Robusta Coffee Leaf Detection based on YOLOv3- MobileNetv2 model

Javierto, Dann Paulo P.; Martin, John Dannielle Z.; Villaverde, Jocelyn F.

doi:10.1109/hnicem54116.2021.9731899

Cited by 19 publications

(4 citation statements)

References 10 publications

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“…They further often exhibit a restricted focus, tailored to particular regions [82,89] or coffee types [35,45,54,64,68,74,85,90,94,97] and further demonstrate a limited scope in identifying nuanced characteristics like roast degrees [47] and maturity stages [60,61]. The need for more universally applicable models is evident, especially in areas like aromatic profiling, where ML remains underutilized, revealing significant untapped potential for research and industry applications.…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

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In the realm of agribusiness, transformative shifts are underway, propelled by the growing demands and expanding scales of grain production. This evolution calls for a critical reevaluation of the existing paradigms in coffee production and marketing paradigms, with a specific focus on integrating Artificial Intelligence (AI). This work aims to review, synthesize, and summarize the available data regarding how Machine Learning (ML) has been used to detect and classify characteristics in coffee beans and leaves. For this purpose, a comprehensive literature review of the most significant research contributions describing the application of AI for advanced classification techniques in coffee agriculture has been carried out. Our analysis suggests that implementing AI technologies allows the classification of coffee, encompassing various attributes such as maturity, roast intensity, disease identification, flavor profiles, and overall quality. More largely, this technological advancement holds the potential to revolutionize coffee farming by providing producers and agricultural specialists with sophisticated tools to enhance production efficiency, minimize costs, and improve the accuracy and confidence of their decision-making processes.

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Section: Challenges and Future Trendsmentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

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“…Strength Weakness [20] Genetic Algorithm -Superior performance to Otsu segmentation method -Obtained high dice coefficient score -Ineffective control over luminosity inhomogeneity -Small dataset [21] Support Vector Machines -Post-processing not required -Better results on instance segmentation -High computational cost -Excessive pre-processing required [23] Extreme Learning Machine -Automatic detection using the mobile application -Great results from an extreme learning machine -The image results from the camera's automatic adjustment are different -Need more segmentation adjustment for background color [24] Deep Convolutional Networks -Able to detect 13 different types of diseases -Big impact augmentation process -Small dataset -Fine-tuning does not have a big impact [25] Convolutional Neural Network Simple morphology erosion improves the detection Has a long runtime [26] YOLOv3-MobileNetv2 -Lightweight depth-wise convolutions are used -Good lighting conditions -Has a long runtime -High computational cost [27] Single The remainder of this paper is organized as follows: In Section 2, the materials and methods are described. The experimental results are described and compared with those of other recent iterative methods in Section 3.…”

Section: Reference Modelmentioning

confidence: 99%

“…The author compared the results, which showed that the method could recognize infection with high precision, as evidenced by the high dice coefficient. Dann et al [26] used the YOLOv3-MobileNetv2 model for detecting diseases in robusta coffee leaves. They develop a prototype that can capture the input images and then classify the disease into four classes: Cercospora, miner, phoma, and rust.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

2022

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Coffee is the world’s most traded tropical crop, accounting for most export profits, and is a significant source of income for the countries in which it is produced. To meet the needs of the coffee market worldwide, farmers need to increase and monitor coffee production and quality. Coffee leaf disease is a significant factor that decreases coffee quality and production. In this research study, we aim to accurately classify and detect the diseases in four major types of coffee leaf disease (phoma, miner, rust, and Cercospora) in images using deep learning (DL)-based architectures, which are the most powerful artificial intelligence (AI) techniques. Specifically, we present an ensemble approach for DL models using our proposed layer. In our proposed approach, we employ transfer learning and numerous pre-trained CNN networks to extract deep characteristics from images of the coffee plant leaf. Several DL architectures then accumulate the extracted deep features. The best three models that perform well in classification are chosen and concatenated to build an ensemble architecture that is then given into classifiers to determine the outcome. Additionally, a data pre-processing and augmentation method is applied to enhance the quality and increase the data sample’s quantity to improve the training of the proposed method. According to the evaluation in this study, among all DL models, the proposed ensemble architecture outperformed other state-of-the-art neural networks by achieving 97.31% validation. An ablation study is also conducted to perform a comparative analysis of DL models in different scenarios.

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“…It is necessary to detect their presence as soon as possible to prevent pests from destroying all existing plants. Almost all of the filtered articles discussed the detection of diseases or the health level of coffee plants through their leaves [9,10,35,41,[45][46][47][48]50,53,54]. The rest detected insect pests based on sound [32] and detected tissue characteristics using ultrasonic waves [11].…”

Section: Iot Smart Farming Technology Solution For Coffee Farmingmentioning

confidence: 99%

The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review

Mulyono

Apnitami

Wangi

et al. 2022

Green Intell. Syst. Appl.

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As one of Indonesia’s main export agricultural commodities, coffee farming faces many obstacles, ranging from plant pest organisms to climate and environmental problems. These problems can be solved using smart farming technology. However, smart farming technology has not been applied intensively in Indonesia. This paper aims to analyze the potential for implementing smart farming for coffee in Indonesia. This article presents a systematic review of the information about the potential application of IoT smart farming for coffee farming in Indonesia by applying the PSALSAR (Protocol, Search, Appraisal, Synthesis, Analysis, Report) review method. This study concludes the list of smart farming technologies for coffee that have the potential to be applied in Indonesia. Those technologies are classified based on their application scope: quality control (including subtopics like coffee quality control), climate monitoring, the anticipation of pest organisms, and coffee processing), coffee production planning, and coffee waste utilization. Regarding infrastructure readiness and the need for smart farming technology for coffee, the island of Java has the most potential for implementing smart farming for coffee as soon as possible. The high potential for application in Java is because the telecommunications technology infrastructure is ready, and the land area and coffee production are large.

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Robusta Coffee Leaf Detection based on YOLOv3- MobileNetv2 model

Cited by 19 publications

References 10 publications

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review

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