Automated Flower Species Identification by Using Deep Convolution Neural Network

Bondre, Shweta; Yadav, Uma

doi:10.1007/978-981-16-6624-7_1

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…In the field of agricultural disease image recognition, transfer learning is becoming increasingly common. However, a variety of factors may influence the modeling efficiency of transfer learning, including the dataset quality, prototypical model collection, negative transfer or unnecessary transfer, and so on 60 . As a result, further study into various aspects of transfer learning is needed.…”

Section: Discussionmentioning

confidence: 99%

“…However, a variety of factors may influence the modeling efficiency of transfer learning, including the dataset quality, prototypical model collection, negative transfer or unnecessary transfer, and so on. 60 As a result, further study into various aspects of transfer learning is needed. To begin, selecting the appropriate prototypical model for parameter-based transfer learning is crucial.…”

Section: Discussionmentioning

confidence: 99%

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Recent advances in agricultural disease image recognition technologies: A review

Bondre

Patil

2023

Concurrency and Computation

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In the world of intelligent agriculture, agricultural disease picture detection plays a critical role. Plant disease identification must be efficient if agricultural production is to be increased sustainably. Anomalies in plants affected by viruses, insects, nutritional deficiencies, or poor weather conditions have traditionally been diagnosed by human experts. Deep learning (DL) and transfer learning, two sophisticated machine learning techniques synonymous with the advancement of artificial intelligence technologies in recent years, have started to be used for the identification of agricultural diseases.However, a variety of significant obstacles exist in the way of widespread use of these approaches. This paper looks at DL and transfer learning in particular, and discusses recent developments in using these advanced technologies to recognize agricultural disease images. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. The analysis and evaluation of these two approaches show that transfer learning is the better choice given existing agricultural disease data tools. The paper then looks at the key problems that need to be addressed for research in this area to advance, such as image dataset creation, the collection of big data auxiliary domains, and the optimization of the transfer learning process. The construction of technically feasible agricultural disease image recognition systems requires the creation of image datasets collected under real cultivation conditions. This survey aims to learn more about DL capabilities in plant disease detection to increase device efficiency and accuracy in future research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Recent advances in agricultural disease image recognition technologies: A review

Bondre

Patil

2023

Concurrency and Computation

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“…For feature extraction, deep neural networks were used. 37 To identify the input data and eventually recognize depression, a "deep integrated support vector machine (DISVM)" algorithm that incorporates the AdaBoost integration strategy, as well as a deep neural network, was used. According to the author's simulation experiments, the proposed depression recognition system identified possible depressed patients among college students using "Sina Weibo data".…”

Section: Depression Detection From Text Analysismentioning

confidence: 99%

“…Firstly, the author gathered text data from college student users on “Sina Weibo” and converted it into input data for machine learning. For feature extraction, deep neural networks were used 37 . To identify the input data and eventually recognize depression, a “deep integrated support vector machine (DISVM)” algorithm that incorporates the AdaBoost integration strategy, as well as a deep neural network, was used.…”

Section: Classification Of Depression Detection Techniquesmentioning

confidence: 99%

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

Yadav

Sharma

Patil

2022

Concurrency and Computation

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Depression is the primary cause of illness and injury in the country, with over 280 million people suffering from it as per the 2021 survey. Depression is one of the most common psychiatric illnesses in the world, affecting millions of people. Early detection of major depression symptoms and treatment with timely intervention can help to prevent the emergence of major depression. This has generated the need for some novel techniques to be utilized for depression detection to help doctors in diagnosing and treating depression effectively. Depression can be investigated through online posts, audio files, facial expressions, as well as through video files. In this regard, the research presents a detailed survey of the existing machine learning methods in depression detection, along with the different datasets available. The research explores automated depression detection strategies and the various ways to detect depression from text, audio, and video, and even examines the various systems and procedures for detecting depression using various criteria. More than 140 related research articles were considered for this review, with 80 of them being processed for comparative analysis based on various key performance indicators. The classifications of various methods used to detect stress, anxiety, and depression are highlighted followed by open research challenges and issues while analyzing depression techniques.

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“…However, in health‐related activities, 15 any true‐false or positive–negative decision might result in serious consequences. However, because deep learning 16 models are opaque, we frequently have no idea what went wrong when an erroneous prediction is generated. Therefore, knowing the model is just as important as improving performance in such tasks.…”

Section: Introductionmentioning

confidence: 99%

A novel automated depression detection technique using text transcript

Yadav

Sharma

2022

Int J Imaging Syst Tech

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Automated Flower Species Identification by Using Deep Convolution Neural Network

Cited by 9 publications

References 11 publications

Recent advances in agricultural disease image recognition technologies: A review

Recent advances in agricultural disease image recognition technologies: A review

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

A novel automated depression detection technique using text transcript

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