7 Computational Intelligence in Agriculture
Hari Prabhat Gupta1, Swati Chopade2 and Tanima Dutta3
1 Assistant Professor in the Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, India
2 M.Tech Degree in Computer Science and Engineering from VJTI, Mumbai, India
3 Assistant Professor in the Department of Computer Science and Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
7.1 Introduction
The prime purpose of agriculture is to produce foods, vegetables, and crops by cultivating the land with natural environmental resources, such as the soil, water, etc. Food is one of the essential need for living beings to sustain life [1]. Due to climate change, problems such as crop diseases, lack of storage management, etc. can become a bottleneck to attain high crop yield. Nowadays, the maximum production of foods from the available land is necessary for the rapidly growing population. There exists low-power, highly-portable, and low-cost agricultural sensors to monitor the land for increasing crop production. Examples of agricultural sensors are temperature and humidity sensor, green sensor, camera sensor, environmental sensor, and soil moisture sensor, etc. The green sensor sense the reflectance of wavelengths for a green light to give information about chlorophyll in the leaves, evaluating Nitrogen (N) status present in the leaf. The soil moisture sensor informs about the dampness level of the soil. Next, ...
Get Emerging Computing Paradigms now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
