Forecasting Agricultural Crop Profitability and Yield in India

Abstract

India\'s heavy reliance on agriculture underscores the importance of accurately estimating agricultural production, considering the interplay of organic, economic, and seasonal variables, particularly amid a growing population. Predicting crop yields is crucial for farmers\' planning, covering storage and marketing strategies. However, this task is complex and requires foresight. Data mining techniques emerge as a potent tool, leveraging extensive datasets to extract invaluable insights. By employing methods like Random Forest, this research offers a swift yet comprehensive examination of crop yield forecasts for specific regions. Predictive analyses like these act as a crucial asset, enabling stakeholders to make well-informed decisions grounded in expected patterns and trends.”

Introduction

I. INTRODUCTION

The agricultural sector stands as a cornerstone in India's economic development and sustenance of food security, supporting the livelihoods of over 50% of its population. However, this crucial sector grapples with multifaceted challenges, including erratic crop yields, market price fluctuations, and restricted access to essential resources and technological advancements. Addressing these challenges demands the implementation of effective tools and strategies aimed at bolstering the efficiency and profitability of India's agriculture. One such initiative, the 'Forecasting Agricultural Crop Profitability and Yield in India,' aims to fill this gap by harnessing machine learning methodologies to predict crop yield and profitability. By meticulously gathering and analyzing data on various factors influencing crop productivity and profitability—ranging from weather conditions and soil quality to market dynamics—and employing machine learning algorithms for predictive insights, this project aims to empower farmers to make informed decisions regarding crop selection, optimal planting schedules, and resource distribution.

Moreover, the predictive model formulated through this endeavor holds promise not only for individual farmers but also for agricultural entities and governmental bodies. Its potential utility spans efficient resource planning, thereby bolstering food security and sustainability endeavors across the nation.

Key facets of this project involve a comprehensive collation and analysis of diverse datasets encompassing agricultural statistics, weather patterns, and market trends. The predictive model, rooted in machine learning, not only adeptly learns from new data but also extrapolates accurate predictions based on historical patterns. An intuitive user interface is envisaged, enabling farmers to seamlessly input data and receive informed predictions about crop yield and profitability.

Ultimately, this project aspires to furnish a valuable resource that empowers farmers and agricultural organizations, optimizing their operational resources and enhancing efficiency and profitability. It also endeavors to contribute tangibly to India's agricultural landscape by fortifying food security, promoting sustainability, and advocating for environmentally conscious practices.

II. RELATED WORK

III. PROPOSED METHODOLOGY

A. Data Collection

The initial phase involves gathering data on diverse factors influencing crop yield and profitability, encompassing weather patterns, soil attributes, and market rates. This data is sourced from a variety of outlets, including governmental bodies, research institutions, and online repositories.

C. Model Development

After the data is pre-processed, machine learning algorithms such as linear regression and decision trees are used to analyze the data and make predictions. The algorithms are trained on a subset of the data, and their performance is tested on a separate validation set to ensure that the models are accurate and reliable.

In our system, we conduct tests on multiple algorithms, comparing and selecting the best one based on the analysis of the classification report. It must calculate the accuracy for both the training and testing datasets, determine specificity, False Positive rate, precision, and recall, and subsequently compare these metrics across various algorithms using Python code.

A. The Involvement steps in the Process Include

1. Identify and define the problem.
2. Prepare the necessary data.
3. Evaluate different algorithms.
4. Enhance and optimize the results.
5. Make predictions based on the refined model.

Classification algorithms will be utilized on dataset 1, whereas regression will be employed for predicting production in dataset 2.

B. Applied Algorithms Include

1. Classification

• Decision Tree
• Logistic Regression
• K Nearest Neighbor
• Random Forest Classifier

2. Clustering

3. Regression

• Decision Tree
• Random forest

C. Model Evaluation

The developed models are evaluated using various performance metrics such as mean absolute error, root mean squared error, and accuracy. The models are also compared with other existing models to determine their effectiveness.

First, let's briefly comprehend certain performance evaluation metrics:

1. General Definitions

a. True Positive (TP) represents the count of instances where the system accurately identifies a condition in the presence of its actual occurrence.

b. True Negative (TN) signifies the count of instances where the system correctly does not identify a condition that is indeed absent.

When assessing precision, which quantifies the ratio of true positive observations to the total predicted positive observations, a lower false positive rate indicates higher precision. In the present study, a noteworthy precision score of 0.788 has been achieved.

Regarding Recall, it measures the proportion of correctly predicted positive observations relative to the total observations in the actual "yes" class. For instance, in a model predicting defaulters, Recall (Sensitivity) is calculated as TP / (TP + FN), offering insight into how effectively the model captures actual positive cases among all instances in the "yes" class.

2. F1 Score

The F1 score involves computing the weighted average of Precision and Recall through a calculated process, factoring in false positives and false negatives. Understanding accuracy intuitively can be challenging, but F1 proves more valuable, especially with uneven class distribution. Accuracy is preferred when the costs of false positives and false negatives are similar. To delve into precision and recall, it's essential that the costs of false positives and false negatives vary significantly.
Formula for General F-Measure:

F-Measure=2TP/(FP+FN+2TP)
Formula for F1-Score:

The F1-Score is calculated as

2×(Recall×Precision)/(Recall+Precision)

?3. Precision

How reliable is the model's positive outcome prediction? Precision, calculated as TP/(TP+FP), becomes crucial, especially in scenarios where the cost of false positives is substantial. Consider a context like skin cancer detection. A low-precision model could lead to numerous patients being incorrectly diagnosed with melanoma, causing additional tests and psychological stress. High false positives might lead to a desensitization to alarms, as they become frequent and often inaccurate.

D. Clustering

Following the application of clustering, we generated an elbow graph to ascertain the optimal number of clusters. A common method for determining this optimal number involves calculating the Within-Cluster-Sum-of-Squares (WCSS), which quantifies the sum of squared distances between each data point within all clusters and their respective centroids. The objective is to minimize this sum.

IV. RESULT & DISCUSSION

Among classification algorithms, Logistic Regression emerged as the most effective in forecasting the profitability of a specific crop, considering factors such as the state, cultivation costs (C1, C2), production costs (Cp), and government support prices for the 2020-21 period. However, the performance of the second dataset was not as satisfactory. To enhance model accuracy, supplementary variables such as rainfall and temperature should be incorporated.

V. FUTURE SCOPE

There are numerous opportunities for advancing research and development in predicting the profitability and yield of agricultural crops in India using Python. These include enhancing the accuracy and reliability of predictive models by collecting and analyzing extensive data and exploring more sophisticated machine learning algorithms. Additionally, there is potential to expand the model's applicability by including additional crops and regions, providing a more comprehensive understanding of crop yield and profitability. Moreover, investigating advanced machine learning techniques such as deep learning can further improve predictive models. Efforts can also focus on enhancing the user experience and accessibility of the model, potentially through developing user-friendly interfaces or integrating with existing agricultural tools and platforms. Overall, these initiatives show great promise for advancing the agricultural sector and contributing to India's overall development and prosperity.

Conclusion

In conclusion, the Profitability and Yield Prediction project utilizing Python and machine learning present a promising avenue for enhancing agricultural efficiency in India. The project focuses on predicting crop yields, considering factors like weather conditions, soil quality, and market prices. By employing a range of machine learning algorithms like Logistic Regression and Decision Trees, the project endeavors to equip farmers with the necessary insights for making informed choices regarding crop selection and allocation of resources. Continuous monitoring and collaboration with stakeholders ensure the accuracy and effectiveness of the predictive models. The potential impact extends beyond individual farmers to benefit agricultural entities and governmental bodies, fostering food security and sustainability. Overall, the research demonstrates the potential of technology-driven solutions in optimizing agricultural practices for the development and prosperity of the country.

References

[1] Kavita, Ms, and Pratistha Mathur. (2020) “Crop Yield Estimation in India Using Machine Learning.” In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 220–224. doi:10.1109/ICCCA49541.2020.9250915. [2] Fan, Wu, Chen Chong, Guo Xiaoling, Yu Hua, and Wang Juyun. (2015) “Prediction of Crop Yield Using Big Data.” In 2015 8th International Symposium on Computational Intelligence and Design (ISCID), 1:255–260. doi:10.1109/ISCID.2015.191. [3] Kamath, Pallavi, Pallavi Patil, Shrilatha S, Sushma, and Sowmya S. (2021) “Crop Yield Forecasting Using Data Mining.” Global Transitions Proceedings, International Conference on Computing System and its Applications (ICCSA- 2021), 2 (2): 402–407. doi:10.1016/j.gltp.2021.08.008. [4] Wigh, Daniel S., Jonathan M. Goodman, and Alexei A. Lapkin. “A Review of Molecular Representation in the Age of Machine Learning.” WIREs Computational Molecular Science n/a (n/a): e1603. doi:10.1002/wcms.1603. [5] Kavita, and Pratistha Mathur. (2021) “Satellite-Based Crop Yield Prediction Using Machine Learning Algorithm.” In 2021 Asian Conference on Innovation in Technology (ASIANCON), 1–5. doi:10.1109/ASIANCON51346.2021.9544562. [6] Bali, Nishu, and Anshu Singla. (2022) “Emerging Trends in Machine Learning to Predict Crop Yield and Study Its Influential Factors: A Survey.” Archives of Computational Methods in Engineering 29 (1): 95–112. doi:10.1007/s11831-021-09569-8. [7] van Klompenburg, Thomas, Ayalew Kassahun, and Cagatay Catal. (2020) “Crop Yield Prediction Using Machine Learning: A Systematic Literature Review.” Computers and Electronics in Agriculture 177 (October): 105709. doi:10.1016/j.compag.2020.105709. [8] Shen, Dinggang, Guorong Wu, and Heung-Il Suk. (2017) “Deep Learning in Medical Image Analysis.” Annual Review of Biomedical Engineering 19 (1): 221–248. doi:10.1146/annurev-bioeng-071516-044442. [9] Minaee, Shervin, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. (2021) “Deep Learning--Based Text Classification: A Comprehensive Review.” ACM Computing Surveys 54 (3): 1–40. doi:10.1145/3439726. [10] Belgiu, Mariana, and Lucian Dr?gu?. (2016) “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114 (April): 24–31. doi:10.1016/j.isprsjprs.2016.01.011. [11] Suthaharan, Shan. (2016) “Support Vector Machine.” In Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, edited by Shan Suthaharan, 207–235. Integrated Series in Information Systems. Boston, MA: Springer US. doi:10.1007/978-1-4899-7641-3_9. [12] Hochreiter, Sepp, A. Steven Younger, and Peter R. Conwell. (2001) “Learning to Learn Using Gradient Descent.” In Artificial Neural Networks — ICANN 2001, edited by Georg Dorffner, Horst Bischof, and Kurt Hornik, 87–94. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer. doi:10.1007/3-540-44668- 0_13. [13] Sherstinsky, Alex. (2020) “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network.” Physica D: Nonlinear Phenomena 404 (March): 132306. doi:10.1016/j.physd.2019.132306. [15]Yu, Yong, Xiaosheng Si, Changh.

Copyright

Copyright © 2024 Astha Tripathi, Mahesh G., Avneet Singh Charabra, Divyam Bahl. 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.