In agricultural economies worldwide, plant diseases are a major cause of economic losses. In this paper we propose an automated method for real time crop monitoring and disease detection. Images were captured on a daily basis for a field of 8 acres of land. Image features are extracted using the Speeded-Up Robust Features (SURF) after the Maximally Stable External Regions (MSER) method find blobs. The features are used to classify the images using Kmeans Clustering in the training phase. The ground truth diseased crop images are stored in a database with the same features to act as prototypes and are compared to real time images for disease detection using nearest neighbor classification. The experimental dataset currently consists of rice crop and maize crop with 100 diseased images and approximately 1000 normal crop images. Results show 83.3% accuracy and provide information to farmers about their crop and if required alert them to disease, allowing for corrective action. There is scope to extend the classification and detection method to real-time platforms. Such applications would prove to be a valuable tool for agricultural yield management, especially since the field of interest covered may be very large and the diseases may not be uniformly distributed.
Internet of Things (IoT), an emerging network of physical objects, acts as catalyst for the future connected world. It is estimated that there will be around 50 billion connected objects by year 2020. An IoT enabled connected world improves the way human live and interact with surroundings. Through IoT valuable information and services are available to humans on demand and in real time. But these information and services may also cause harm at certain level if not thoroughly observed. With the advent of IoT, the future of the connected world will face new types of security threats since more than half of the total connected objects today are exposed to such threats and vulnerability and this number may increase as more devices are getting connected to internet. Security is the major concern in designing IoT systems since the data collected by IoT objects may be critical and also data transmitted and processed by overall IoT system may be sensitive and may lead to issues with safety, privacy, authorization and authenticity etc. Therefore while taking advantage of IoT we must also consider the ways, to the highest possible degree, to prevent the future IoT connected world from harming us. Cyber security in IoT deals with protecting connected objects for data authorization, authentication, tempering and losses as well as identifying potential risks to the system. This paper provides a brief review on how to adopt security practices in designing IoT systems to make them secure and safe.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.