Smart indoor plantation system using soil moisture sensor and light dependent resistor sensor

Plantation methods, including hydroponics, have been extensively used in agriculture. They also employed a time-based irrigation system for the plant. The goal of this project was to create a self-sustaining indoor plantation system that uses soil moisture sensor data to control the flow of water when the sensor detects that the soil is almost dry. Soil conditions are monitored, and crops are irrigated more efficiently with the help of this new technology. Water is conserved by just watering the plants when they absolutely need it, rather than watering them continually all the time as the traditional method would require. Light-dependent resistors are used to measure the brightness of the surroundings in this project. As a result, the grow light will be activated when the ambient light level drops. With the help of a soil moisture sensor and a light-dependent resistor (LDR), one can create a system that automatically waters and lights plants. Finally, the soil moisture sensor collects data for the sprinkler system and displays it on the LCD screen, and then the appropriate measures are taken. When the soil's humidity level is high, the water that flows will be stopped

Computer Vision Based Machine Learning and Deep Learning Approaches for Identification of Nutrient Deficiency in Crops: A Survey

Agriculture is a significant industry that plays a major role in a country’s sustainable environment and economic development. The global population demands increased food production with minimal losses. Nutrient deficiency is one of the major and crucial factors influencing crop production significantly. Common techniques for determining crop nutrition status are the diagnosis of plant morphology, Enzymology, chemical effects, fertilization, etc. However, the above techniques are invasive and time-consuming or infeasible while considering varied production practices in different locations, environments and climatic conditions. Computer Vision is an area of Computer Science that deals with creating Artificial Intelligence based vision systems that can use image data, process, and analyze as humans perform. Early Detection of Crop Nutrient deficiencies favors the farmers to monitor the affected crops and plan for the manure or fertilizer application, which supports to regain of the crop’s efficiency for attaining its maximum yield. Modern computer vision systems rely on Machine Learning (ML), Remote sensing, Satellite imagery, unmanned aerial vehicles (UAVs), Internet of things (IoT) based sensor devices, and Deep Learning (DL) models that use algorithms to extract required features from data. The objective of this work is to provide an overview of recent research and identify the scope of computer vision-based technologies used for identifying crop nutrient content and deficiency, find research challenges in predicting nutrient imbalance in comparison with plant diseases that show certain similar characteristics, thereby to improve crop health and production