Smart Farming Using Deep Learning Technique

  • S Sri Heera, Rahul D, S Sudarshan Athreya, S Suriya Narasimman, K Muthu Harini


The trend and technology of the world is always set in the direction of optimization and precision. The agricultural sector has also been changing rapidly to keep up with modern times. It has been made clear that crops require adequate amounts of nutrients alongside water to grow into healthy plants that can sate the human populous. Previous irrigation systems use either permanent sensors to measure moisture content or use robots to irrigate the plants. Existing technology can measure the growth of plants using only the diameter or length of leaves. Currently Weeds can be only detected using permanent fixed equipment. The fixed sensors may trouble the growth of roots. The robots since being wireless may not be able to transmit large amounts of data for providing efficient and fast communication. For some plants the growth prediction may not be accurate mostly when the plants are mature. Weed’s precise locations cannot be determined for automatic weeding to be performed. The proposed system aims to create a model to monitor both external and internal status of the plants. Techniques like Computer Vision for image processing and Long Short-Term Memory is used for the growth monitoring of the plant and Convolutional Neural Network is used for weed detection. Apart from identification of the weed, the exact location is marked and required action is taken with the help of drones. The communication is done using Global System for Mobile Communication to achieve fast data transfer.

How to Cite
S Sri Heera, Rahul D, S Sudarshan Athreya, S Suriya Narasimman, K Muthu Harini. (2020). Smart Farming Using Deep Learning Technique. International Journal of Control and Automation, 13(02), 771 - 775. Retrieved from