دسته بندی:
بینایی ماشین - Machine vision
سال انتشار:
2022
عنوان انگلیسی مقاله:
Plant leaf disease detection using computer vision and machine learning algorithms
ترجمه فارسی عنوان مقاله:
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین
منبع:
ScienceDirect- Elsevier- Global Transitions Proceedings, 3 (2022) 305-310: doi:10:1016/j:gltp:2022:03:016
نویسنده:
Sunil S. Harakannanavar
چکیده انگلیسی:
Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom-
mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to
the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind
the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in
plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing
with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the
samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm-
ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to
256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means
clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted
using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis
and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally,
the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM),
Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is
tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples.
keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease
قیمت: رایگان
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