International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 11 | Nov 2022
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Plant Leaf Disease Detection using Deep Learning and CNN Sanjana Patel1 1Bengaluru,Karnataka,India
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Abstract – All around the globe, problems such as forest
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural network, most applied to analyse visual imagery. CNNs are more efficient than other image classification algorithms. As they utilize little pre-processing The learning is completely automated in CNN
extinction and food shortage are major concerns for economic development of a country and social welfare of the people. There are different reasons for these issues. One of the main reasons for these problems is diseases occurring in the plant leaf. The potential threat to humans and other animals because of the plant disease is immense. This paper attempts to provide an end-to-end solution to automate the detection of plant leaf disease and provides alert starting from the source.
whereas in traditional algorithms these filters are handengineered. This independence from prior knowledge and completely automated without any human intervention in feature extraction is a major advantage.
Through this paper we propose to create a novel low-cost implementation of disease detection using state-of-the art Deep learning concepts like Convolutional Neural Network, Activation Functions, binary_crossentropy combined with fundamental principles of RDBMS and UI/UX Our solution aims to be a robust, low-cost, hardware-independent and seamless solution for disease detection in present times. The experimental accuracy of this system is 96.4% which is good enough for real-world scenarios.
2. Literature Survey Vijai Singh et all [1] describe the plants plays an important role in agriculture field, as having disease in plants are quite natural. This paper subtly informs that proper care should be taken in certain area else it causes serious effects in plants. This paper proposes an algorithm for image segmentation which is used for automatic detection and classification of plant leaf diseases.
Key Words:
Deep Learning, Convolutional Neural Network, Binary Cross Entropy, RDBMS, Activation Function
Amar Kumar Deya et all [2] discuss leaf rot disease detection for betel vine (Piper betel L.) based on image processing algorithm. This paper presents the drawback of current manual detection and uses identifying color feature of the rotted leaf areas to find detection in plant diseases. Subsequently, the rotted area was segmented, and area of rotted leaf portion was extracted from the various plant feature data. The results showed a performance of this automatic vision-based system in practice with easy validation.
1. INTRODUCTION Plant disease have impacted society and world history. All varieties of plants wild and cultivated alike, are subject to disease. The result of the plant losses due to disease can result in hunger and starvation. Many valuable crops might be very susceptible to disease and it might be a possibility that survival of such plants without human intervention is not possible. In such cases early detection would be utmost important in order to prevent the further spread of disease to other parts of the plant. The proposed solution using deep learning techniques would be a vital component to solve these issues as it does not involve any hardware component and it is a seamless solution with high accuracy to detect diseases in leaf as early as possible and take appropriate measures.
A.P Soni et all [3] Proposed a green heart shaped betel leaf, in India it is known as Paan. This paper covers the easy, accurate, and less human intervention method of leaf area measurement. Leaf area of plants is a useful tool in physiological and agronomic studies. Around 1000 betel leaf investigation is performed and the paper includes important aspects of these investigation. These results are in turn compared with the graphical technique of leaf area measurement. The advantage of this method is the easiness and the accuracy of finding the estimated leaf area precisely.
It has become a very tedious problem for farmers to detect the plant disease and treat it with appropriate measures as early as possible. The current system that are in use are ineffective and time-consuming. So using CNN based model for disease detection would be a low-cost and an effective approach to solve this problem.
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Daisy shergill& et.al.,[4] describes a approach is useful in crop protection especially in large area farms, which is based on automated techniques that can detect diseased leaves using color information of leaves. The disease can be detected by capturing an image of a certain plant leaf
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