an iot based smart solution for leaf disease detection
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This paper presents an automated system utilizing IoT technology to detect diseases in plant leaves by analyzing temperature, humidity, and color variations. The data collected from sensors is processed through an Arduino system and sent to a cloud platform for diagnosis, helping farmers identify plant health. The approach aims to improve agricultural yield and quality by enabling early disease detection.
an iot based smart solution for leaf disease detection
- 1. A Novel Approach to Detect Diseases in Plant Leaf using IOT DESCRIPTION: In this paper an automated system has been developed to determine whether the plant is normal or diseased. The normal growth of the plants, yield and quality of agricultural products is seriously affected by plant disease. This paper attempts to develop an automated system that detects the presence of disease in the plants. An automated disease detection system is developed using sensors like temperature, humidity and colour based on variation in plant leaf health condition. The values based on temperature, humidity and colour parameters are used to identify presence of plant disease. Here we take samples of different leaves as the input. These leaves are then sensed by the sensors to determine different parameters based on which it is recognized to be healthy or diseased. The proposed system consists of temperature, humidity, and color sensors for collecting data from plant leaves based on variation in temperature, humidity and color of plant leaves. The data collected from the leaves consists of current environmental factors like temperature, humidity and color. The changes that a plant undergoes are captured by the temperature humidity and color sensors and analyzed with the Arduino software. The data collected from temperature, humidity and color sensors are given to Arduino UNO kit from which the information is communicated to the farmers. The system makes use of Wi-Fi shield in order to send the data from the host system to the cloud platform for analysis. The cloud platform that we have used is the www.thingspeak.com. The collected data in the cloud platform is then compared with the dataset in order to detect whether the leaf under consideration is normal or affected. The Figure.2 shows schematic diagram of the proposed work.
- 2. TECHNICAL SPECIFICATIONS: HARDWARE: Micro controller : Arduino Uno Crystal : 16 MHz Wi-Fi Module Colour Sensor DHT 11 SOFTWARE: Arduino IDE and Embedded c APPLICATIONS: In Agriculture
- 3. BLOCK DIAGRAM: Arduino DHT 11 Wi-Fi Module Power Supply Web page Colour Sensor