Image Processing System for Early Detection of Cocoa Fruit Pest Attack

Basri, Basri; Harli,; Indrabayu, -; Areni, Intan Sari; Tamin, Rosmawati

doi:10.1088/1742-6596/1244/1/012003

Cited by 14 publications

(20 citation statements)

References 14 publications

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“…The mobile application for identification with the Deep Learning method using tensor flow module implemented without pre-processing enhanced. Even though implementing mobile with Android applications was very helpful for farmers, accuracy has not been maximized [11]. Previously, to support the research process, a method of classifying the color characteristics of cocoa fruits was carried out by applying Gabor Filtering.…”

Section: Related Work a Implementation Of Smart System On Cocoa Culti...mentioning

confidence: 99%

A Review of Image Processing Techniques for Pest and Disease Early Identification Systems on Modern Cocoa Plantation

Basri,

Indrabayu,

Achmad

et al. 2023

IJCDS

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This study presents a potential approach to applying image processing to the cocoa farming industry. Industry 4.0 is changing how technology is applied in cocoa cultivation. Research shows that pests and diseases are major factors that can reduce productivity, especially in the upstream and downstream industries. This study aims to conduct a literature review of several related studies, including previous research. Based on relevant references related to previous research, it is found that the case of disease attack in cocoa is most often studied. This study shows that the color analysis model is more inclined to the case of disease image patterns. The performance pattern of the existing approaches, the average accuracy is 82.85%. More challenges in image processing and efforts are made to improve accuracy but with lower processing complexity. This study also recommends a model that supports the implementation of image processing with machine learning technology in the upstream cocoa cultivation industry. The study results show that the proposed modeling framework can be the following reference for research development towards artificial intelligence technology to support Industry 4.0, particularly in cocoa cultivation.

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Section: Related Work a Implementation Of Smart System On Cocoa Culti...mentioning

confidence: 99%

A Review of Image Processing Techniques for Pest and Disease Early Identification Systems on Modern Cocoa Plantation

Basri,

Indrabayu,

Achmad

et al. 2023

IJCDS

View full text Add to dashboard Cite

show abstract

“…For example, image processing is very useful in identifying fruit maturity and size [3]. Like previous research, on a desktop-based system, image processing can detect pests and plant diseases only by analyzing the texture features of fruit skin [4]. The health sector is inseparable from utilizing the potential of image processing analysis in supporting expert decision systems [5].…”

Section: Introductionmentioning

confidence: 99%

“…Image processing systems based on mobile applications currently become the primary research, especially on system compatibility and complexity. The development of research based on image processing with low-level pixels requires an analysis to improve accuracy [9]. For this reason, precise measurement of accuracy is certainly a significant concern.…”

Section: Introductionmentioning

confidence: 99%

Application for Image Processing in a Receiver Operating Character-Based Testing Review

Basri

Tamin

Harli

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The image processing technology is currently developing quite rapidly. Implementation of technology based on artificial intelligence systems has entered various sectors and platforms, mostly in the health and agriculture sectors. There are challenges in developing image processing applications, especially on mobile platforms, including processing a large amount of data that involves millions of pixels that must be recognized. To measure the quality of the results of the process certainly requires a reliable framework. In testing the quality of training data in the database and case studies, many image processing techniques utilize the Receiver Operating Character (ROC) approach’s reliability. In some cases, this ROC approach is very different depending on the data model, output, and algorithm used. This study aims to review ROC’s use as an approach used in testing systems on developed mobile applications. The case studies used in this research are image processing in agriculture cases for pests and disease recognition in cocoa fruits and chili leaves, which is then implemented in a mobile application. Testing entities in the ROC approach are analyzed based on actual and predicted conditions. The measurement results show that the ROC approach is applied in both test cases. It can only produce accuracy measurements on the value of accuracy, sensitivity, and precision. The values on chili leaves are accuracy 60%, sensitivity 60%, and precision 100%. The accuracy is 66.7% for cacao fruit objects, sensitivity 66.7%, and precision 100%.

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“…In agricultural fields, RTIP that uses large amounts of data itself has been widely applied, including being used for early detection systems on cocoa pest attacks [24], classification of the quality of whole plant corn silage (WPCS) for cattle feed [25], and the creation of an automatic weed control system that can distinguish weeds and plants correctly in tomatoes [26], celeries [27], potatoes [28], and lettuces [29]. There are similarities in the functions of these studies, i.e.…”

Section: Introductionmentioning

confidence: 99%

Implementation of real-time image processing on bacterial cellulose formation using soybean-boiled wastewater with the variation of carbon sources during fermentation

Marzuki,

Nugroho,

Ahmadi

et al. 2023

BIO Web Conf.

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Bacterial cellulose (BC) is produced by aerobic bacteria through oxidative fermentation in synthetic and non-synthetic mediums. Several mediums reported to be used as BC formation mediums are coconut water and soybean-boiled wastewater. Carbon sources are needed to optimize the BC formation process. Recent study has implemented a real-time image processing approach for monitoring BC formation. This study aimed to investigate the correlation between variables that influence the fermentation and to determine the kinetic model of BC formation using an image processing approach with the variation of carbon sources during the fermentation. The results showed that the correlation between fermentation time and thickness had the highest percentage for glucose, sucrose, and mannitol mediums. The kinetic observation of BC formation in the medium using glucose, sucrose, and mannitol followed the Gompertz model equation, with the medium using sucrose having the fastest rate of increase at the 44th hour, followed by the medium using mannitol at the 112th hour, and the medium using glucose at the 149th hour.

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Image Processing System for Early Detection of Cocoa Fruit Pest Attack

Cited by 14 publications

References 14 publications

A Review of Image Processing Techniques for Pest and Disease Early Identification Systems on Modern Cocoa Plantation

A Review of Image Processing Techniques for Pest and Disease Early Identification Systems on Modern Cocoa Plantation

Application for Image Processing in a Receiver Operating Character-Based Testing Review

Implementation of real-time image processing on bacterial cellulose formation using soybean-boiled wastewater with the variation of carbon sources during fermentation

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