Shape based approach for detecting Musa Species in fruit industry

Abstract

Agro export industries generate a substantial amount of revenue to Indian economy. In the fruit industry, various fruits like banana, mango, apple and pomegranate, etc. are transported in the conveyor for a post harvest process like classification, sorting, grading and juice extraction. The manual discrimination of various fruits consumes time and, it can be automated. This research work is intended to build an image processing algorithm that ensures automatic discrimination of banana (Musa Species.) from other fruits like Citrus, Apple, and Pomegranate. The input object is segmented using Background subtraction and threshold method. Morphological operations are performed to obtain the clear contour of the segmented objects. The shape of the banana and non-banana are described by scale and translation invariant signature. Binary SVM with signature feature vectors detect the banana fruit from the non-banana fruit automatically. The accuracy rate is 95%.

Identifying of unripe Ambon and Hijau banana fruits using computer vision and extreme learning machine classifier

“…The results of classification using shape features in this study proved to be better than [10] which used shape features extracted by scale invariant shape analysis. While in the study [11], although it produced higher accuracy, the classified banana and non-banana objects had significant differences in shape so they were easier to be identified. Furthermore, the use of texture features extracted with LBP showed an increase in recognition performance.…”

“…Study by [10] classified banana cultivars of Cavendish, Lady Finger, and Pisang Awak using the shape feature and obtained the best accuracy using classifier Bayessian network. Study by [11] performed classification using the shape feature and support vector machine (SVM) to distinguish bananas from other fruits. While in the research conducted by [12] and [13] using banana tiers in the classification process.…”

Design of a optimization algorithm for binary classification

“…That is, they are useful Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  Design of a optimization algorithm for binary classification (Miguel Angel Cano Lengua) 1597 when the response to the company's requirements is housed within a finite set of possible results. We have as an example, the verification if an email is unwanted or unwanted, in this case there are only two options and it is known as binary classification, we can see in works related to the detection of lung cancer to classify the nodules in benign and malignant [1], in addition to image processing for automatic discrimination of bananas [2]. On the other hand, you can have information in which its classification is given by multiple categories.…”

“…Consider another function 𝐻 2 : 𝕍 2 × 𝕍 2 → ℝ ∪ {+∞} with 𝐻 2 ∈ 𝒟(𝑖𝑛𝑡(𝒦 2 )) and 𝜃 1 , 𝜃 2 > 0 positive parameters. We define (1) and (2).…”

A multivariate Gaussian modeling for counting bananas

“…from other fruits like Citrus, Apple, and Pomegranate. The accuracy rate is 95% [3]. Nur Badariah Ahmad Mustafa has discussed the Image Processing of an Agriculture Produce: Determination of Size and Ripeness of a Banana.…”