A convolutional neural network-driven computer vision system toward identification of species and maturity stage of medicinal leaves: case studies with Neem, Tulsi and Kalmegh leaves

Mukherjee, Gunjan; Tudu, Bipan; Chatterjee, Arpitam

doi:10.1007/s00500-021-06139-9

Cited by 16 publications

(5 citation statements)

References 32 publications

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“…[14,24] Although there have been numerous studies that have examined the recognition of herbs using datasets of leaf images datasets. [40][41][42][43][44][45][46] The presented CNN MobileNetV2 model, in this research attained a remarkable accuracy of 98.69%. The methodology's excellent accuracy, together with positive outcomes from other evaluated standard criteria, establishes it as a viable fundamental element for the digitalization of the authentication process for spices.…”

Section: Confusion Matrixmentioning

confidence: 95%

Identification of Spices and their Adulterants by Integrating Machine Learning and Analytical Techniques: A Representative Study

Naman,

Sharma,

Baldi

2024

Int. J. Pharm. Sci. Drug Res.

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The adulteration of spices presents a substantial risk to public health and the authenticity of culinary customs. This article introduces a comprehensive interdisciplinary strategy to tackle this problem, by combining traditional and advanced techniques to authenticate spices in simple manner.The machine learning and artificial intelligence, has shown shows significant promise in diverse area based on their ability to differentiate on the basis of distinct morphological features. This article oversees the possibility to explore machine learning to check the quality of the spices and to identify their adulterants. As a representative case study, machine learning models for spice recognition, including transfer learning with pre-existing models such as MobileNetV2 is reported for the selected spice (Capsicum annum) demonstrating 97.97% accuracy. In addition, the study examines several conventional and analytical methodologies viz., qualitative tests, microscopy, colorimetry, density measurement, and spectroscopy. Furthermore, quantitative method based on high-performance liquid chromatography was developed and reported here briefly for analysing the capsaicin concentration, which is essential for assessing the quality of C. annum. This comprehensive study focuses on important areas of research that have not been well explored. The results may open a new vista in determining the quality of spices and identification of their adulterations using the artificial intelligence.

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Section: Confusion Matrixmentioning

confidence: 95%

Identification of Spices and their Adulterants by Integrating Machine Learning and Analytical Techniques: A Representative Study

Naman,

Sharma,

Baldi

2024

Int. J. Pharm. Sci. Drug Res.

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“…Segmented mango leaves were used for the stress recognition of various mango leaves based on a self-built CNN [66]. Similarly, A CNN model was utilized to classify medicinal crop varieties and their maturity based on leaves [67]. Gang et al [68] employed ResNet 50 to establish an estimation model for the growth indexes (fresh and dry weights, height, leaf area, and diameter) of lettuce in a greenhouse.…”

Section: Health and Growth Monitoringmentioning

confidence: 99%

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

et al. 2023

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The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN’s application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification.

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“…This paper [8] describes how to identify medicinal plants using leaf features and preprocessing techniques. In this paper [9], authors focus on extracting image features and segmenting images of 125 different herb leaves from Malaysia, including Belalai Gajah, Rerama, Sirih, Mexican Mint, and Senduduk. For each image, a total of 14 features were extracted: 7 geometrical features and 7 textural features.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Identification and classification of medicinal plants using leaf with deep convolutional neural networks

Bhelkar¹,

Sharma²

2022

ijhs

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The Indian medical practise of Ayurveda has gained international renown. Herbal preparations are the basis of Ayurveda medicine. The pharmaceutical industry is beginning to pay more attention to medicinal plants because they have fewer adverse effects and reactions than modern medicine and are also less expensive. In recent years, numerous Deep learning, machine learning algorithms that are both effective and reliable have been utilised for plant classifications by using images of leaf. In this work, 45 distinct medicinal plant leaves were used, and a deep learning model was applied in order to achieve a high degree of accuracy in the classification and recognition procedures that were carried out with the help of computer vision techniques. After categorising the leaves of numerous medicinal plants, the Xception model has a 97.65% accuracy rate.

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A convolutional neural network-driven computer vision system toward identification of species and maturity stage of medicinal leaves: case studies with Neem, Tulsi and Kalmegh leaves

Cited by 16 publications

References 32 publications

Identification of Spices and their Adulterants by Integrating Machine Learning and Analytical Techniques: A Representative Study

Identification of Spices and their Adulterants by Integrating Machine Learning and Analytical Techniques: A Representative Study

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

Identification and classification of medicinal plants using leaf with deep convolutional neural networks

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