International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Leaf Disease Detection Using Image Processing and ML Prof.(Mrs) Jyotsna V. Barpute1, Ashwini B. Phadatre2, Gauri S. Honrao3 Assistant professor of Computer Engineering, Savitribai Phule Pune University 2,3 Pursuing Bachelor of Computer Engineering, Suman Ramesh Tulsiani Technical Campus Faculty of Engineering Kamshet, India ---------------------------------------------------------------------***--------------------------------------------------------------------1
Abstract - In most nations, agriculture is the primary
handle a variety of complex issues such as image classification, pattern analysis, and feature extraction. Deep learning allows you to identify illnesses using many different feature sets. Of these, the traditional handmade approach and DL features are the most popular feature sets. For efficiently extracting features, pre-processing such as picture enhancement, colors modification, and segmentation is required. Classifiers are applied after feature extraction. KNN, SVM, decision tree, RF, ANNs, and Deep CNN are amongst the most widely known classifiers.
source of income for farmers, and productivity estimation is a major difficulty for them. Agriculture, together with its linked industries, is indisputably India's major source of income, particularly in the country's vast rural areas. For eons, agriculture has been cultivated in every country. Agriculture encompasses both the technology and the art of nurturing plants. Agriculture performed a major role in the development of human civilization. Agriculture has been done by hand for ages. Because the world is moving toward new technologies and implementations, agriculture must also modernize. The Internet of Things is essential for smart farming. Sensors connected to the internet of things can provide all kinds of information about agricultural landscapes. We can achieve impressive results by implementing deep learning techniques such as CNN. CNN models are used to detect disease in plants via plant leaves, and CNNs have shown to be particularly effective in Machine Vision. Image processing is a technique for performing actions on images in order to improve it and extract features from them. It is a kind of signal processing in which the input is an image and the output is an image or image features. Key Words: Network
2. Proposed Work Objective The intention of this proposed methodology is by using a plant leaves image to predict the type of disease. CNN, is fully unsupervised methodology, is applied to predict the outcome. This research aims to apply CNN to verify plant leaf disease. The goal of this study is to use image recognition to detect unhealthy plant leaf areas and categorize plant diseases. One of the most important uses of image processing is picture recognition, which is an important tool for early diagnosing in crop production.
3. MOTIVATION OF WORK
Image Processing, Convolution Neural
Farmers tend to judge diseases simply examining at them through their naked eyes. However, that's not always the right strategy. Many times, a farmer should approach specialists for detection of diseases, which is timeconsuming in farmlands. Several applications have been developed using digital image processing techniques in various industries such as industrial inspection, medical imaging, remote sensing, agricultural processing, and so on.
1. INTRODUCTION The modern disease detection approach is built on the basis optical inspection by specialists, that provides for the detection and recognition of plant disorders. To do so, an enormous team of professionals, as well as ongoing monitoring by specialists, is required, with prices rising as farms become larger. Around the same time, in some regions, farmers lack proper facilities or the knowledge that they'll need to seek expertise. As a result, even though consulting specialists demand a significant fee, they also are time consuming process. Plant diseases impair agricultural production volume and quality. Plant disease compromises the integrity of the leaf, fruit, stem, vegetable, and its products. This has a big effect on productivity, which has a big influence on cost. In recent years, machine learning has gained popularity as a beneficial technique in agriculture. The concept is essential when it comes to producing, monitoring, managing, and enhancing productions. Deep learning is a type of machine learning that uses multiple layers to transform input into information. It is also used to
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