Abstract
The text describes a novel approach that combines Internet of Things (IoT) technology with image processing to identify factors affecting plant growth in agriculture, such as environmental conditions or the presence of pesticides/fertilizers. By using an IoT sensing network to gather environmental data and analyzing leaf images through MATLAB software, this integrated method aims to provide insights into optimizing agricultural practices for improved crop yield and quality. This innovative combination of IoT and image processing offers a promising solution for enhancing agricultural efficiency and sustainability through data-driven decision-making.
Introduction
The introduction highlights the significance of agriculture in India, where a large portion of the population relies on it for their livelihood. By integrating technologies like the Internet of Things (IoT) and image processing, innovative solutions can be developed to enhance agricultural practices, leading to improved crop quality and economic growth. This approach combines IoT's network of interconnected devices with image processing techniques to analyze environmental factors and optimize agricultural processes for sustainable solutions.
Literature review
The Literature Survey section in the context of the text provides an overview of existing research related to the use of IoT and image processing in agriculture. It references various studies that focus on topics such as deficiency identification in plants, cloud computing applications, water regulation in agriculture, precision irrigation systems, and the integration of IoT technologies in farming practices. These studies contribute to the development of smart agricultural solutions aimed at improving crop yield and addressing challenges faced by farmers.
Methods
The methodologies described in the text involve combining Internet of Things (IoT) technology with image processing techniques to monitor and analyze environmental conditions for plant growth. By using sensors for factors like temperature, humidity, and soil moisture, along with image analysis on MATLAB, the system aims to detect changes in plant health and provide early alerts to farmers to prevent crop failure. This approach integrates IoT sensors, a camera, and data processing to optimize crop production and reduce losses through timely interventions based on environmental data and image analysis.
Algorithm for temperature and humidity
The algorithm for DHT11 (Temperature and Humidity) sensor involves initializing the sensor to send data at a specific baud rate, reading temperature and humidity values from an analog pin, displaying the values on a serial monitor, and then continuing to read data at intervals before de-initializing the sensor. This algorithm outlines the step-by-step process for collecting and monitoring temperature and humidity data using the DHT11 sensor in a sustainable solutions context, likely for environmental monitoring or control systems.
Determinant factors
The text describes the analysis of histograms representing different plant conditions based on pixel values. It explains how the distribution of pixel values in the histograms can indicate the health status of plants, such as the presence of dark spots or color variations. By examining the pixel distribution in specific ranges, like the absence of dark tones or emergence of black spots, one can infer the plant's health condition and take appropriate measures.
Histogram analysis results
Histogram analysis in the context of the study involves examining the distribution of pixel values in images to assess the health of plants based on factors like mineral deficiencies or environmental stress. By comparing histograms from different sets of images, researchers can identify patterns that indicate specific issues affecting the plants, such as dark spots representing nutrient excess or light regions indicating heat stress. This analysis, combined with data from IoT sensors monitoring environmental conditions, allows for automated and accurate assessment of plant health using image processing techniques.
Conclusion and future
The conclusion and future work section of the text discusses the potential of combining Internet of Things (IoT) and Image Processing in agriculture for automated monitoring and analysis of plant health. The integration of IoT sensors with image processing techniques allows for continuous monitoring of environmental factors and plant conditions, providing valuable data for farmers. The future work may involve further automation through the use of drones or rovers equipped with IoT sensing networks to enhance field monitoring and data collection efficiency.