DATA DRIVEN OPTIMIZATION OF CROP SELECTION AND CULTIVATION USING MACHINE LEARNING & IOT

Abstract

DATA-DRIVEN OPTIMIZATION OF CROP SELECTION AND CULTIVATION USING MACHINE LEARNING & IOT ABSTRACT In the face of growing global population and climate change, sustainable agriculture is essential. This paper explores the potential of Machine Learning (ML) to enhance crop selection and cultivation practices to address these challenges. Traditional agricultural methods often fall short in adapting to rising population demands and evolving climate conditions. ML, with its data-driven and predictive abilities, can revolutionize crop management by analyzing factors such as soil quality, climate conditions, and historical crop performance. The study investigates various ML algorithms, including neural networks and decision trees, to optimize crop selection for specific regions. Additionally, it examines the use of real-time data acquisition methods, such as loT sensors and satellite imagery, to provide a comprehensive view of agricultural landscapes. Integrating ML into precision agriculture improves irrigation, fertilization, and pest control, promoting sustainable farming practices, reducing resource waste, and supporting global food security.

Specification

FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
The Patents Rules, 2003
RROV4SIONAL/COMPLETE SPECIFICATION
(See section 10 and rule 13)

1. TITLE OF THE INVENTION
"Data-driven optimization of Crop Selection and Cultivation using Machine Learning & IoT"

2. APPLICANT(S)
(a) NAME:
(b) NATIONALITY:
(c) ADDRESS:
SRI SAIRAM ENGINEERING COLLEGE
INDIAN
Sri Sairam Engineering College,
Sai Leo Nagar, West Tambaram, Chennai - 600044
(a) NAME:
(b) NATIONALITY;
(c) ADDRESS:
ENIYAN A
INDIAN
Department of Electronics and Instrumentation
Engineering, Sri Sairam Engineering College,
Sai Leo Nagar, West Tambaram, Chennai-600044.
(a) NAME:
(b) NATIONALITY:
(c) ADDRESS:
ABDUL AZEEZP
INDIANDepartment
of Electronics and Instrumentation
Engineering, Sri Sairam Engineering College,
Sai Leo Nagar, West Tambaram, Chennai-600044.
(a) NAME:
(b) NATIONALITY:
(c) ADDRESS:
BHARATH KUMARS
INDIAN
Department of Electronics and Instrumentation
Engineering, Sri Sairam Engineering College,
Sai Leo Nagar, West Tambaram, Chennai-600044.

(a)
NAME: SUSHANTH P
(b)
NATIONALITY: INDIAN
(c)
ADDRESS: Department of Electronics and Instrumentation
Engineering, Sri Sairam Engineering College,
Sai Leo Nagar, West Tam ba ram, Chennai-600044.
(a)
NAME: Dr. T. Sathies Kumar
(b)
NATIONALITY: INDIAN
(c)
ADDRESS: Associate Professor, Department of Electronics and
Instrumentation Engineering, Sri Sairam Engineering College, Sai Leo Nagar, WestTambaram, Chennai-600044.
(a)
NAME: Dr. S. Arunprasad
(b)
NATIONALITY: INDIAN
(c)
ADDRESS: Associate Professor, Department of Mechanical
Engineering, Sri Sairam Engineering College, Sai Leo Nagar, West Tambaram, Chennai-600044.
3. PREAMBLE TO THE DESCRIPTION
PROVISIONAL
The following specification describes the invention?
COMPLETE
The following specification particularly describes the invention and the manner in which is to be performed.
4. DESCRIPTION (Description shall start from next stage.) Annexed along with this form
5. CLAIMS (Claims should start with the preamble - "We claim" on separate page)
Claims are attached at the end of the specification
6. DATE AND SIGNATURE (to be given at the end of last page of specification) Given at the end of the specification
7. ABSTRACT OF THE INVENTION (to be given along with complete specification on separate page)
Annexed along with this form
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DATA-DRIVEN OPTIMIZATION OF CROP SELECTION AND CULTIVATION USING MACHINE LEARNING & loT
FIELD OF INVENTION:
Today's agriculture is not only about increasing the crop yield but also about preserving the environment and further enhancing farming practices. Such challenges include identifying which crops are appropriate for a given piece of land and maximizing its potential through fertilizer applications. In order to respond to these challenges, we seek to construct a Smart Agriculture Solution that is intended to help farmers making harvest monitoring, and efficient crop selection and fertilization depending on specific parameters of the land.

BACKGROUND OF INVENTION:
1.
A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast:
The system projected during this work is created by a neural network wherever inputs area unit treated on an individual basis. Static soil information in handled by fully-connected layers whereas dynamic meteorological.information is handled by continual LSTM layers. This explicit design was trained with historical information for many soil properties, precipitation, minimum and most temperature against historical yield labels at county level. When training, the model was tested in an exceedingly separate information set and showed comparable results with existing yield prognostication ways that create use of in-depth remote sensing data, the most important lesson learnt from our experiments is that it's attainable get ascendable yield forecast as a result of the projected neural network model will notice and exploit redundant info each within the soil and within the weather information. To boot, the model might be able to learn AN implicit illustration of the cycles of the crops evaluated during this paper, considering the seasonal atmospherically information used as input.

2.
Machine learning approach for forecasting crop yield based on climatic parameters:
The present study provides the potential use of information mining techniques in predicting the crop yield supported the environmental condition input parameters. The developed webpage is user friendly and therefore the accuracy of predictions square measure higher than seventy- five per cent all told the crops and districts designated within the study indicating higher accuracy of prediction. The user-friendly web content developed for predicting crop yield may be utilized by any user their alternative of crop by providing environmental condition knowledge of that place.

3.
Crop Prediction on the Region Belts of India: A Naive Bayes MapReduce Precision Agricultural Model:
The planned work introduces efficient degree economical crop recommendation system. Use of naive mathematician makes the model terribly economical in terms of computation. The system is scalable because it may be wont to take a look at on totally different crops. From the yield graphs the simplest time of sowing, plant growth and gather of plant may be known. Conjointly the best and worst condition may also be incurred. The model focuses on all style of farms, and smaller farmers may also be benefitted.

4.
Agricultural Production Output Prediction Using Supervised Machine Learning Techniques:
Two supervised classification machine learning formula has been enforced during this study, the choice Tree Learning ID3 (Iterative Dichotomiser 3) and KNNR discover the patterns within the knowledge set containing average temperature and precipitation worth obtained throughout the cropping amount of six major crops in 10 major cities of Bangladesh for the past twelve years and provides the prediction. ID3 uses the choice tree table that consists of the ranges of the precipitation, temperature and yield knowledge. The research provides an answer to the current downside that was much required for farmers in People's Republic of Bangladesh. Though the research is restricted to some mounted dataset, the long run ahead promises addition of a lot of knowledge which will be analyzed with more machine learning techniques to come up with crop predictions with higher exactness. Moreover, the analysis will result in profits and invention of advanced farming techniques which will improve our economy and can facilitate United States stand out as a technologically advanced country

OBJECTIVES:
Our main objective is to use machine learning techniques in crop selection and cultivation, and address the challenges faced by modern agriculture, such as climate change and increasing population demands. By leveraging data- driven approaches, we optimize agricultural practices by analyzing various factors like soil quality, climate conditions, and historical crop performance. The machine learning algorithms can improve crop selection for specific regions, predict yield, and enhance precision agriculture techniques, thereby contributing to sustainable farming and reducing environmental impact

SUMMARY:
The projects innovative integration of machine learning with crop selection and cultivation represents a transformative shift in agriculture, driving data-driven precision to optimize farming practices. By analyzing diverse agricultural data, it holds the potential to revolutionize resource allocation, enhance climate resilience, and bolster global food security. Despite challenges in data quality, technological complexities, and fostering widespread adoption, its societal impact is significant. It empowers farmers with informed decision-making, promoting sustainable agricultural practices and paving the way for a more productive, resilient, and sustainable future in global agriculture.

BRIEF DESCRIPTION OF DRAWINGS:
FIGURE 1: Data Flow Diagram:
External Entities: - Agricultural Data Sources: These entities provide diverse data like soil quality, climate patterns, historical yields, and market trends. - Users (Farmers/Agricultural Practitioners): They interact with the system, inputting information or receiving recommendations.
Processes: - Data Collection and Preprocessing: This process involves gathering data from various sources, cleaning, transforming, and organizing it for analysis.
- Machine Learning Model Development: In this process, machine learning algorithms are applied to the preprocessed data to develop models for crop selection and recommendations.
- Recommendation Generation: Once the models are trained, this process generates personalized crop recommendations based on input data.

Data Stores: - Agricultural Data Repository: Centralized storage containing collected and preprocessed data. - Trained Models Repository: Storage for machine learning models developed for crop selection.
Data Flows: - Input Data: Flows from external entities (agricultural data sources, users) to the data collection and preprocessing process.
- Processed Data: Flows from data collection to model development and recommendation generation processes.
- Generated Recommendations: Flow from the recommendation generation process back to the users for their reference and decision-making.

FIGURE 2: Use Case Diagram
Actors:
1.
User/Farmer: Engages with the system to receive crop recommendations based on specific inputs and requirements.
2.
System Administrators/Developers: Manage and maintain the system, ensuring its proper functioning and updates.
Use Cases:
1. Input Data Collection: - Allows the User/Farmer to input relevant agricultural data, such as soil quality, climate conditions, and historical yields.
2. Data Preprocessing: - Processes the input data, performing cleaning, transformation, and organization for further analysis.
3. Model Training and Development: - Utilizes machine learning algorithms to develop models based on the preprocessed data for crop selection and cultivation.
4. Recommendation Generation: - Generates personalized crop recommendations based on the trained models and input data. 5. Feedback/Refinement: - Permits the User/Farmer to provide feedback on the recommendations, refining the model for improved accuracy.

Relationships:
1. User-to-System Interactions: - The User/Farmer interacts with the system to input data, receive recommendations, and provide feedback.
2. Internal System Processes: - Processes such as data preprocessing, model training, and recommendation generation occur within the system.
3. Administrator-to-System Interactions: - The Administrator interacts with system to train the ML model and inspect the working regularly and uses the user/farmer feedback to improve the performance.

FIGURE 3: Sensor collection module prototype
Collect comprehensive and relevant agricultural data, including historical crop yield, weather patterns, soil composition, and market trends.
Preprocess and clean the collected data to ensure data quality and consistency for accurate analysis.

FIGURE 4: Comparison of Machine Learning algorithms
Explore and implement various machine learning algorithms, such as decision trees, random forest, support vector machines, and neural networks.Train and evaluate these algorithms to identify the most suitable model for crop selection and cultivation prediction.Utilize ensemble learning techniques to combine the predictions of multiple machine learning algorithms.Develop an ensemble model that mitigates biases and uncertainties associated with individual algorithms, improving the overall accuracy and reliability of crop selection recommendations.

FIGURE 5: User Interface
• Develop a user-friendly software application that incorporates the developed machine learning models.
• Enable farmers and agricultural practitioners to easily access and utilize the predictive models for crop selection.
• Providing user interface for inputting soil type, climate conditions, and market preferences.
• Generate personalized recommendations for crop selection based on the input parameters.
• Offer real-time monitoring and alerts to assist farmers in optimizing cultivation practices throughout the crop growth cycle.

CLAIMS
WE CLAIM:

Claim 1: A method for optimizing agricultural practices using machine learning, comprising:
• Collecting and analyzing environmental data including but not limited to soil quality temperature and humidity as well as market demand.
• Implementing machine learning algorithms to provide the recommendations regarding the types of crops to be selected and how different types of irrigation, pest control and fertilization should be performed.
• Allowing the users to pei fui m the monitoring and receive forecasting aimed towards the improvement of the harvest as well as reduction of resources used biologically.
• Developing the designed recommendations into a farmer oriented agriculture decision support system so as to enhance farming output and ensure sustainability.

Claim 2: This device includes soil moisture sensors, temperature and humidity sensors, a microcontroller unit (MCU) for data processing and wireless communication and power supply unit for continuous operation.

Claim 3: A portable, data-driven solution for crop selection and cultivation using machine learning algorithms. The system integrates loT sensors and real-time data analysis to optimize agricultural practices across various environments, enhancing productivity and sustainability.

Claim 4: Machine Learning Algorithm is implemented to predict different crops for different soil and weather conditions. Random Forest Algorithm is used as it has better accuracy than Decision Tree Algorithm.

Claim 5: This ML implementation is done using Cloud services. The data is acquired from the on-field module mentioned in Claim 2, and is processed and stored in Cloud.

Claim 6: This ML and cloud services is integrated through the means of loT (Internet of Things). Various sensors are used to collect the data and predicts an unified output.

Claim 7: The recommendation of natural Fertilizers according to the nutritional requirements of the crop and the soil needs, can significantly enhance soil health and crop yield while minimizing environmental impact.

Claim 8: The system autonomously recommends fertilizers based on soil and crop data, eliminating the need for direct farmer intervention.

NAME
Dr. J. Raja
(Principal, Sri Sairam Engineering College)
AEniyan
A
Abdul Azeez P
Bharath Kumar S
Sushanth P

Dr. T. Sathies Kumar
Dr. S. Aruhprasad

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