An IoT Based Agricultural Management Approach Using Machine Learning

Kasiselvanathan, M.; Sekar, G.; Prasad, J.; Lakshminarayanan, S.; Sharanya, C.

doi:10.1109/icidca56705.2023.10099598

Cited by 6 publications

(1 citation statement)

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“…A new field, automatic leaf disease detection using machine learning, uses image processing methods and machine learning algorithms to detect and diagnose diseases in plant leaves. Rule-based and machine-learning-based methods [12] can be used to categorize the automatic leaf disease detection systems now in use. Rule-based systems rely on a preset set of guidelines based on the visual traits of the condition.…”

Section: Overview Existing Systemmentioning

confidence: 99%

Machine Learning Based Automatic Leaf Diseases Detection

et al. 2023

E3S Web Conf.

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The method for applying machine learning to automatically detect leaf diseases is presented in this paper. A convolutional neural network was used to extract pertinent features from leaf image datasets that included healthy and diseased leaves. The dataset was compiled and pre-processed. Accuracy, precision, and recall measures were used to assess the machine learning algorithm after it had been trained on the labeled dataset. According to the findings, the algorithm was very precise and recallable in its ability to detect leaf illnesses, making it a potential method for practical use. This strategy may help with early leaf disease identification and prevention, increasing crop productivity and lowering the demand for toxic pesticides. Here we are identifying the Bacterial spot, Early blight.

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Section: Overview Existing Systemmentioning

confidence: 99%