Chili Classification Using Shape and Color Features Based on Image Processing

Sihombing, Yobel Fernanda; Septiarini, Anindita; Kridalaksana, Awang Harsa; Puspitasari, Novianti

doi:10.15294/sji.v9i1.33658

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…This study utilized a K-MEANS model and, based on the image capture distances, achieved accuracies of 93.3% at a distance of 50 cm, 64% at 100 cm, and 63% at 150 cm. In another study [28], the K-Nearest Neighbors (KNN) method was employed, using both Euclidean and Manhattan distance calculation algorithms to identify two types of features: color and shape. This method was used to classify five classes of chilies: cayenne pepper, green chili, big green chili, big red chili, and curly chili.…”

Section: -1-related Workmentioning

confidence: 99%

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Moya,

Quito,

Pilco

et al. 2024

Emerg Sci J

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In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF

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Section: -1-related Workmentioning

confidence: 99%

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Moya,

Quito,

Pilco

et al. 2024

Emerg Sci J

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“…Two features have been extracted; shape and color to categorize the fruits into five classes; cayenne pepper, green chili, big green chili, big red chili and curly chili. High accuracy has obtained in average of above 90% [32]. Yet, the experiment only been done for individual chili fruit images.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis on localization and prediction of depth chili fruits images using YOLOv5

2022

IJATEE

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Chili fruits are essential ingredients that Malaysians consider essential for cooking. Adding chili to a dish used to produce a second fiery flavour. In south-western Ecuador, one of the most important plant-growing regions on the American continent at the time. As a result, this provides evidence that people were using chili as an additional food element as early as 600 years ago. A traditional method of picking chili is common, but it is less precise and timeconsuming. Incorrect picking and grading will cause the harvesting process to take longer. The advancement of computer vision and pattern recognition has demonstrated its effectiveness in image recognition. Because of their simplicity and low complexity, 2 dimensional images are frequently used in image recognition. As a result, advancement in automated picking systems with object detection is common. However, due to a lack of image information, such as depth, 2D images are thought to be difficult to identify the growing stages or maturity level of chili fruits. Object detection is prevalent for determining the localization and category of objects. One of the well-established methods such you look only once (YOLO) has widely used is in object detection. To anticipate this effort, the fast, reliable and able to recognize small object, YOLOv5 is proposed to localise and predict the category of chili fruits which allows the process to determine a chili's form and categories based on its colour. The proposed model is able to differentiate and localize the position as well as the colour of chili fruits with above 94% in average. Hence, our achievement has proven its effectiveness and becomes our greater goal of developing an autonomous chili fruit picking robot which could help farmers or agricultural sectors to reduce their labours during the grading process.

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“…Chilli fruit color is an important character that is widely used in chilli classification [6,7]. In the development of identification using image processing, the color and shape of chillies are important characteristics [8]. Color is also a consideration for consumers in choosing the type of chilli according to their needs [9].…”

Section: Introductionmentioning

confidence: 99%

Variation of Fruit Color in Cakra Hijau, G1/M8 and HV-149 Chilli Pepper Cultivar: Physiology and Molecular Approach

Arumingtyas,

Zakiyah,

Monica

et al. 2023

JTLS

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The fruit color of chili pepper is an important characteristic in identification and classification and is often used as the basis for determining consumer preferences. Information on the relationship between chili fruit color and its molecular profile is very important in supporting selection activities in plant breeding. This study aims to identify genetic diversity associated with the fruit color of three genotypes of chili (Capsicum frutescens L.): Cakra Hijau, HV-149 and G1/M8, using Random Amplified Polymorphic DNA (RAPD) and Simple Sequence Repeat (SSR). Morphological confirmation was carried out according to Capsicum descriptors. Nineteen RAPD markers and six SSR markers were used for genetic variability assessment. Genetic variation was analyzed using the unweighted pair group method with the arithmetic mean and the Jaccard similarity index. The three chili genotypes had different fruit colors at each maturation stage. The immature Cakra Hijau fruit is dark green and turns dark red as it ripens. The immature fruits of the G1/M8 line are light green and turn red when ripe. Finally, immature HV-149 fruits are dark green and yellow when ripe. The SSR markers used in this study were unable to show polymorphism. On the other hand, the RAPD marker successfully detected genetic variation in the three chili genotypes and resulted in a total of 49 alleles. The average value of polymorphic information content of the RAPD primers used ranged from 0 to 0.296, with the highest index indicated by OPA-1. The dendrogram shows the separation of the three genotypes into two main clusters, with the first cluster consisting of the HV-149 variety and the second cluster consisting of Cakra Hijau and G1/M8 lines. This study revealed that there are genetic variations based on the morphological characteristics of fruit color at each ripening stage and RAPD band profile. The RAPD marker was more effective than the SSR marker for identifying the genetic diversity of fruit color in the three chilies studied.

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Chili Classification Using Shape and Color Features Based on Image Processing

Cited by 7 publications

References 27 publications

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Analysis on localization and prediction of depth chili fruits images using YOLOv5

Variation of Fruit Color in Cakra Hijau, G1/M8 and HV-149 Chilli Pepper Cultivar: Physiology and Molecular Approach

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