Intelligent Firmament - An Overview on Crop Classification and Recommendation System Using Machine

Abstract

The agriculture industry is essential to maintaining human existence and guaranteeing the availability of food worldwide. As the global population continues to rise, there is an urgent need for novel technology that will improve agricultural efficiency and output. The \"Intelligent Firmament\" is a cutting-edge system that combines machine learning methods for crop classification with individualized recommendations to transform agriculture. Utilizing proficient machine learning algorithms, the system categorizes crops according to meteorological conditions, soil properties, and satellite photos. The Crop Classification Module is the first part of the Intelligent Firmament. This module extracts useful data about crop varieties, development stages, and general health by analyzing high-resolution satellite photos using deep learning models. To deliver dynamic recommendations, the Intelligent Firmament also makes use of climate models and real-time weather forecasts. The system can adjust and recommend appropriate crops that are in line with expected environmental conditions favorably to machine learning models that have been trained on past weather data to predict future climate trends. By taking a proactive stance, farmers may reduce the hazards brought on by erratic weather patterns and adjust their farming methods as necessary. The Intelligent Firmament, which uses machine learning to classify crops and provide customized recommendations, is a paradigm change in agriculture.

Introduction

I. INTRODUCTION

With the world population growing and environmental issues becoming more pressing, there is never before been more demand on the agriculture sector to increase production, sustainability, and efficiency. A ground-breaking approach known as "Intelligent Firmament" uses machine learning to turn agriculture into a precision-focused, data-driven industry. Crop classification and recommendation are the two main components of this system, which integrates technology innovations to enable farmers and other stakeholders to make well-informed decisions for the best possible production and resource use. Despite having a long history, traditional farming methods frequently lack the accuracy and flexibility required to handle the challenges of contemporary agriculture. The emergence of satellite technology and the progress made in machine learning create new opportunities for the technical development of agriculture. By combining satellite photos, soil data, and machine learning algorithms, the Intelligent Firmament aims to close the knowledge gap between conventional wisdom and state-of-the-art technology and produce a comprehensive picture of the agricultural landscape.

The Intelligent Firmament has two main goals: first, to create a powerful Crop Classification Module that can recognise crops from satellite images and ground-level data; and second, to put into place an advanced Recommendation Module that can offer customised, data-driven recommendations for crop choices and cultivation techniques. The system hopes to improve agriculture to a more resilient, sustainable, and efficient condition by accomplishing these goals. The Intelligent Firmament covers a wide range of geographic areas and farming techniques. Because of its adaptability, the system can be used in a variety of settings and with varying soil types and farming practices. But it's important to recognise their limitations. The quality and accessibility of the data are key components of the system's efficacy, and implementation may be impacted by regional legal frameworks and socioeconomic circumstances. Moreover, the system is a tool designed to supplement conventional farming knowledge and methods rather than a cure-all. Through the utilisation of satellite technology, data integration, and machine learning, this system aims to improve crop management techniques, provide farmers with actionable insights, and support worldwide efforts towards resilient and sustainable food supply.

The methods, findings, and consequences of this novel approach to crop classification and recommendation in agriculture are further explored in the sections that follow. The Intelligent Firmament sets out on a revolutionary quest to completely reshape the agricultural landscape.

II. OVERVIEW

Harshiv Chandra et al. proposes that the application of a Random Forest classifier-based explainable AI (XAI) model. Utilising a variety of physiochemical characteristics, the developed model predicts a given soil's relative soil fertility with accuracy. It also provides an explanation of the reasoning behind the model's prediction of soil fertility indicators utilising user appealing diagrams. When comparing the model's accuracy to the most advanced machine learning models, it displays 97.02%[1].

Gunjan Bobade et al. proposes model is a smart irrigation system driven by artificial intelligence (AI) that anticipates levels of soil moisture based on the weather, humidity, and soil moisture using machine learning (ML) principles. Due to the cloud-based nature of this technology, current information may be retrieved via Internet of Things sensors[3]. To categorise or forecast values, the data is subjected to supervised learning using the decision tree paradigm. With an accuracy of 90%, the decision tree's conclusion aids in predicting how much water crops will use, enabling effective water use and encouraging yield growth.

Sagar BM et al. proposes includes information on temperature, humidity, pH, NPK value, and historical trends in precipitation are all part of the system's technique. Rainfall value is precisely forecasted using the Gradient Boosting model, which is an essential input for the process of crop suggestion that follows[2]. XGBoost is utilised to analyse the combined effects of temperature, humidity, pH, NPK value, and expected rainfall in order to find the optimal crop for a specific set of circumstances.

Y Nancy Jane et al. brings out this project's objective to produce an intelligent dashboard which offers agricultural recommendations for every district in Tamil Nadu by utilising the Shiny framework. XGBoost is utilised in the construction of the shiny framework's prediction model, which offers excellent accuracy in decision-making scenarios and is effective in managing large datasets. The dataset includes 13 years of crop cultivation history for every district in Tamil Nadu, together with climate characteristics. In order to forecast the beneficial harvest for each district, the model is fed the most recent data on the influential factors, which are retrieved using APIs[4].

M Lakshmi Haritha et al. showcased the goal of the project is to increase crop prediction accuracy above the current models. To encourage its maximal growth, seed data that includes the appropriate parameters—such as topsoil pH, humidity, and temperature, as well as rainfall, and the amounts of nutrients, potassium, and phosphorous — are gathered. The crop prediction is carried out using the data that was acquired. The effectiveness of the Naïve Bayes, Ada Boost, Decision Tree, and Voting classifier algorithms is compared based on efficiency, confusion matrix, and additional performance measures like precision, recall, and F1 score[5].

Prasant Kumar et al. showcased system, It uses previously recorded soil property measurements as the foundation for its machine learning-based crop recommendation system. This technique lessens the chance of soil degradation while also assisting in crop health maintenance. Numerous sections, among them downpours [6]. Random forest and other machine learning techniques, Naive Bayes, KNN and the methods of logistic regression and decision tree techniques are employed for the analysis of temperature, pH, N, P, K, and humidity. Guidance is given for raising a suitable crop in light of these assessments. The analysis helps in developing the model.

Siva Ramakrishna Sani et al. proposes that the accuracy score is used to gauge the effectiveness of the recommendation system, and the model was trained on a sizable dataset. Lastly, we will use the trained model to forecast a crop that will grow on the provided area based on the specified characteristics. Farmers are capable of making well-informed decisions regarding crop scheduling and management, academics, and policymakers with the support of our suggested strategy[10].

Divyansh Saxena et al. offered a model that makes use of machine learning methods to help farmers. The system also provides information on the kinds and quantities of seeds and fertiliser required for production. This method will suggest the best crop for a given plot of land based on soil properties and climatic conditions. As a result, farmers can plant a new crop, possibly increase the profit they make, and avoid contaminating their soil by employing our technology [11].

Yalabaka Srikanth et al. showcased the development of research area which has created a method to assess the quality of the soil and suggest crops. Additionally, by utilising machine learning using algorithms to maintain the recommendations as accurate as possible, it forecasts the amount of fertiliser required for the crop and in the selling place based on soil characteristics such soil water content, nutrients, and rainfall before implementing the suggested model [12].

Nabila ElBeheiry and Robert S. Balog showcased article which offers a review of the most current science fiction literature in order to pinpoint problems, solutions, and standard procedures for integrating technology. To guarantee suitable analysis and interpretation, the survey was carried out on 588 papers that were published on the IEEE database using Cochrane methodology. After analysing the contributions provided by the papers, research themes were determined in order to ascertain the technology needed to create science fiction[14]. Data analysis, actuators and machines, big data, sensors, and communication are the themes that have been found. The most popular SF systems are those that use intelligent decision-making, autonomy, and remote monitoring.

III. DEEP LEARNING ARCHITECTURES

Deep learning can dramatically increase crop recommendation and classification systems by identifying complex patterns and abstractions from large-scale agricultural data. Ensemble models enhance overall performance, robustness, and generalisation by combining several separate models. The enhance of AI model can help in showcasing the accuracy of the significant work. Here is an ensemble method that makes use of deep learning architecture for a crop recommendation and categorization system:

A. Ensemble Of Convolutional Neural Networks

Use different architecture or random initialization to train numerous CNNs.

Utilising methods such as weighted averaging or majority voting, combine their predictions.

When it comes to crop classification based on images, this ensemble can work well.

B. Ensemble Of Recurrent Neural Networks

Training many RNNs, maybe varying the hyperparameters or initializations.

Combine their forecasts to identify various time trends in the crop growth time series data.

useful in agriculture for managing sequential data, such as crop growth over time.

C. CNN-RNN Hybrid Ensemble

Integration of the predictions made by CNNs and RNNs.  Do use of CNNs' advantages for spatial data (like satellite photos) and RNNs' advantages for temporal data (like sequential data).

Effective for applications where both geographical and temporal information are required.

D. Ensemble Of Transformer Model

Teach several transformer models with various initializations or structures.

In multi-modal agricultural data, combine their outcomes to capture various contextual interactions.

ideal for combining data from several sources, such as soil, weather, and past crop yield. Therefore, the approach is built to transform the model.

E. Stacking Ensemble

Train various deep learning models (e.g., CNN, RNN, Transformer) separately.

On top of that, combine their forecasts using a meta-model. The meta-model gains the ability to evaluate each individual model's predictions according to how well it performs. The model's ultimate robustness and accuracy can be increased with this technique.

F. Bagging (Bootstrap Aggregating)

Use bootstrap samples of the training data to train multiple instances of the same deep learning architecture.

Combine their forecasts, frequently by average or by majority vote.

This method can lessen overfitting and increase stability.

G. Boosting

Train models one after the other, concentrating on fixing the mistakes created by the earlier models.

Add weighted voting to their predictions.

Boosting highlights difficult passages, which enhances the group's performance.

H. Random Forest Of Deep Models

Independently train a variety of different deep learning models (e.g., CNNs, RNNs).

Utilising an aggregation method akin to a random forest, combine their forecasts.

This technique may successfully manage various data characteristics and increase robustness.

IV. FORMULATION

The algorithms and models that you are utilizing in your deep learning architecture will determine the formulas for crop recommendation and categorization. I can, nevertheless, give you a broad rundown of the main ideas that these procedures entail. The important depiction of how intellectually the activation and the loss function can build up the model for the efficiency of the model. Therefore here is the model formulation to showcase.

A. Softmax Activation

The Softmax activation function is frequently utilized in a neural network's output layer for classification tasks. For every class, it turns the raw output scores into a probability. Here is the way how each probability represents the class and the total number of class representations on each frequency applied.

It is noteworthy that the particulars will vary depending on your crop recommendation and classification system's architecture, algorithm, and objectives. For accurate model evaluation, make sure your data has been well-preprocessed and divided into training, validation, and testing sets. Based on your data's properties and performance indicators, modify your algorithms and hyper-parameters.

V. PIPELINE

Preparing the data and then deploying the model are the first steps in developing a crop recommendation system. This is an overview of the steps involved in creating a crop recommendation system:

1. Image Acquisition: Preparing the data and then deploying the model are the first steps in developing a crop recommendation system. This is an overview of the steps involved in creating a crop recommendation system:
2. Pre-processing: Handle missing values, outliers, and discrepancies in the data. Normative features should be made standard. Encode categorical variables if necessary. To understand trends and linkages, examine and evaluate the data. The data cleaning and cleansing will enhance the approach to build the model without any overflow.
3. Feature Extraction: Take out pertinent data elements that can help with crop suggestions. Create new features based on domain expertise. If necessary, reduce dimensionality.
4. Model Selection: Select the best deep learning or machine learning models for crop recommendation. Examples of this include support vector machines, decision trees, random forests, and deep neural networks. To improve performance, think about assembling several models.
5. Training: Divide the data collection into sets for testing, validation, and training. Utilizing the training set, validate the chosen models. To boost the performance of the model, adjust the hyper-parameters.
6. Evaluation: To make sure the models have good generalization, assess them using the validation set.
7. Models should be adjusted considering validation results.
8. Using the proper statistics (F1-score, recall, accuracy, and precision), evaluate the models over testing set.
9. Ensemble of Hybrid Models: If appropriate, combine predictions from several models to form an ensemble or hybrid model. Modify voting procedures or weights to maximize the performance of the ensemble.
10. Crop Recommendation: Based on input data, use the learned models to forecast the appropriate crops.
11. Put in place a system for managing suggestions' degrees of confidence and uncertainty. Think about adding restrictions or user choices to the recommendation system.
12. Validation: Gather feedback from users or domain experts to validate the system's recommendations. Update and enhance the recommendation models frequently in response to user input and fresh data.
13. Deployments: Install the crop recommendation system in an area used for production. Connect the system to any applications or user interfaces. Track the system's functionality in actual situations.
14. Documentation: Record every step of the pipeline, including the model topologies, data sources, preprocessing stages, and deployment techniques.
15. Give the crop recommendation system's users instructions.
16. Maintenance and Updates: To keep the models current and correct, regularly add new data to them. Track system performance and take quick action to resolve any problems.

The specifics of this pipeline may change depending on the attributes of the data, the models selected, and the objectives of the crop recommendation system. This pipeline offers a generic framework.

VI. SYSTEM DESIGN

Here is a sample for demonstrating the modelling

VII. RECOMMENDATION

Crop Recommendation System has the potential to completely transform farming operations by providing farmers with individualized advice on the best crops to plant. This method, which makes use of cutting-edge technology like machine learning, has the capability to greatly increase agricultural frequency of sustainability and productivity. It is advisable to consistently priorities the improvement and broadening of data sources, such as historical yields, soil health, and climate patterns. The system's capacity to adjust its algorithm on a frequent basis in response to changing climatic circumstances enhances the accuracy of crop estimates. Furthermore, adding user feedback into the suggestion process creates a collaborative approach, aligning the system more closely with the practical demands and insights of farmers.

To improve the capabilities of the system, strategic cooperation with researchers, technological professionals, and agricultural experts is necessary. The system will continue to be at the forefront of innovation thanks to this interdisciplinary partnership, which addresses new issues in the agriculture sector. In order to successfully usher in a new era of precision agriculture, a Crop Recommendation System must be implemented holistically, combining cutting-edge technology, accurate data, user involvement, and continuous collaboration.

Conclusion

The creation of a crop recommendation system is a critical step towards effective and sustainable farming methods. Through the integration of several variables that include soil types, climate, and past yields, this system leverages machine learning and data-driven insights to provide farmers with tailored suggestions. The use of intricate models, spanning from conventional decision trees to sophisticated ensemble techniques, guarantees the system\'s flexibility in response to the complexities of agricultural environments. By means of stringent procedures for training, validating, and fine-tuning, the system gains proficiency in forecasting the best crop selections according to dynamic environmental circumstances. The combination of ongoing model updates and user feedback creates a strong feedback loop that gradually improves the system\'s dependability. By considering user preferences and providing useful insights, the crop recommendation system enables farmers to arrive at sensible choices that avoid risks and increase yields. This system is a technological cornerstone that supports sustainable farming practices and contributes to global food security as agriculture faces ever-changing challenges. The effective implementation and upkeep of these systems portend a bright future in which technology and agriculture combine to foster efficiency and creativity in the production of our most vital commodities.

References

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Copyright

Copyright © 2024 Joyce Mary B, Dr M Anand Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.