A Fuzzy Logic Approach for the Determination of Cavendish Banana Shelf Life

Taghoy, Darvin M.; Villaverde, Jocelyn F.

doi:10.1109/tencon.2018.8650454

Cited by 26 publications

(13 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Reduced leaves size, turning brittle Virus [42], [46] Banana bract mosaic virus Programming Interface/Software Used Studies MATLAB [4], [5], [7], [37], [42], [43], [45], [46], [50], [54]- [56] PYTHON [49], [57] Colabeler [57] WEKA [58], [59] Torch7 ML Computational Framework (LuaJIT Programming Language) [9] AgiSoft PhotoScan, (Agisoft LLC, St Petersburg, Russia) software [44]…”

Section: ) Ri 31: What Programming Interfaces Have Been Used For Impl...mentioning

confidence: 99%

“…XGBoost based on complete wavelengths was found to be the best classification model. In [5], the authors used a fuzzy logic technique to construct an automated system for determining the shelf-life of a Cavendish banana fruit based on its ethylene gas level. The ethylene gas released by the fruit is measured using an MQ3 sensor, while the temperature range inside the chamber is monitored using a DHT22 temperature sensor.…”

Section: ) Shelf-life and Ripeness Level Detectionmentioning

confidence: 99%

“…The fuzzy logic controller has been used to classify the hundreds of samples of bananas into various categories of ripeness [4]. Another fuzzy logic approach is used to determine the progression rate of banana ripeness and determine the shelf-life of banana fruit [5]. Model extracted ethylene gas content and ambient temperature for ripeness evaluation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

et al. 2022

View full text Add to dashboard Cite

for a project on "Application of IoT in Agriculture Sector" through the ICPS division under grant ID DST-319."ABSTRACT Agricultural productivity is the asset on which the world's economy thoroughly relies. This is one of the major causes that disease identification in fruits and plants occupies a salient role in farming space, as having disease disorders in them is obvious. There is a need to carry genuine supervision to avoid crucial consequences in vegetation; otherwise, corresponding vegetation standards, quantity, and productiveness gets affected. At present, a recognition system is required in the food handling industries to uplift the effectiveness of productivity to cope with demand in the community. The study has been carried out to perform a systematic literature review of research papers that deployed machine learning (ML) techniques in agriculture, applicable to the banana plant and fruit production. Thus; it could help upcoming researchers in their endeavors to identify the level and kind of research done so far. The authors investigated the problems related to banana crops such as disease classification, chilling injuries detection, ripeness, moisture content, etc. Moreover, the authors have also reviewed the deployed frameworks based on ML, sources of data collection, and the comprehensive results achieved for each study. Furthermore, ML architectures/techniques were evaluated using a range of performance measures. It has been observed that some studies used the PlantVillage dataset, a few have used Godliver and Scotnelson dataset, and the rest were based on either real-field image acquisition or on limited private datasets. Hence, more datasets are needed to be acquired to enhance the disease identification process and to handle the other kind of problems (e.g. chilling injuries detection, ripeness, etc.) present in the crops. Furthermore, the authors have also carried out a comparison of popular ML techniques like support vector machines, convolutional neural networks, regression, etc. to make differences in their performance. In this study, several research gaps are addressed, allowing for increased transparency in identifying different diseases even before symptoms arise and also for monitoring the abovementioned problems related to crops.

show abstract

Section: ) Ri 31: What Programming Interfaces Have Been Used For Impl...mentioning

confidence: 99%

Section: ) Shelf-life and Ripeness Level Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

et al. 2022

View full text Add to dashboard Cite

show abstract

“…According to the specific application classification, the research of spectrum in fruit is roughly divided into qualitative detection and quantitative detection. Including pathogen detection (Ilic et al, 2018), origin traceability (Pang et al, 2018), acidity detection (Assis et al, 2018), brittleness and hardness detection (Onda et al, 1994), shelf life detection (Taghoy & Villaverde, 2018), external damage (Blasco et al, 2009), pesticide residue detection (Wang et al, 2021).…”

Section: Study On Fruit Quality Detection Based On Spectral Technolog...mentioning

confidence: 99%

A bibliometric and visual analysis of fruit quality detection research

Luo

ZHANG

et al. 2022

Food Sci. Technol

View full text Add to dashboard Cite

To understand the research status and current dynamics of fruit quality detection and objectively reflect the influence of different countries, research institutions and authors in this field, the software CiteSpace was used to bibliometrically and visually analyze the relevant literature on fruit quality detection in Web of Science (WoS) database from 1982 to 2021. The results showed that the number of publications on fruit quality detection showed a trend of slow in the early stage, rapid in the middle stage and intensified in the late stage. China has the largest number of international publications, accounting for 21.80% of the WoS database, but its intermediary centrality is inferior to that of the United States, Germany, Italy and Spain; the United States is at the core of international cooperation, establishing cooperative relations with most countries; through keywords co-occurrence network and keyword clustering, it is concluded that the research mainly focuses on fruit quality detection based on spectral technology or electronic nose technology, fruit composition detection based on high-performance liquid chromatography (HPLC), fruit edible safety detection, and genetic analysis of fruit quality-related traits. Bibliometrics and visual analysis of this fruit quality detection research can provide a certain reference for relevant researchers.

show abstract

“…Con el paso del tiempo aparecieron los controladores avanzados, entre los cuales se pueden citar el MPC, adaptativo, en cascada, difuso o borroso, etc. [4]. Este último es utilizado en procesos no lineales y se basa en tres etapas que son: emborronado, reglas de membresía y desemborronado.…”

Section: Introductionunclassified

Controlador Difuso en Arduino Uno para un Entorno Académico

Avilés-Castillo¹,

Buele

Espinoza³

2022

Publicare

View full text Add to dashboard Cite

Los procesos de enseñanza – aprendizaje requieren una constante actualización y la incorporación de nuevos enfoques. Para esto las instituciones de educación superior necesitan realizar importantes inversiones económicas o buscar alternativas de menor costo. Por ello en este artículo se presenta la implementación de un algoritmo de control difuso en una tarjeta embebida Arduino Uno. El diseño del controlador difuso se realiza con cinco funciones de membresía tanto a la entrada como a la salida SISO (entrada simple/salida simple). Como entrada se tiene el valor del error acumulado que es la diferencia entre el valor deseado y el valor medido. Las pruebas de simulación se efectuaron en el entorno de diagrama de bloques Simulink de MATLAB. Mientras que las pruebas experimentales se desarrollaron en una estación didáctica que imita un proceso de control de presión industrial. También se obtienen otros parámetros que son parte de las curvas de proceso para un mejor análisis. De esta manera se valida esta propuesta, ofreciendo información para futuras investigaciones

show abstract

A Fuzzy Logic Approach for the Determination of Cavendish Banana Shelf Life

Cited by 26 publications

References 4 publications

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

A bibliometric and visual analysis of fruit quality detection research

Controlador Difuso en Arduino Uno para un Entorno Académico

Contact Info

Product

Resources

About