Leaf Disease Detection and Remedy Suggestion Using Convolutional Neural Networks

Saraswathi, R. Vijaya; Bitla, Vaishali; Radhika, P; Kumar, T. N. R.

doi:10.1109/iccmc51019.2021.9418013

Cited by 13 publications

(1 citation statement)

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“…Since we use the CNN algorithm in our model to identify disease, we employ it for good data [4]. Convolutional Neural Networks are a class of feed forward neural networks that have the ability to process multidimensional data [3] . Predicting those leaf diseases through advanced technology like machine learning enables early detection, preventing widespread crop damage.…”

Section: Proposed Methodsmentioning

confidence: 99%

Leaf Disease Detection and Remedy Recommendation

Venkataramana

2024

IJSREM

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Agriculture impacts life and economic status of the people. Improper management of diseases results in annual loss of agricultural yield which will have serious effects on the quality, quantity and productivity if no proper care is taken. By using some automatic technique such as image processing, detection of leaf disease is quite significant and beneficial. The use of the most utilized deep learning classification mechanism, Convolutional Neural Network, helps in this regard. This paper proposes an innovative machine learning approach for automated leaf disease detection. By utilizing image processing and deep learning algorithms, the system analyzes leaf images taken with digital cameras or smart phones. Through training a convolutional neural network (CNN) on a comprehensive dataset containing healthy and diseased leaves, the system becomes adept at distinguishing between various disease types [1] . Leveraging tagged images of healthy and diseased leaves, our system showcases robustness and high accuracy. The automated image processing, particularly involving deep convolutional networks, ensures rapid and accurate results. The system's effectiveness will be gauged through extensive experimentation, comparing its performance against existing methods. Ultimately, this project contributes to the progress of precision agriculture and sustainable crop management practices. Key Words: Convolutional neural network (CNN), Pre- Processing, Deep Learning, Image Processing, Classification, Remedy Recommendation .

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Section: Proposed Methodsmentioning

confidence: 99%