Abstract
Agriculture is a process of growing crops, soil cultivating, it provides food and fabric and helps for growing country's economy. In India more than 50% of people directly and indirectly depend on the agriculture. For developing agriculture, main interceptions are weather hazards and the crop diseases. Weather hazards cannot be prevented, but the loss that occurs due to crop diseases can be reduced. This can be achieved by identifying the crop disease as early as possible and it is also important to identify the type of crop diseases for preventing the spreading of the disease. In India, we have 160 million hectares of arable land and it is second largest country after the United States. Identifying the crop diseases by human action manually is practically difficult and it is hard to identify the type of crop diseases. So, many researchers involved in to identify the crop diseases based on the image processing for helping the real-time gadgets which can be used to identify the crop disease and its types. This survey focuses on the investigation on the different surveys carried out and work related to the crop disease identification and detection based on the image processing. Computer vision and image processing-based work will help to detect of crop diseases along with many practical based applications like drones, IoT based devices etc. In recent studies most of the works are depending on the machine learning and deep learning-based image processing on various studies of predictions. After analyzing the related work on crop detection based on image processing, most of the works achieved better results based on deep learning algorithms compared to the machine learning algorithms.
Highlights
Farming had very important role in the economic growth of any Country
For Machine Leaning algorithms require labelled data for classification and for predicting the class based on the features. In this survey they used three types of features taken from the images of leaves of plants for predicting the plant diseases
In the following table we can see the accuracy achieved by ML algorithms for predicting the plant disease
Summary
Farming had very important role in the economic growth of any Country. It is the field which highly affects the Gross Domestic Product (GDP) of the countries. For Machine Leaning algorithms require labelled data for classification and for predicting the class based on the features. In this survey they used three types of features taken from the images of leaves of plants for predicting the plant diseases. By collecting the color information diseases leaves can be predicted For this image should convert to RGB to HSV color space is required. After collecting the HOG features, applied the classification algorithms for training and testing to find the accuracy In this survey they applied SVM, Gaussian Naïve Bayes, Logistic Regression, Linear Discriminate analysis and Random forest algorithm.
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