Detection and Classification of Cotton Leaf Diseases Using Faster R-CNN on Field Condition Images

Sarwar, Rehan; Aslam, Muhammad Arsalan; Khurshid, Syed Khaldoon; Ahmed, Tauqir; Martinez-Enriquez, A. M.; Waheed, Talha

doi:10.31080/asag.2021.05.1066

Cited by 7 publications

(1 citation statement)

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“…Thusly, our work would assist ranchers with forestalling plant illnesses, increment efficiency and benefit. Our future work focuses on a superior informational collection with countless qualities and furthermore execution of yield expectations, preventive activities, and restorative activities, pesticides required and plausible expense for proposed pesticides [21]. This framework can be stretched out to some other yield having the accessibility of enough huge datasets for that harvest.…”

Section: Researchers Who Used Framework To Detect the Cotton Leaf Dis...mentioning

confidence: 99%

Detection of cotton leaf disease with machine learning model

Hyder,

Talpur

2024

Turkish Journal of Engineering

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This study aims to use a machine learning (ML) model to accurately classify four datasets of cotton crop leaves as either infected or healthy. Bacterial blight, Curly virus, Fussarium Wilt, and healthy leaves were used as the datasets for the study. ML is a useful tool in detecting cotton leaf diseases and can minimize the rate of disease. The problem is that without machine learning technique it is very difficult and time consuming to detect the diseases then to sort out this problem a machine learning model is proposed and to test the accuracy of the proposed model, the confusion matrix concept was used. The researchers have done their research works to diagnose the diseases by using (ML) model but the drawback of their research was that the results which were given by the different (ML) models were not accurate. The target of the study was to identify diseases affecting the cotton plant in the early stages using traditional techniques. However, utilizing various image processing techniques and machine learning algorithms, including a convolutional neural network, proved to be helpful in diagnosing the diseases. This technological approach can simplify the detection of damaged leaves and minimize the efforts of farmers in detecting those diseases. Cotton is a natural fiber produced on a large scale, and it is grown on 2.5% of overall agronomic land. The detection of cotton leaf diseases is crucial to maintain the crop's productivity and provide reliable earnings to farmers. A confusion matrix is N X N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by machine learning model. This technique has four parameters to test the accuracy of the results which is given in my research work.

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Section: Researchers Who Used Framework To Detect the Cotton Leaf Dis...mentioning

confidence: 99%