IOTS BASED COTTON DISEASE PREDICTION

Cotton crop in Pakistan's second-largest cultivational crop. In 2015-2016, the cultivational area is increased in the last 30 years from 7.86 million acres in Pakistan. With the increase of synthetical chemicals and fertilizers in cotton crop, the diseases of the cotton crop are also increased that

Project Title

IOTS BASED COTTON DISEASE PREDICTION

Project Area of Specialization

Artificial Intelligence

Project Summary

Cotton crop in Pakistan's second-largest cultivational crop. In 2015-2016, the cultivational area is increased in the last 30 years from 7.86 million acres in Pakistan. With the increase of synthetical chemicals and fertilizers in cotton crop, the diseases of the cotton crop are also increased that leads to decrease of cotton production output and loss in the economy. In the field of Data science, machine learning with a combination of IoT systems gain too much maturity in the prediction system. So, there is a need for the Cotton Crop Disease Prediction System using IoT System.  This study is based on the IoT system, which will get the environment data from sensors and using the IoT system with machine learning will predict the disease that how much the occurrence of chances for diseases to occur in the crop field. This will help the farmer community about taking precaution measurements for taking needful steps in the early stages for the prevention of disease of the cotton crop. This study is based on two different applications, a mobile-based application, and IoT Based Hardware Sensors. The IoT system has different sensors that will give the reading of temperature, humidity, PH value and the humidity in the soil. The mobile application has a machine learning algorithm that will communicate with IoT based Hardware sensors and gives predictions about the chances of disease occurrences. The successful implementation of this research in the fields of the cotton crop and taking preliminary precautionary action to prevent from disease occurrence can help in more production of cotton and boost the economy.

Project Objectives

Overall contribution of Punjab province is 70 to 85% of total cotton production. But in recent years, due to high production loss former are not highly interested to cultivate it because their production cost increased then yielding. Although, for cultivation of it they depend on less efficient, time consuming, older and manual ways. I try to introduce an easily manageable remote system for young generation of inexperienced formers. Basically, the proposed system worked in two domains IoT and machine learning.

The research aim is to:

Develop a smart phone application for timely and accurate prediction of cotton pest.

Develop an easily manageable inexpensive system for sensing and monitoring cotton crop remotely.

Project Implementation Method

The prediction using the IoT system with machine learning is done for the help of cotton crop cultivators. This study is based on the IoT system, which will get the environment data from sensors and using the IoT system with machine learning it will predict the disease that how much the occurrence of chances for diseases to occur in the crop field. This study will present a mobile application for the farmer to get a prediction about the disease to occur in the crop field. This mobile application will connect with sensors through Arduino which is single-board hardware. This single-board microcontroller has both interfaces of analog data receiving and digital pins for data sending and receiving. The IoT hardware system will interact with mobile applications to send data to the environment.

The mobile application has a machine learning algorithm that will predict the occurrence of disease with their number of chances in percentage. The IoT system has different sensors that will give the reading of temperature, humidity, pH value and the humidity in the soil. All the communication with mobile application data will be done through Bluetooth interface. This will help the farmer community about taking precaution measurements for taking needful steps in early stages for the prevention of disease of the cotton crop.

DATA USED:

the data set of cotton diseases with their favorable environment data values is used. The data set used for predicting cotton diseases has nine different attributes. These attributes are as follows.

• Disease name
• Stage of occurrence
• Duration
• Disease belonging
• Temperature from
• Temperature to
• Humidity
• PH from
• PH To

Benefits of the Project

1) Monitoring Pest Infestation and Crop Health:

Through distant monitoring, a farmer can easily collect information about the presence of insects and rodents. Sensors placed in different corners of the field detect the infestation of pests or pathogens and transmit it to a dashboard. A farmer can use this dashboard to instantly connect with his fields and manage crop health.

Remote pest monitoring has drastically reduced manual inspection and random site visits. The farmers can now target areas that are affected by bugs and spray pesticides on required areas only. This considerably reduces the unnecessary use of pesticides, minimizing the chances of crop intoxication and environmental contamination. The collected data can also be used to identify the insects' breed and their population in the affected crop zones.

2) Weather monitoring and Analytics:

The data gathered from pest detection sensors when recorded and analyzed properly can predict the attack by pests. Tracking the weather conditions and breeding patterns can also assist in identifying the threat level of pest populations.

During the breeding seasons, the probability of infestations is extreme. Moreover, rodents feed on crops to accumulate fat before they hibernate. Predictive analytics make use of such information to establish patterns and trends of probable pest outbreaks and swarm attacks. Based on the type of pest infestation and their population, the analytics feature can also recommend steps for future prevention and information for complete treatment.

3) Automated crop health monitoring:

Integrated pest management (IPM) is a process that is encouraged to favor ecological, social, and economic consequences of pest control. It is an approach that focuses on the limited usage of pesticides to manage pest damage and incur the least possible pesticide-related hazards.

The implementation of IoT in the IPM system will automate time-consuming operations such as manual data point measurement and inspection. Automation makes the process more accurate, cost-effective, and assist farmers to take instant actions based on the response from the sensors. Use of pesticides will also be optimized which will further decrease environmental contamination and harm to crop health.

Technical Details of Final Deliverable

The major crop of Pakistan is a cotton group that has the largest area of cultivation compared to other crops. It gives a large number of revenues in the economy of Pakistan, India, Bangladesh and Iran. In this study, the prediction using the IoT system with machine learning is done for the help of cotton crop cultivators. This study is based on the IoT system, which will get the environment data from sensors and using the IoT system with machine learning will predict the disease that how much the occurrence of chances for diseases to occur in the crop field. This study is based on two different applications, a mobile-based application, and IoT Based Hardware Sensors. The mobile application has a machine learning algorithm that will predict the occurrence of diseases with their number of changes in percentage. The IoT system has different sensors that will give the reading of temperature, humidity, pH value and the humidity in the soil. All the communication with mobile application data will be done through Bluetooth interface. This research has successfully communicated with the IoT based Hardware sensors got data, communicated with mobile app and predicted the cotton crop diseases. This system shows that the temperature PH and humidity has directly related with the occurrence chances of cotton crop diseases. This will help the farmer community about taking precaution measurements for taking needful steps in early stages for the prevention of disease of the cotton crop.

Final Deliverable of the Project

Hardware System

Core Industry

IT

Other Industries

Agriculture

Core Technology

Internet of Things (IoT)

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Responsible Consumption and Production

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com