RETRACTED: Deep learning framework for leaf damage identification

Sánchez-DelaCruz, Eddy; Lopez, J.P.; Alabazares, David Lara; Tello-Leal, Edgar; Fuentes-Ramos, Mirta

doi:10.1177/1063293x21994953

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…The RelieF algorithm detects the restrictive reliance between used features. [69] also use multi-class categorisation is to connect labelled input with the appropriate outputs. This method achieves 95.90% accuracy.…”

Section: Machine Learning Based Classifier Techniquesmentioning

confidence: 99%

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology. By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.

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Section: Machine Learning Based Classifier Techniquesmentioning

confidence: 99%

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis

Asim

Ullah

Razzaq

et al. 2022

International Journal of Food Properties

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Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living

Sonawani¹,

Patil²

2023

IJPCC

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Purpose Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology. Design/methodology/approach This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality. Findings The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data. Originality/value This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

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RETRACTED: Deep learning framework for leaf damage identification

Cited by 5 publications

References 38 publications

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis

Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living

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