Abstract
A device composed of actuators is the internet of things. The internet of things (IoT) should be used for enhancing agricultural efficiency in precision agriculture. The bedrock of the Indian economy, agriculture, is adding to the country's total economic performance. Nevertheless, the efficiency contrasts with world norms. Regardless of the usage of minimum agricultural advancements and farmers from villages today for other productive enterprises, regions move to a metropolitan region, and they cannot rely on agriculture. Farming creativity is not new, but smart farming is expected to be pushed to the following internet level by IoT, a unit made up of actuators or sensors. This chapter demonstrates IoT's role in agriculture and its use in identifying plant diseases through leaf images. Several researchers' works in the domain are also outlined, and future perspectives of IoT in recognizing plant diseases are discussed briefly.
TopIntroduction
India is predominantly a nation of agriculture. More than 60% of the Land area in India is used for agriculture in terms of overall agricultural space, and it is the second-most populous country. Farmers adopt conventional methods such as manual field inspection, which requires regular irrigation of crops and applying pesticides with or without knowledge of the volume of pesticides to be added to combat pests, Ferentinos (2018). There are risks of these conventional methods, such as waterlogging in fields, improper pesticide use, fungicides, lack of knowledge about the variety of crops cultivated for a specific type of land. Low-quality Production, lack of soil fertility, and predicted output from agricultural land are the consequences of these disadvantages.
In comparison, a recently created service such as Cloud-IoT allows a free online service that helps the remotely installed sensors to be plugged and played. As farmers have begun to implement innovative farming strategies that have resulted in increased crop yields, developments in agricultural methods have rendered it relatively straightforward. Previous processes were time-consuming, expensive, and needed more labor, but it is clear to execute multiple activities now in no time, Singh et al.(2021). Earlier diagnosis of the disease was entirely based on conventional techniques as farmers used their expertise and employed a professional to diagnose signs to locate the condition and take some required preventive steps. The traditional approach has various disadvantages as it relies on the expert's eyesight, has been very time-consuming, slow, labor-intensive, and loses accuracy Singh et al.(2020). Therefore, it is necessary to substitute manual techniques for detecting diseases with automatic techniques since one of the critical issues is the consistency and protection of agricultural products. Seeing conditions at their earlier stages is very significant since their spread can be easily managed without hindering crop quality and productivity, Singh et al.(2020). This chapter introduces diverse methods for the identification of diseases suggested by scholars from all around the world.
Key Terms in this Chapter
Smart Agriculture: Smart farming is a big leap from traditional farming as it brings certainty and predictability to table. Robotics, automation, and cloud software systems are tools for smart farming.
Cloud Computing: Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet.
Image Processing: Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Internet of Things: Is the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data.