INTELLIGENT MUSHROOM CULTIVATION SYSTEM WITH IMAGE BASED DISEASE DETECTION

Abstract

Mushroom cultivation is increasingly favored for its space and time efficiency/ alongside its high nutritional value. However, traditional manual methods often lead to inefficiencies and waste, limiting optimal yields. This study presents an innovative loT and machine learning-based smart mushroom cultivation system that utilizes an ESP32 microcontroller and agricultural sensors for automated monitoring. The system optimizes- environmental conditions and accurately detects diseases, facilitating the separation of affected mushrooms. Key features include real-time data analysis, scalability, and adaptability for various mushroom species. The K-Means Clustering algorithm and Support Vector Machines (SVM) are employed for effective image processing, enhancing the identification of mushrooms and disease detection. By addressing the limitations of manual methods, this loT and ML-based system revolutionizes mushroom cultivation, promoting efficiency, productivity, and sustainability. This approach not only improves mushroom quality and yields but also reduces labor costs and environmental impact, offering a viable solution for modern agricultural practices.

Specification

FIELD OF INVENTION:

The innovation project aims to monitor, control, and detect infectious diseases in mushrooms. Mushrooms are a rich source of protein and play a significant role in daily nutrition. Infectious diseases negatively affect mushroom growth and can decrease mushroom cultivation. To address this problem, an intelligent mushroom monitoring system and image-based disease detection techniques have been designed.

BACKGROUND OF INVENTION:

• The invention of an "Intelligent Mushroom Cultivation System with Image-Based Disease Detection" draws insights from two key research papers. The first paper, "Smart Farming using loT and Machine Learning with Image Processing" by Supriya Ghavate and Joshi H. U. presents a smart farming system that integrates Internet of Things (loT) technology and machine learning with image processing to enhance agricultural productivity. The system utilizes loT sensors to monitor key environmental factors such as soil moisture, temperature, and humidity in real-time, employing a NodeMCU microcontroller and CC3200 single chip for data collection. A camera interface captures images of plants for disease detection, and machine learning algorithms analyze these images to provide farmers with disease information and solutions via an Android mobile application.

• The second paper, "Automatic Temperature and Humidity Control System by Using Fuzzy Logic Algorithm for Mushroom Nursery", written by Theeramet Kaewwiset and Paitoon Yodkhad, introduces a Fuzzy Logic-based control system specifically designed for mushroom nurseries. This system collects temperature and humidity data via sensors, which are processed by a microcontroller to automatically manage fans, mist sprayers, and heaters. It is tested on various mushroom types, such as Straw, Angel, and Oyster mushrooms, and demonstrates superior control over environmental conditions compared to manual methods. The system reduces human intervention while increasing the precision of environmental adjustments, leading to better productivity in mushroom cultivation.


OBJECTIVES:

Our primary objective of this project i is to develop a smart mushroom monitoring and controlling system that utilizes temperature, humidity, and soil moisture sensors to optimize growing conditions. Additionally, it incorporates an advanced night vision camera to capture images of the mushrooms for disease detection. The captured images are processed using a K-means clustering algorithm to identify and classify the mushrooms, determining whether they are infected with disease. The system then displays the results, indicating the health status of the mushrooms, thereby enabling timely interventions to ensure optimal growth and yield.

SUMMARY:

• The system employs ESP32 microcontrollers and agricultural sensors to enhance various aspects of mushroom cultivation.

• The DHT11 sensor continuously monitors temperature and humidity levels, displaying the results on an LCD screen. The optimal temperature range for mushroom growth is maintained between 55 to 65 degrees Celsius, with a relative humidity of 75 to 78%.

• If the temperature decreases below the set range, a Peltier module activates to restore the appropriate temperature.

• When humidity levels drop, a water pump is activated to spray water,
ensuring adequate moisture for mushroom growth.


The system simultaneously monitors soil moisture levels to prevent water clogging, promoting a healthy growing environment.

• A high-vision camera captures images of the mushrooms at regular intervals for monitoring purposes. The captured images undergo preprocessing, followed by analysis using the K-Means algorithm to determine if any mushrooms are infected.
BRIEF DESCRIPTION OF DRAWINGS:

FIGURE 1: Block diagram of the monitoring and image processing system

The diagram illustrates a system for controlling mushroom growth using an integrated approach. It includes sensing components for monitoring various environmental factors like temperature, humidity, soil moisture, and light intensity. These sensors transmit data to a microcontroller that processes the information and communicates with a cloud platform for further analysis and processing. Based on the data received, the microcontroller controls various aspects of the mushroom environment. This includes adjusting the illuminating light levels and activating a water sprayer. The system also incorporates image processing, with a camera capturing images of the mushroom growth. These images are then analyzed using machine learning techniques running on a PC, helping to optimize growth conditions and predict potential problems. This integrated system leverages technology to create a more efficient and controlled environment for cultivating mushrooms.

FIGURE 2: DATA COMMUNICATIONS

The diagram shows a farm automation system connected to the internet of things. The system is comprised of a cloud server, a remote monitoring and control app, an ESP32 SoC, a sensing module, and a farm automation system. The cloud server acts as the central hub for data and control, while the remote monitoring and control app allows users to monitor and control the system from anywhere. The ESP32 SoC is a microcontroller that connects the farm automation system to the internet, and the sensing module collects data from the farm environment. The farm automation system is responsible for controlling the farm's environment, such as temperature, humidity, and irrigation. The mushrooms are presumably the target crop being grown in this system.

FIGURE 3: MONITORING PARAMETERS

This figure shows a LCD displaying the humidity and temperature of the mushroom kit.

FIGURE 4: ALGORITHM FOR IMAGE PROCESSING

FIGURE 5: MACHINE LEARNING METHOD

Infected mushrooms are identified and captured through images, which are then normalized to a standard size to account for varying dimensions and aspects. Preprocessing techniques are applied to enhance image quality, including contrast enhancement, resizing, and noise reduction. These processes improve clarity and accuracy, preparing the images for analysis. Normalized images undergo refinement, and sample pre-processed images demonstrate the improved results. This standardized approach enables effective analysis, facilitating machine learning algorithms to detect diseases and classify mushroom types accurately, paving the way for automated mushroom cultivation monitoring and optimization.

FIGURE 6: IDENTIFYING THE DEFECTED PARTS

This figure shows the defected part of the mushroom and the defected part is highlighted to specify the particular part

CLAIMS
WE CLAIM:

Claim 1: A system for monitoring, controlling growth and detecting infected mushrooms this includes:

- The system is responsible for maintaining essential conditions such as temperature, humidity, and soil moisture for optimal mushroom growth.

- The DHT11 sensor continuously monitors both temperature and humidity levels, displaying temperature in degrees Celsius (°C) and humidity in grams per cubic meter (g/m3).

- The Soil Moisture Sensor (SKU A87568) provides continuous measurements of soil moisture levels to ensure the substrate remains adequately damp.

- A Peltier module is employed to regulate and maintain the required temperature and humidity levels within the growing environment.

- A water pump is utilized to increase soil moisture, ensuring the substrate meets the necessary damp conditions for mushroom growth.

- The ESP32 module facilitates communication of environmental data to the user via Wi-Fi or Bluetooth. It also supports the display of real-time variables on an LCD screen for convenient monitoring.

- The HD CAMERA V380-IPC 3 captures images of the growing mushrooms at regular intervals for subsequent image processing.

- Captured images of mushrooms, which may vary in form and dimensions, are pre-processed to standardize their size, eliminate noise, remove backgrounds, and reduce unwanted distortions.

- Segmentation is performed to identify areas of interest, specifically regions that may be infected. This is achieved through the K-Means Clustering algorithm, which effectively isolates infected areas.

- Classification using Support Vector Machines (SVM; provides enhanced accuracy and reliable results of whether the mushroom is nfected or not.

Claim 2: The system of Claim 1, wherein the control module comprises a Peltier element for temperature control and a water pump for soil moisture adjustment.

Claim 3: The system of Claim 1, wherein the image processing unit utilizes K Means clustering algorithm for image segmentation.

Claim 4: The system of Claim 1, wherein the image processing unit utilizes Support Vector Machine algorithm for identifying the Area o-' infected region.

Claim 5: The system of Claim 1, wherein the sensors continuously monitor temperature, humidity, and soil moisture, and transmit data to the ESP32 module at predetermined intervals.

Claim 6: The system of Claim 1, further comprising an alert system that notifies the user via email, SMS, or mobile app when temperature, humidity, or soil moisture levels deviate from predetermined ranges.

Claim 7: The system of Claim 1, wherein the Temperature and humidity is continuously displayed in the LCD display.

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