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Intelligent Smart Agriculture Methane Crop Yields Detection and Prediction for Machine Learning Techniques

S. P. Ramesh*, Dr. Muthusamy Periyasamy

Research Scholar, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India

Abstract: The MEVM (methane energy value model) was created for several energy crops. Machine learning has been created with big data developments and better enrolling than make new open doors for data genuine science in the multi-disciplinary agri-business progresses territory. By applying machine learning to sensor data, farm the chief's frameworks are forming into ceaseless artificial intelligence engaged tasks that give rich recommendations and encounters to farmer decision help and action. IoT contraptions give data about the nature of developing fields and a short time later make a move dependent upon the farmer input. The arrangement endeavors to mastermind diverse possible unstructured associations of crude data, assembled from different kinds of IoT devices, united and advanced self-ruling style using the upside of model changes and model-driven designing to change data in a coordinated structure.

Keywords: machine learning, big data, Internet of Things (IoT)

Received June 20, 2022; Revised July 27, 2022; Accepted August 09, 2022

Citation: S. P. Ramesh, Dr. Muthusamy Periyasamy, Intelligent Smart Agriculture Methane Crop Yields Detection and Prediction for Machine Learning Techniques. Applied Ecology and Environmental Sciences, 10 (8). 509-518. January 2022. doi:10.12691/aees-10-8-3

1. Introduction

The methane output per hectare is the most important aspect to consider while producing biogas. Furthermore, the energy crops must be cultivated in crop cycles that are both sustainable and adaptable [1].

Anaerobic digestion biomass may be cultivated as a forerunner, main crop, or follower crop. Thus the accuracy level of the crops can vary according to the type of algorithm chosen, and the right algorithm with selective features must therefore be chosen, which maximizes the accuracy of the suggestion on a favourable basis. The Division of Methane Agricultural Engineering, in collaboration with its partners, studies biogas production from a range of energy crops and methane agricultural wastes to maximize methane output and improve the economic efficiency of long-term biogas production.

Figure 1. Machine Learning Architecture

In any event, given this development especially in the immediate past, we can expect that IoT technologies will have a crucial role to play in the various applications of the methane agricultural sector [2].

This is a direct consequence of the functionality provided by IoT, including the central correspondence system (used to link smart objects from sensors, cars, mobile devices to users using the Internet) and the reach of administration, for example, local or remote data collection, cloud-based smart knowledge processing, and decision-making, user interface and agriculture auto operation [3,4].

Most of the existing papers either offer no information or display a minimal emphasis on various IoT-based models, designs, advanced techniques, the use of IoT for food safety, and other potential problems with the most recent statistical data points in mind [5]. The key contribution of this essay is to have real insight into:

• The expectations of the food industry for the world

• Continued advances in IoT, both in terms of insight and business, and how these developments help provide answers to the agriculture industry.

• Limitations that the agriculture sector is facing.

RSME and AIC equations are listed below,

[picture] | (1)

[picture] | (2)

1.1. Problem Statement

The problem has been multifaceted information from this raw data, which has contributed to the improvement of new methodology and techniques, such as machine learning, which can be used to integrate the knowledge of data with methane crop yield assessment. A variety of approaches such as artificial neural networks, Information Fuzzy Network, Decision Tree, Regression Analysis, Bayesian Credentials Network.

[picture] | (3)

Figure 2. Major hurdles in technology implementation for smart agriculture

1.2. Objective

This thesis has been set up as an attempt to reassess the exploration and reflect on the importance of machine learning processes in the field of methane agricultural crop generation. Accurate and timely crop creation predictions are crucial for important strategic decisions such as import-export, marketing transport calculation, and so on as shown in Figure 3 by the Directorate of Financial Aspects and Measurements.

[picture] | (4)

Figure 3. Field-Side Architecture

2. Literature Survey

This allows clients to make methodological improvements, such as harvesting a more robust inherited variety before planting or, in any case, modifying the yield type to require exceptional climatic varieties to continue to grow throughout the harvesting cycle [6].

This work centers around the expectation of the most productive yield that can be developed in the agrarian land utilizing AI methods. Subsequently, farmers can develop the most beneficial yield in the best appropriate months [7].

Remote Sensor Network is an advanced new to India where it will, in general, be used in the Agriculture Sector in India for extending crop yield by giving a figure of plant diseases and nuisance [8]. To be sure, the model will have the ability to learn Artificial Network known examples of trends of yields assessed during this article, taking into account seasonal atmospheric knowledge used as information [9].

Machine learning approach for forecasting crop yield based on climatic parameters: The current examination gives the likely utilization of information mining procedures in foreseeing the crop yield upheld the environmental condition input parameters [10]. The easy-to-understand web content produced for foreseeing crop yield might be used by any client their option of the crop by giving environmental condition information on that place.

Crop Prediction on the Region Belts of India: A Naïve Bayes MapReduce Precision Methane Agricultural Model The planned work presents a proficient degree economical crop recommendation framework [11,12]. The model spotlights all styles of farms, and smaller farmers may also profit This model may be more increased to search out the yield of each crop, and for a chemical recommendation [13,14,15].

3. System Model

At present, a consistent combination of remote sensors and IoT in smart agriculture will lift agriculture to standards that were already incomprehensible. By introducing smart agriculture, IoT will help to enhance the management of a variety of traditional farming problems, such as dry season reactions, increased yields, land logic, water systems, and frustration regulation [16].

3.1. Soil Sample

Soil is the "stomach" of plants, and its examination is the initial step of the evaluation to acquire field-explicit knowledge, which is then further used to make various basic decisions at different levels. Factors that are necessary to break down soil supplement levels include soil composition, crop history, application of fertilizer, water system level, topography, and so on. These factors include an understanding of the compound, physical, and natural state of the soil to consider the limiting factors with the overall goal that crops can be handled appropriately [17].

Like seed health, time to plant, and even extent of planting, when some are firmly developed and others less so.

3.2. Irrigation

Annual precipitation, including snowfall, which is the main source of water in India is estimated to be about 4 000 km3. Annual evapo-happening capacity in the nation is 1 775 mm, but shifts from at least 1 239 mm in Jammu and Kashmir to a maximum of 2 052 mm in Andhra Pradesh The total catchment area of Indian streams is projected to be 252.8 MHA. The Ministry of Water Supply, Government of India, for productive water, has divided the entire nation into 20 water bowls comprising the main bowls, plus a wide variety of diverse excess medium and small stream frames below Table 1.

Figure 4. General hierarchy of possible applications, services, and sensors for smart agriculture

Table 1. Catchments Area, Average Surface, And Groundwater Potential In Individual River Basins

3.3. Yield, Forecasting, and Harvesting Monitoring

Yield monitoring is a method used to break down different angles when compared to methane agricultural yield, equivalent to grain mass flow, moisture content, and grain quantity harvested. It is used to determine specifically by recording the yield of the crop and the amount of moisture to be weighed, how well the crop has been produced, and what to do immediately. Monitoring of yields is seen as a simple piece of exactness cultivating at harvest time as well as before, as monitoring of yield efficiency presupposes an immediate mission. Yield quality relies upon numerous elements, for example, adequate fertilization with great quality dust, particularly when foreseeing seed yields under changing natural conditions. The recorded data can be reassessed for the selection of breeds, crops, and animals which yield optimal benefit in the available environment and resources [18].

The special requirements of wireless sensor and actuator networks are Low latency, Low bandwidth, Long battery life, and High data security. It is not attractive for business communication networks because of the low data rate. The below equations are mentioned to proposed work methods.

[picture] | (5)

[picture] | (6)

[picture] | (7)

[picture] | (8)

4. Implementation Results

Overlaid-edged harvest advancement depends on varying impacts of the dirt type in which the yield is filled to measure the moisture content. Because of this dataset, we are planning a measured TensorFlow-dependent relapse model that takes the dirt, temperature, humidity, and soil status as knowledge and predicts whether the conditions are optimal for such yield [19].

Figure 5. State-wise Agri report

Figure 6. Indian Map Agri part

Figure 7. Agri Yield reports in all states

Figure 8. Cost of cultivation and production Yields

Figure 9. Cost of cultivation and production Yields

5. Results and Discussions

The training data set is then submitted to the ANN model for the prediction model for the crop suggestion. The activation function and the hidden layers are carefully chosen to obtain the best results during model generation. After generating the model test data, the model is given for error and accuracy calculation. The model predicts and suggests that crops are seeded with a precision of about 96.8 for the generated model when the inputs are supplied by the user interface.

At a point where the computer detects a peculiarity, the fundamental advance taken is to send an SMS to the farmer to make him remember the situation that has not been met.

[picture] | (9)

[picture] | (10)

[picture] | (11)

[picture] | (12)

[picture] | (13)

5.1. Software

• Python: Python 3 needs to exist mounted resting on the member of staff serving at the table. This is used to dash and instruct models of mechanism knowledge.

• TensorFlow: is an open-source mechanism of knowledge records shaped through the Google intelligence squad. The python accomplishment of this record is in the direction of the existing build to perform prognostic psychotherapy tasks.

• Cloud Server: This is used to create a Globus Cloud Server.

• PYTHON: I used to simulation processes like this implementation

Figure 10. Agri Production Monitoring System

Figure 11. Training and Validation Score Level 1

Figure 12. Training and Validation Score Level 2

Figure 13. Validation Score Level

Figure 14. Variable Importance

Figure 15. Model Performance

Figure 16. Correlation Matrix in Agri Crops

Figure 17. Correlation Matrix in Agri Crops

Figure 18. Confusion Matrix

Figure 19. Confusion Matrix

6. Conclusion

This paper proposed a structure for the administrative framework, offering a convenient and open approach to spread and investigate different sorts of geospatial data for ground highlights of development things in present-day agrarian parks. The system is upheld by geospatial android applications, which have become the significant channels of geospatial data spread in the previous decade and offer fundamental guide tasks and progressed elements of data customization, perception, and analysis. Also, the administrative framework will be created to give clients valuable elements of online perception, geospatial route, and questioning, reformatting and change, an outline of the region of interest, on-the-fly data analysis, on-request data processing, and scattering, topical guide creation, etc, in assistance arranged engineering climate.

References

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