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
Plant Disease Detection and Identification using Leaf Images using deep learning Aditya Shinde1, Tejas Raykar2, Prafulla Patil3, Harshvardhan Mali4, Dr.Yogesh Gurav5 1,2,3,4B.E.
Students, Department of IT,Zeal College of Engineering and Research, Maharashtra, India. Yogesh Gurav, Department of IT ,Zeal College of Engineering and Research, Mahrashtra, India. --------------------------------------------------------------------***--------------------------------------------------------------------5Dr.
Abstract - Every country's primary demand is for agricultural products. Infected plants have a negative impact on agricultural production and economic resources in the country. In agriculture, early illness detection is crucial for maximum crop production. Automatic methods for classification of plant diseases can also help in taking action after recognising the symptoms of leaf diseases. In the agricultural industry, plant disease detection is vital since it impacts the plant's robustness and health, both of which are important variables in agricultural productivity. These problems are common in plants, and if appropriate preventative actions are not taken, the culture may suffer serious consequences. In the real world, disease detection is currently based on an expert's opinion and physical examination, which is timeconsuming and costly. We're introducing artificial intelligence-based automatic plant leaf disease detection and classification for quick and easy disease diagnosis and classification. Our method's principal purpose is to boost agricultural crop productivity. Picture collection, image preprocessing, segmentation, and classification are just a few of the processes we went throughin this process. Key Words: Image Processing, Dataset, Agriculture, Plant 1. INTRODUCTION Agriculture has a crucial part in the economic development of any country. It is a field that has a considerable impact onthe gross domestic product of a country. The agriculture sector in India accounts for around 16% of the country's GDP. A variety of factors influence the quality and quantity of crops grown. As a result of fluctuating weather and local conditions, these plants are prone to a variety of diseases. These disorders can also lead to considerable financial losses if they go unnoticed. In India, diseases, pests, and weeds claim 15 to 25% of crops. Plants are incredibly important in our lives since they generate energy and aid in the fight against global warming. Many diseases today affect plants, resulting in major economic, social, and ecological consequences. As a result, it's vital to identify plant disease precisely and rapidly. The irrepressible or non-infectious nature of the essential causal operator of plant diseases is used to classify them. The most extensively used approach for plant disease detection is expert naked eye observation, which allows specialists to identify and diagnose plant diseases. This necessitates a huge staff of experts as well as ongoing expert monitoring, both of which come at a great cost when farms are large. At the same time, in certain nations, farmers lack adequate facilities or even the knowledge of how to contact professionals. As a result, consulting specialists is both expensive and time-consuming. In this situation, the suggested technique works well for monitoring wide fields ofcrops. It is also easier and less expensive to identify diseases automatically by simply looking at the symptoms on the plant leaves. Plant disease identification by sight is a more time-consuming and inaccurate task that can only be performed in limited locations. Automatic detection, on the other hand, requires fewer efforts, takes less time, and is more accurate. Some common plant diseases include bacterial, black spotting, andrust, viral, and red cotton leaf. Image processing is a technique for calculating the size of the diseased area and determining the colour difference in the afflicted area. 2. LITERATURE SURVEY They research the limit of SVM related with millimeter-wave(mm-wave) low-terahertz (THz) assessments. In any case, they took care of the issue of collection a mix of natural itemswith a multiclass SVM using the Digital Binary Tree designing. With this procedure, the mix-up rate doesn't outperform 2%. Moreover, moved from the W-to D-band (low THz). The standard explanation is the addition of the flat objective and the probability to have more negligible systems in the viewpoint on a cutting edge game plan. They have noticed an outrageous decay diverged from the microwave region. It is unsurprising with the lead of the water, which is one of the essential pieces of the apple. Then, arranged the SVM with the D-band informational collectionin conclusion played out the portrayal on dark models and gained an accuracy of 100% [1]
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