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Agriculture plays a vital role in the present global economy. Continuous pressure on the agricultural system results in increased expansion of the human population. Continuous researches have been performed in our past, to study the interactions between plant immune responses and pathogens. Previously, researchers usually applied large-scale genetic screening and genomic approaches to identify genes and proteins of interest. But now, the event of machine learning algorithms, which are a group of analytic methods that modify model building process and iteratively learn from information to gain knowledge without expressly programming, provides additional powerful and best tools to classify plant diseases from images of infected leaves.
Machine learning is considered as one of the branches in Artificial Intelligence to work automatically or to predict information or to give instructions to a system to perform an action. The aim of Machine Learning is to explain the structure of the data and fit that data into models that can be utilized by the researchers. This project work involves analysis of various machine algorithms which is applied to plant disease prediction. Machine Learning technology can accurately find out the presence of pests and disease in the leaves of a plant. Upon this Machine learning algorithm, one can predict the chance of any disease and pest attacks in the future.
Why: Problem statement
Normal manpower for monitoring will not be possible to predict the exact amount and intensity of pests and disease attacked on the farm for spraying fertilizers/pesticides. Usually in India, farmers manually observe and use some applications which have huge database limitations. According to a proverb, prevention is better than cure, our project aims at the prediction of flora disease and suggests remedies using machine learning.
How: Solution description
This project helps farmers to protect their farmyard from all kinds of pests and disease attacks and fix them.
In this project, folded neural network models were created to predict plant disease detection and diagnosis using only the leaves images diseased plants, through deep learning methodologies.
Machine learning has become prominent with big data technologies and high-performance computing to create new opportunities for data-intensive science. In this project, we present a comprehensive review of research applications of machine learning in agricultural systems. The prediction and diagnosis in this project demonstrate how agriculture will benefit from machine learning technologies. By using machine learning to sensor data, farm management systems are entering into real-time artificial intelligence. hence these enabled programs provide rich recommendations and insights for farmer decision support and action.
Using python code, the first step is to collect samples and feed the data, then we train the received sample as healthy and unhealthy. if it is predicted as unhealthy we are suggested with remedies then exit.
We have used python software to obtain the solution for this project. the packages required are listed below:
How is it different from competition
The infection of leaves by pests affects the agricultural production of the country. But usually, farmers or experts observe the leaves with the naked eye for detection and identification of disease. But applying this method, it may be time-consuming, high cost and inaccurate. Automatic detection or prediction using image training techniques gives us quick and accurate results. This paper is concerned with a new idea to the development of the plant disease recognition model, based on leaf image classification, and simultaneously gives us remedies. The existing plant disease prediction project helps us only to predict if the leaf is infected or not. But in this project, we will be able to predict the disease and suggest remedies as well.
Who are your customers
Agriculture student for research purpose
Project Phases and Schedule
Phase 1: Data Collection(plant images)
Phase 2: Processing and segmentation
Phase 3: Feature extraction and selection
Phase 4: Analyse image
Phase 5: Disease prediction and suggested remedies