works retrieved from seven digital libraries. The main results are: a classification scheme of the published studies was produced; there is no predominance of the devices used to capture psychophysiological data; over 50% of the studies have explored indicators related to mental states and neural activity; and 80% have analyzed composite data to understand the cognitive load and in the context of understanding debugging programs and strategies. Our findings can benefit researchers and students by creating a systematic map of the literature, being a starting point for future research.

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Section: Methodsmentioning

confidence: 99%

Usage of Psychophysiological Data as an Improvement in the Context of Software Engineering: A Systematic Mapping Study

Vieira

Farias

2020

XVI Brazilian Symposium on Information Systems

Self Cite

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“…Using leaf images of healthy and sick plants, customized deep learning models based on unique convolutional neural network architectures have been constructed to diagnose plant diseases [64]. Many conventional deep learning architectures are paired with optimization and customization approaches to provide considerable accuracy using technology in plant disease detection [64,67,68]. The performance of several deep learning techniques has been studied and compared [69].…”

Section: Functionalitymentioning

confidence: 99%

Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations

et al. 2022

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In this technologically advanced era, with the proliferation of artificial intelligence, many mobile apps are available for plant disease detection, diagnosis, and treatment, each with a variety of features. These apps need to be categorized and reviewed following a proper framework that ensures their quality. This study aims to present an approach to evaluating plant disease detection mobile apps, which includes providing ratings of distinct features of the apps and insights into the exploitation of artificial intelligence used in plant disease detection. The applicability of these apps for pathogen or disease detection, identification, and treatment will be assessed along with significant insights garnered. For this purpose, plant disease detection apps were searched in three prominent app stores (the Google Play store, Apple App store, and Microsoft store) using a set of keywords. A total of 606 apps were found and from them, 17 relevant apps were identified based on inclusion and exclusion criteria. The selected apps were reviewed by three raters using our devised app rating scale. To validate the rater agreements on the ratings, inter-rater reliability is computed alongside their intra-rater reliability, ensuring their rating consistency. Also, the internal consistency of our rating scale was evaluated against all selected apps. User comments from the app stores are collected and analyzed to understand their expectations and views. Following the rating procedure, most apps earned acceptable ratings in software quality characteristics such as aesthetics, usability, and performance but gained poor ratings in AI-based advanced functionality, which is the key aspect of this study. However, most of the apps cannot be used as a complete solution to plant disease detection, diagnosis, and treatment. Only one app, Plantix–your crop doctor, could successfully identify plants from images, detect diseases, maintain a rich plant database, and suggest potential treatments for the disease presented. It also provides a community where plant lovers can communicate with each other to gain additional benefits. In general, all existing apps need to improve functionalities, user experience, and software quality. Therefore, a set of design considerations has been proposed for future app improvements.

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“…Using leaf images of healthy and sick plants, customized deep learning models based on unique convolutional neural network architectures have been constructed to diagnose of plant diseases [66]. Many conventional deep learning architectures are paired with optimization and customization approaches to provide considerable accuracy using technology in plant disease detection [69,70,66]. The performance of several deep learning techniques has been studied and compared [71].…”

Section: Expert or Community Not At Allmentioning

confidence: 99%

Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations

Siddiqua¹,

Kabir²,

Tanzina³

et al. 2022

Preprint

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In this technologically advanced era, with the proliferation of artificial intelligence, many mobile apps are available for plant disease detection, diagnosis and treatment, each with a variety of features. These apps need to be categorized and reviewed following a proper framework that ensures their quality. This study aims to present an approach to evaluating plant disease detection mobile apps, this includes providing ratings of distinct features of the apps and insights into the exploitation of artificial intelligence used in plant disease detection. The applicability of these apps for pathogen or disease detection, identification, and treatment will be assessed along with significant insights garnered. For this purpose, plant disease detection apps were searched in three prominent app stores (the Google Play store, Apple App store, and Microsoft store) using a set of keywords. A total of 606 apps were found and from them 17 relevant apps were identified based on inclusion and exclusion criteria. The selected apps were reviewed by three raters using our devised app rating scale. To validate the raters' agreement on the ratings, inter-rater reliability is computed alongside their intra-rater reliability, ensuring their rating consistency. Also, the internal consistency of our rating scale was evaluated against all selected apps. User comments from the app stores are collected and analyzed to understand their expectations and views. Following the rating procedure, most apps earned acceptable ratings in software quality characteristics such as aesthetics, usability and performance, but gained poor ratings in AI-based advanced functionality, which is the key aspect of this study. However, most of the apps cannot be used as a complete solution to plant disease detection, diagnosis and treatment. Only one app, Plantix -your crop doctor, could successfully identify plants from images, detect diseases, maintain a rich plant database, and suggest potential treatment for the disease presented. It also provides a community where plant lovers can communicate with each other to gain additional benefits. In general, all existing apps need to improve functionalities, user experience, and software quality. Therefore, a set of design considerations has been proposed for future app improvements.

show abstract

Technological support for detection and prediction of plant diseases: A systematic mapping study

Cited by 31 publications

References 35 publications

Usage of Psychophysiological Data as an Improvement in the Context of Software Engineering: A Systematic Mapping Study

Usage of Psychophysiological Data as an Improvement in the Context of Software Engineering: A Systematic Mapping Study

Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations

Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations

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