Automatic detection of mango ripening stages – An application of information technology to botany

Mim, Farjana Sultana; Galib, Syed Md.; Hasan, Md. Fakhrul; Jerin, Sanjida Akther

doi:10.1016/j.scienta.2018.03.057

Cited by 61 publications

(25 citation statements)

References 8 publications

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“…Farjana Sultana Mim et al [7] implemented a method for classifying the mangoes into six maturity levels. More than 100 mangoes of different stages were used for the experiment.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

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A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Mohan¹,

Sreekumar²

2019

IJCA

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Plump fruits are a vital part of the human diet giving mandatory vitamins, minerals and other health encouraging compounds. Quality assessment and finding of fruit ripeness is a major concern in agriculture business and becomes a growing research concern in computer vision. Image processing is an advanced field which led to a higher demand to reduce the high rate of errors and given more possible results. Therefore, the objective of many types of research is to standardize and reduce manual work in the classification of tomatoes ripeness. One of the most important feature of an image is color. Estimating the ripeness of fruits via color can be performed as it is the dominant feature in describing the information of the image. However, each color models have been given a different performance when used in the experiment. This paper is a survey of different techniques that are deployed over different varieties of fruit images in order to detect maturity stages for ripening, fruit region estimation and also, the effect of different color models and other features on detecting ripeness was studied in this literature survey.

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“…Farjana Sultana Mim et al [7] implemented a method for classifying the mangoes into six maturity levels. More than 100 mangoes of different stages were used for the experiment.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

“…The developed system cannot be used for harvesting the objects from tree. 7 Mim, Farjana Sultana, et al [7] RGB and HSI features were extracted (24 features).…”

mentioning

confidence: 99%

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Mohan¹,

Sreekumar²

2019

IJCA

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“…The SVM and K nearest neighbors (K-NN) were developed for hair color classification [15]. Automated mango ripening stages detection by color classification was performed using DT classifier [16]. Without using CNN based structure, SVM machine learning algorithm was also used for vehicle color recognition [17].…”

Section: International Rice Research Institute (Irri) Has Proposedmentioning

confidence: 99%

“…As discussed earlier, DT was applied to fruit categorization through color classification and achieved 96% accuracy [16]. Therefore, we apply this classification technique to leaf color classification.…”

Section: B Classification Using Decision Treementioning

confidence: 99%

Nitrogen Fertilizer Recommendation for Paddies through Automating the Leaf Color Chart (LCC)

Islam¹,

Rizan²,

Tusher³

et al. 2020

IJACSA

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Nitrogen fertilizer is inevitable for rice production to ensure that the crop's nitrogen need is adequately supplied, during the growing season. International Rice Research Institute (IRRI) has proposed Leaf Color Chart (LCC) to detect the exact nitrogen need of paddy. Farmers generally monitor the plant's growth (which is also an indicator of the nitrogen concentration of leaves) by comparing the leaf color with the corresponding color of the LCC. Currently, in most cases, LCC is used manually to determine the fertilizer need and thus, there is a chance of either overestimating or underestimating the amount of fertilizer. To avoid this problem, a smart fertilizer recommendation system is proposed in this paper. The proposed method is able to automate the manual acquisition and interpretation of leaf color for classification through LCC. The experimentation considers a sample of 6000 Aman paddy leaf images. The data acquisition process was performed according to IRRI's guidance of taking the paddy leaf images within the body shade by our developed application. The data/images have already been made public in Kaggle-a wellknown dataset website. The semantic segmentation of the dataset was performed by a powerful Convolutional Neural Network (CNN) backbone architecture-DeepLabV3+. Color classification into 4 categories of the LCC was performed by CNN architecture which consists of seven layers. Information gain based evaluation was performed in the Decision Tree (DT) approach to select features and with the selected features DT classified images into 4 categories. Color classification by our two proposed methods achieved 94.22% accuracy in CNN Model and 91.22% accuracy in the DT classifier.

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“…Entre éstos, el color de la piel es reconocido como un índice de madurez aceptable para muchas frutas, como el mango, la frambuesa y la fresa. El color es uno de los factores de calidad más importantes, ya que está estrechamente relacionado con las propiedades químicas y físicas de las frutas (Mim et al 2018). Las propiedades de la textura son factores clave, que influyen en la aceptabilidad de la fruta y el valor agregado de sus productos para el consumidor (Nambi et al 2016).…”

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Comportamiento de características fisicoquímicas durante la maduración del mango Tommy Atkins luego de cosechado

Villamizar-Vargas

Quiceno-Gómez

Giraldo

2019

Rev. U.D.C.A Act. & Div. Cient.

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Automatic detection of mango ripening stages – An application of information technology to botany

Cited by 61 publications

References 8 publications

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Nitrogen Fertilizer Recommendation for Paddies through Automating the Leaf Color Chart (LCC)

Comportamiento de características fisicoquímicas durante la maduración del mango Tommy Atkins luego de cosechado

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