International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 05 | May 2022
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
LEAF DISEASE IDENTIFICATION AND REMEDY RECOMMENDATION SYSTEM USINGCNN G. Vijendar Reddy 1, a), R. Manikanta raj 2, b), V. Kalyan 3, b), V. Anudeep4, b), C. Ganesh 5, b) Associate Professor, Department of IT, Gokaraju Rangaraju Institute of Engineering and Technology 2,3,4,5 Student, Department of IT, Gokaraju Rangaraju Institute of Engineering and Technology ----------------------------------------------------------------------------***---------------------------------------------------------------------------Abstract- Agriculture is one field which has a high impact on life and economic status of human beings. Improper 1
management leads to loss in agricultural products. This process is to detect the leaf disease detection using the deep neural network, the alternative of conventional neural network. This can easily detect the disease of leaf. First user can upload the image of the leaf and it will upload the image on the screen. Then analyze the image by pressing the button. The disease can analyze and show the status of a leaf that is healthy or unhealthy. The disease can be detected in the image of a leaf. This work utilizes an open dataset of 1500 pictures of unhealthy and solid plants, where deep convolutional systems and semi supervised techniques are used to characterize crop species and detect the sickness status of 3 distinct classes.
Keywords: deep neural network, deep convolutional systems, semi supervised techniques. I.
INTRODUCTION
Agriculture is one of the main positions in the world. The Fundamental needs for all the living things is Food, henceforth it assumes a critical part. Accordingly, it has become fundamental to work on the nature of horticultural merchandise. It is basic to deal with these yields accurately from the beginning. A plant's life expectancy has various stages. Soil readiness, planting, adding compost and manures, water system techniques, infection conclusion, pesticide use, and yield collecting are totally included. For instance, bugs, animals, weeds, nematodes and illnesses causes the crop yield incidents of around 30%-41%. Crop contaminations, as per a few evaluations, cause normal result misfortunes of 42% for the main food crops. Leaf Diseases debilitate trees and bushes by disrupting the photosynthesis, the cycle through which the plants produce energy. Accordingly, the illness forecast at the beginning phase is basic. Crop illnesses may now and then clear out a whole yield's efficiency. Therefore, ranchers should learn all that they can about editing sicknesses quickly so they can really control them. Because of the contemporary populace's rising craving for food and food things, horticultural frameworks have embraced a wide way to deal with utilizing manures for development goals. This model spotlights on recognizing leaf sicknesses from the get-go, lessening the probability of the whole plant being annihilated. Manual assessment of a leaf and ailment forecast are two customary methods of illness recognition. Notwithstanding, this technique doesn't permit a rancher to pinpoint the exact infirmity. Thus, understanding appropriate sickness picture handling strategies might be utilized to distinguish plant leaf infection. These are state of art methodology that utilizestate of art innovation to give exact results.
II. EXISTING SYSTEM Disease identification in existing systems is accomplished using successful methods such as K-mean clustering, texture, and colour analysis. It employs texture and colour traits that are common in normal and afflicted areas to identify and differentiate distinct agriculture. Conventional multiple regression, artificial neural networks (back propagation neural networks, extended regression neural networks), and support vector machines are some of the other approaches used (SVM). The SVM-based regression strategy resulted in a more accurate representation of the association between environmental circumstances and illness level, which might be valuable for disease management.
DISADVANTAGES OF EXISTING SYSTEM: The results showed that using an SVM-based regression strategy not only improved the description of the link between environmental factors and disease level, but it might also be effective for plant disease detection. Existing approaches such as k-means and SVM are inefficient, taking a long time to analyse and forecast with low accuracy.
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