Overview / Usage

The use of technology in the detection and analysis process increases the accuracy and reliability of these processes. For example, the people who use the latest technology to analyze the diseases that arise unexpectedly are at a higher chance of controlling them than those that do not. In the recent occurrence of coronavirus, the world relied on the latest technology to develop preventive measures that have helped reduce the rate at which the disease is transmitted. Crop diseases are a significant threat to human existence because they are likely to lead to droughts and famines. They also cause substantial losses in cases where farming is done for commercial purposes. The use of computer vision (CV) and machine learning (ML) could improve the detection and fighting of diseases. Computer vision is a form of artificial intelligence (AI) that involves using computers to understand and identify objects. It is primarily applied in testing drivers, parking, and driving of self-driven vehicles and now in medical processes to detect and analyze objects. Computer vision helps increase the accuracy of disease protection in plants, making it easy to have food security.

Methodology / Approach

The process of plant disease detection system basically involves four phases as shown in fig The first phase involves acquisition of images either through digital camera and mobile phone or from web. The second phase segments the image into various numbers of clusters for which different techniques can be applied. Next phase contains feature extraction methods and the last phase is about the classification of disease.

Image acquisition,

In this phase, images of plant leaves are gathered using digital media like camera, mobile phones etc. With desired resolution and size. The images can also be taken from web.

The formation of database of images is completely dependent on the application system developer. The image database is responsible for better efficiency of the classifier in the last phase of the detection system.

Image segmentation

This phase aims at simplifying the representation of an image such that it becomes more meaningful and easier to analyze. As the premise of feature extraction, this phase is also the fundamental approach of image processing. There are various methods using which images can be segmented such as k-means clustering, otsu’s algorithm and thresholding etc. The k-means clustering classifies objects or pixels based on a set of features into k number of classes. The classification is done by minimizing the sum of squares of distances between the objects and their corresponding clusters.

Classification

For classification, a software routine is required to be written in MATLAB, also referred to as classifier. A number of classifiers have been used in the past few years by researchers such as k-nearest neighbour (KNN), support vector machines (SVM), artificial neural network(ANN), back propagation neural network (BPNN), Naïve Bayes and Decision tree classifiers. The most commonly used classifier is found to be SVM. Every classifier has its advantages and disadvantages, SVM is simple to use and robust technique.

Technologies Used

CNN

TensorFlow

Keras

OpenCV

Python

Matplotlib