4 research outputs found
Detection of citrus leaf diseases using a deep learning technique
The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively
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Sheep management and production enhancement are difficult for farmers due to the lack of dynamic response and poor welfare of the sheep. Poor welfare needs to be mitigated, and each farm must receive an expert-level assessment of critical importance. To mitigate poor welfare, researchers have conducted machine learning-based studies to automate the sheep health behavior monitoring process instead of using manual assessment. However, failure to recognize some sheep health behaviors degrades the performance of the model. In addition, behavior challenges, parameters, and analysis must be considered when conducting a study based on machine learning. In this paper, we discuss the different challenges: what are the parameters of the sheep health behaviors, and how to analyze the sheep health behaviors for automated machine learning systems to be helpful in the long term? The hypothesis is based on a different review of the literature of precision-based animal welfare monitoring systems with the potential to improve management and production.info:eu-repo/semantics/publishedVersio
Identification and recognition of animals from biometric markers using computer vision approaches: a review
Although classic methods (such as ear tagging, marking, etc.) are generally used for
animal identification and recognition, biometric methods have gained popularity in
recent years due to the advantages they offer. Systems utilizing biometric markers have
been developed for various purposes in animal management, including more effective
and accurate tracking of animals, vaccination, disease management, and prevention
of theft and fraud. Animals" irises, retinas, faces, muzzle, and body patterns contain
unique biometric markers. The use of these markers in computer vision approaches
for animal identification and tracking systems has become a highly effective and
promising research area in recent years. This review aims to provide a general overview
of the latest developments in image processing approaches for animal identification and
recognition applications. In this review, we examined in detail all relevant studies we
could access from different electronic databases for each biometric method. Afterward,
the opportunities and challenges of classical and biometric methods were compared. We
anticipate that this study, which conducts a literature review on animal identification
and recognition based on computer vision approaches, will shed light on future research
towards developing automated systems with biometric methods