FRUIT MATURITY DETECTION SYSTEM USING
Dr. S. Senthil Rani*2
, Dr. B. Ashok Kumar*3 *1
Student, Department of Electrical and Electronics Engineering, Velammal College of Engineering and Technology, Madurai, TamilNadu, India
Professor, Department of Electrical and Electronics Engineering, Velammal College of Engineering and Technology, Madurai, TamilNadu, India
Professor, Department of Electricaland Electronics Engineering, Thiagarajar College of Engineering, Madurai, TamilNadu, India
Fruit maturity analysis is very much essential for fruit cultivators and vendors. When the fruit mature beyond the permissible limit, it will become rotten and so the fruit is unfit for human consumption. To find a solution for this problem, a low cost maturity detection system using image analysis and artificial neural network (ANN) have been proposed in this work. By considering fruit colour and size the maturity detection can be done. For accurate colour prediction, area algorithm is used. Such algorithm prediction is harmless when compared to infrared ray or chemical testing. In this system gas sensor is used to detect the harmful gases sprayed over the fruit. The proposed work can be converted into complete automatic fruit maturity detection system.
KEYWORDS: Fruit maturity detection, Image Processing, Sensor, ANN modelling.
India is an agricultural country. India is currently the world’s largest producer of several dry fruits. India produce 90.2 million metric tonnes of fruits and the area under cultivation of fruits stood at 6.3 million hectares. Every year 16% of fruit being wasted due to unavailable cold storage and transportation facility.Agriculture is an important sector that provides fruits and vegetables for human beings. Fruits are globally consumed and it is an important part of our daily life. Fruit contains phytochemicals or plant chemicals, these biologically active substances can help to protect humans from some diseases. It provides livelihood and nutritional security to people all over the world. Most of fruits are harvested at early stages for the purpose of exporting. Hence, it is important to harvest the fruit at a correct stage. There is three different maturity stages such as (1) Pre-mature (2) Mature (3) Over mature stage. In , fluorescence spectroscopy has been used to analyse the maturity of fruit. Mangoes were used for the maturity detection. The maturity of fruit has been determined using intensity of fluorescence. From the chart, the different maturity level of mangoes are identified. Chlorophyll peaks are high in non-mature mangoes where as in over mature mangoes chlorophyll peaks are not found. This method is time consuming compared to other methods. In ,image processing technique has been used to determine the maturity of banana. This method is useful in automation model development. Colour intensity value and area classifier algorithms were used for detection of maturity of banana. In this system for differentiating under mature banana area algorithm is used and it is accurate. The algorithm used in this method unable to identify the difference between the mature and over mature fruit. In , Theimage processing method has used for analysing the maturity level of the fruit. Pre-processing is one of the important stage used in this method for maturity analysis. Commonly used technique is clustering and classification. Gas sensor are used in this system and it is used to calculate only the ethylene gas released by fruits. In , system uses image processing for fruit maturity detection. A fruit maturity detector is a application consisting of android device and server. This system uses JAVA, Net Beans platform, JBDC. The use of various languages in this system makes the system more complex to understand
Volume:02/Issue:03/March-2020 www.irjmets.com In , image processing, Artificial Neural Network (ANN) are deployed. This method is used to determine maturity of cherry and strawberry. The maturity level of the fruit is identified based on the shape, colour, size of the fruit. Fruit quality has done using MATLAB has lower accuracy of 63% of cherry fruit and 60% of strawberry.
In the proposed system, the conveyor belt carries the fruit and the sensor detects the fruit. A camera takes the video of fruit in front of sensor and sends the video input to the computer for further process. The system is trained initially with a set of images defined as the reference images and then real images are compared with the reference images. Using the colour and size of the fruit, maturity of the fruit is analysed using digital image processing. Gas sensor is used to identify the gas sprayed over the fruits. Manual picking of fruits is a time consuming process. This paper is used to determine the fruit maturity detection in order to reduce the man power.
Fig.2. Image based fruit analysis
Microcontroller used in this system is Arduino UNO. The board consists of set of analog and digital input/output pins. For loading programs from personal computers serial communication interfaces, Universal Serial Bus (USB) are used. The C and C++ languages are used for programming the microcontrollers.
b) Video Camera
High quality CMOS image sensor with Image Resolution of 2.5 Mega Pixels is used for colour saturation, brightness, sharpness and contrast. The lens used are f=6.0, f=0.2, and anti-flicker of 50HZ, 60HZ. Image quality is about RGB24 or 1420 and Focus Range is 4cm or infinity, Frame rate of 30fps.
c) Gas Sensor
The gases sprayed over the fruit are harmful to the human beings. This device is used to detect any gases that are sprayed over the fruit. If any gas is identified by turning the LED ON otherwise the LED is in OFF position.
d) IR Sensor
IR Sensor is used for different applications. In proximity sensor IR technology is used to detect the near object. It consists of IR transmitter as LED and IR receiver as photo diode. IR receiver is used to detect the radiation from IR receiver, the presence of the object is analysed by the variation in the voltage.
e) HUE Detection
HUE detection is mainly based on the colour. This method is used to differentiate between the colour such as red, green and yellow. Hue can typically be represented quantitatively by a single number, often corresponding to an angular position around a central or neural point.
f) K Means Classifier
K means is used for classification. The algorithm used in this is clustering, which will divide the given data into number of clusters or K clusters. In other words, k means clustering is used to classify the similarity patterns obtained from segmented image.
g) Maturity Detection
For analysing the size of fruit, area and perimeter of the segmented image is taken. By using number of pixels area of the fruit is analysed. According to pixel intensity the maturity of the fruit can be identified. A grade level according to the horizontal and vertical size can be done.
h) Grey Scale Conversion
This block converts RGB images to grey scale by eliminating the hue and saturation information while retaining the luminance. The algorithm used is to convert the RGB values to NTSC coordinates, sets the hue and saturation components to zero.
i) Image Intensity Border
This block tends to reduce the overall intensity level in addition to suppressing border structures. The output image is grey scale or binary depending on the input image.
j) Dilation and Erosion
Dilation adds pixels to the boundaries of object in an image, while erosion removes pixels on object boundaries. The number of pixels added or removed depends on the size and shape of structuring element used to process the image.
Fig.4. Final output
Fruit maturity detection is essential for fruit cultivators and vendors. Manual picking of fruits is a time consuming process. To overcome this problem,a low cost maturity detection system using image analysis havebeen proposed in this work. Colour and size of the fruit is the important parameters for maturity detection of fruit. For accurate colour prediction, area algorithm is used. In this system lemon has been used for maturity detection. Gas sensor is used to detect the harmful gases sprayed over the fruit. The proposed work can be converted into complete automatic fruit maturity detection system.
I would like to thank my guide Dr. S. Senthil Rani for her valuable support, guidance and encouragement.
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