Grading of ripening stages of red banana using dielectric properties changes and image processing approach

Mohapatra, Asutosh; Shanmugasundaram, S.; Malmathanraj, R.

doi:10.1016/j.compag.2017.10.010

Cited by 40 publications

(24 citation statements)

References 18 publications

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“…It might be due to the formation of simple sugars on hydrolysis of starch during ripening which increases the ionic concentration of the product. As a result the ability of the biomolecules in fruit to store the energy is increased gradually (Mohapatra et al, ). On ripening, the capacitance value of sapota increases (Teerachaichayut, Terdwongworakul, Keawsumnuk, Rangsi, & Seangkeaw, ) in a linear trend as shown in Figure .…”

Section: Resultsmentioning

confidence: 99%

“…Detection of sugar content in citrus fruits using capacitance measurement principle was studied by Bhosale (). Capacitance based techniques have also been used in developing algorithms to evaluate the quality of intact fruits like banana (Mohapatra, Shanmugasundaram, & Malmathanraj, ; Soltani, Alimardani, & Omid, ), watermelon (Kato, ), and avocado (Bean, ).…”

Section: Introductionmentioning

confidence: 99%

“…Capacitance based techniques have also been used in developing algorithms to evaluate the quality of intact fruits like banana (Mohapatra, Shanmugasundaram, & Malmathanraj, 2017;Soltani, Alimardani, & Omid, 2010), watermelon (Kato, 1997), and avocado (Bean, 1962).…”

Section: Introductionmentioning

confidence: 99%

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Development of capacitance based nondestructive ripening indices measurement system for sapota (Manilkara zapota)

Raja

Saravanan

2019

J Food Process Engineering

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The study aimed to develop a nondestructive sensing system to determine the ripening indices of sapota fruit, based on the principle of dielectric capacitance measurement. The unripened sapota fruit contains starch, which gets converted to simple sugars during ripening. This conversion increases the electrical energy storability (i.e., electrical capacitance) of the fruit. A capacitance measurement system was developed, which consists of two standard copper‐coated printed circuit board (PCB) electrodes and a microprocessor. The capacitance of fruit was determined at 9 V D.C. input voltage. Physicochemical properties such as total soluble solids (TSS), titrable acidity, firmness during ripening were studied for the fruits stored at ambient condition (30 ± 2°C). The changes in physicochemical properties and their correlation with capacitance were studied during ripening. The capacitance of the fruit increases with the increase in total soluble solids content of the fruit. With the correlation observed (R2 = .9727), an algorithm has been developed for sensing the ripening stages (mature green, semi‐ripe, and ripe) of the fruit. The system shows an accuracy of 90.74% in determining the stages of fruit ripening. Practical Applications The developed system helps in nondestructively determining the ripening stages (mature green, semi‐ripe, ripe) of sapota at low cost. The detection process is less time‐consuming. Also, the fruit does not undergo any tissue damage since the fruit is not applied with any force in the capacitance measurement process.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of capacitance based nondestructive ripening indices measurement system for sapota (Manilkara zapota)

Raja

Saravanan

2019

J Food Process Engineering

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“…Rajkumar dkk 13 telah menggunakan pencitraan hiperspektral untuk mempelajari hubungan kualitas dengan tingkat kematangan buah pisang dan berhasil membuat model dengan akurasi tinggi untuk parameter total padatan terlarut (TSS), kelembaban dan ketegasan pisang. Beberapa dengan metode lainnya secara non destruktif juga telah dilakukan oleh peneliti, diantaranya penggunaan karakteristik dielektrik [14][15][16] , karakteristik impedansi 17 dan aplikasi electronic nose 18 untuk memprediksi tingkat kematangan buah pisang.…”

Section: Pendahuluanunclassified

Pengukuran Tingkat Kematangan Buah Pisang Cavendish Berdasarkan Reflektansi Cahaya Led

Pramono¹

2020

J. Penelit Pascapanen Pertan

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Penentuan tingkat kematangan buah pisang cavendish biasanya dilakukan secara manual dengan membandingkan warna kulit buah pisang dengan bagan warna standar. Hal ini mempunyai kekurangan yaitu tidak konsisten dan sangat subyektif tergantung dari keahlian operator. Penelitian ini dilakukan untuk mendapatkan hasil pengukuran tingkat kematangan buah pisang cavendish yang lebih akurat, mudah dan tidak merusak (non destruktif). Pengukuran kematangan buah pisang dilakukan berdasarkan faktor reflektansi cahaya, yaitu cahaya hijau (500-560 nm), jingga (580-610 nm), dan merah (600-650 nm) yang diperoleh dari LED. Sebuah spektrofotometer pada rentang spektrum 350-700 nm digunakan sebagai pembanding dalam penelitian ini, dengan sumber cahaya berasal dari lampu halogen untuk dapat memenuhi rentang panjang gelombang pada spektrofotometer. Hasil terbaik didapatkan dari pengukuran nilai reflektansi dengan cahaya merah (600-650 nm), dimana didapatkan nilai reflektansi sebesar 30%-39%, 39%-49%, 49%-59%, 59%-67% dan di atas 67% untuk tingkat kematangan 2,3,4,5 dan 6. Pengujian dengan menggunakan 71 sampel pada tingkat kematangan 2 sampai dengan 6 menghasilkan pengukuran 55 benar (77%), 11 kurang matang satu tingkat (16%) dan 5 lebih matang satu tingkat (7%). Sistem pemindaian (scanning) pada seluruh permukaan kulit pisang atau penggunaan citra kamera digital juga dapat dilakukan untuk meningkatkan akurasi hasil pengukuran. Measurement of Cavendish Banana Ripeness Stage Based on LED Light ReflectanceThe determination of the ripeness stage of cavendish bananas is usually done manually by comparing banana peels with a standard color chart. This has the disadvantage of being inconsistent and very subjective depending on the expertise of the operator. This research was conducted to obtain the results of the measurement of the ripeness stage of cavendish banana which is more accurate, easy and not destructive. The ripeness stage measurement of banana was based on the light reflectance factor, which were green light (500-560 nm), orange (580-610 nm), and red (600-650 nm) obtained from the LEDs. A spectrophotometer in the spectrum range 350-700 nm was used as a comparison in this study, with light sources coming from halogen lamps to be able to meet the wavelength range of the spectrophotometer. The best results were obtained from the measurement of reflectance values with red light, where the reflectance values were 30%-39%, 39%-49%, 49%-59%, 59%-67% and above 67% for the maturity level of 2, 3 , 4, 5 and 6. Testing using 71 samples at maturity level 2 to 6 resulted in a prediction of 55 correct (77%), 11 less mature one level (16%) and 5 more mature one level (7%). A scanning system on the entire surface of a banana peel or the using image of digital camera can also be done to improve the accuracy of the measurement results.

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“…The process of fruit maturity has also been related to changes in their dielectric properties. In [13], the ripening of red banana is analyzed using features such as capacitance, relative permittivity and impedance, and image processing techniques such as local binary patterns (LBPs). The classifiers showed a high accuracy in predicting the different ripening stages.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video

et al. 2019

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The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes. Moreover, aerial images and the estimated ripeness can be used as indicators for detecting water stress and determining the water applied during irrigation. Additionally, they can also be related to the crop coefficient (Kc) of seasonal water needs. The purpose of this research is to develop a new computer vision algorithm to detect the existing fruits in aerial images of an apple cultivar (of Red Delicious variety) and estimate their ripeness stage among four possible classes: unripe, half-ripe, ripe, and overripe. The proposed method is based on a combination of the most effective color features and a classifier based on artificial neural networks optimized with genetic algorithms. The obtained results indicate an average classification accuracy of 97.88%, over a dataset of 8390 images and 27,687 apples, and values of the area under the ROC (receiver operating characteristic) curve near or above 0.99 for all classes. We believe this is a remarkable performance that allows a proper non-intrusive estimation of ripening that will help to improve harvesting strategies.

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Grading of ripening stages of red banana using dielectric properties changes and image processing approach

Cited by 40 publications

References 18 publications

Development of capacitance based nondestructive ripening indices measurement system for sapota (Manilkara zapota)

Development of capacitance based nondestructive ripening indices measurement system for sapota (Manilkara zapota)

Pengukuran Tingkat Kematangan Buah Pisang Cavendish Berdasarkan Reflektansi Cahaya Led

An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video

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