Exploring Groundnut Area, Production and Yield Trends using Deep Learning

Pullaganti Gowri; Nilavathy Kutty; Alli Pandiyathuray

doi:https://doi.org/10.14445/22315381/IJETT-V74I3P109

Research Article | Open Access | Download PDF

Volume 74 | Issue 3 | Year 2026 | Article Id. IJETT-V74I3P109 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I3P109

Exploring Groundnut Area, Production and Yield Trends using Deep Learning

Pullaganti Gowri, Nilavathy Kutty, Alli Pandiyathuray

Received	Revised	Accepted	Published
25 Oct 2025	24 Jan 2026	29 Jan 2026	28 Mar 2026

Citation :

Pullaganti Gowri, Nilavathy Kutty, Alli Pandiyathuray, "Exploring Groundnut Area, Production and Yield Trends using Deep Learning," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 3, pp. 115-128, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I3P109

Abstract

Growing groundnuts is an important part of Andhra Pradesh (AP) agricultural economy. It brings a lot of revenue for the regions and transforms the lives of those living in rural areas. To make smart decisions about how to run a farm and use resources wisely, it is important to look at trends in the area, production, and the yield of groundnuts over time. Conventional statistical techniques frequently fail to represent intricate temporal patterns accurately, underscoring the necessity for more resilient, data-driven methodologies to improve decision-making and promote the enduring viability of groundnut agriculture. This paper suggests a new way to use a deep learning method called an Autoencoder (AE) - Long Short-Term Memory (LSTM), named AELSTM, to look at the patterns in the state of AP expanding groundnut area, production, and yield. The main goal of the suggested strategy was to get clear information that would help people make decisions and create policies based on data that would help to protect and grow groundnut farming in the country. Indiastat, a statistical database, collected secondary data on groundnut area, production, and yield in AP from 1990 to 2023. AELSTM was used to do trend analysis. Initially, AE was utilized for dimensionality reduction, resulting in a compressed lower-dimensional latent representation. These small representations were transferred to an LSTM network to model temporal relationships, and trend analysis was done to find long-term patterns in the data. We used R² values to check the accuracy and quality of the trend representation by comparing the proposed method to traditional statistical trend models and architectural base models. The proposed model R² values 0.95, 0.96, and 0.94 for area, production, and yield, respectively, show that it works better than traditional statistical models and architectural base models like the dense autoencoder, vanilla-LSTM, and Sequence-to-Sequence (Seq2Seq).

Keywords

Area Production and Yield, Autoencoder, Groundnut, Trend, Kalman Filter, Long Short-Term Memory.

References

[1] A.K. Bharti et al., “Pattern of Growth and Technological Impact on Oilseeds Production in Uttar Pradesh,” Journal of Crop and Weed, vol. 8, no. 2, pp. 1-6, 2012.
[Google Scholar] [Publisher Link]

[2] Agossou Gadedjisso-Tossou, Komlavi II Adjegan, and Armand Ketcha Malan Kablan, “Rainfall and Temperature Trend Analysis by Mann-Kendall Test and Significance for Rainfed Cereal Yields in Northern Togo,” Sci, vol. 3, no. 1, pp. 1-20, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[3] D.C. Kalita, “Trends of Area, Production and Productivity of Fiber Crops in Assam,” Agricultural Situation in India, vol. 68, no. 7, pp. 329-333, 2011.
[Google Scholar] [Publisher Link]

[4] Sharmin Akhter, Jaba Rani Sarker, and Keya Rani Das, “Growth and Trend in Area, Production and Yield of Major Crops of Bangladesh,” International Journal of Economics, Finance and Management Sciences, vol. 4, no. 1, pp. 20-25, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Sarah Yasmin, and Md. Moniruzzaman, “Forecasting of Area, Production, and Yield of Jute in Bangladesh using Box-Jenkins ARIMA Model,” Journal of Agriculture and Food Research, vol. 16, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[6] K. Viswanatha Reddy, and Kingsly T Immanuelraj, “Area, Production, Yield Trends and Pattern of Oilseeds Growth in India,” Economic Affairs, vol. 62, no. 2, pp. 327-334, 2017.
[CrossRef] [Google Scholar] [Publisher Link]

[7] MD. Siraj-UD-Doulah et al., “Forecasting the Production of Jute based on Time Series Models in Bangladesh,” International Journal of Statistics and Applied Mathematics, vol.5, no.1, pp. 32-38, 2020.
[Google Scholar] [Publisher Link]

[8] Niranjan Sivalingam, G. Balaganesh, and Jamaludheen Avunchikkad, “An Analysis of Trend in Production, Consumption and Trade of Cotton in India,” International Research Journal of Agricultural Economics and Statistics, vol. 8, no. 2, pp. 293-298, 2017.
[CrossRef] [Google Scholar]

[9] Rajesh Kumar Dwivedi, Rajesh Khavse, and Manoj Kumar Ahriwar, “Trend Analysis of Area, Production and Productivity of Small Millets in Madhya Pradesh, India,” Plant Archives, vol. 24, no. 1, pp. 543-548, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[10] J.K. Saha et al., “Analysis of Growth Trends in Area, Production and Yield of Tea in Bangladesh,” Journal of Agriculture and Food Research, vol. 4, pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Md. Abdullah Al Mamun et al., “Growth and Trend Analysis of Area, Production and Yield of Rice: A Scenario of Rice Security in Bangladesh,” PloS one, vol. 16, no. 12, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Pramod K. Aggarwal et al., “Quantification of Yield Gaps in Rain-Fed Rice, Wheat, Cotton and Mustard in India,” International Crops Research Institute for the Semi-Arid Tropics, Technical Report, no. 43, Patancheru, Andhra Pradesh, India, 2008.
[Google Scholar] [Publisher Link]

[13] Comlanvi Sitou Akibode, and Mywish K. Maredia, “Global and Regional Trends in Production, Trade and Consumption of Food Legume Crops,” AgEcon Search, 2012.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Belay Tseganeh Kassie et al., “Climate Variability and Change in the Central Rift Valley of Ethiopia: Challenges for Rainfed Crop Production,” The Journal of Agricultural Science, vol. 152, no. 1, pp. 58-74, 2014.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Chandra Mohan Misra, “Trends in Area Production and Productivity of Groundnut in India: Issues & Challenges,” Journal of Research in Agriculture and Animal science, vol. 4, no. 7, pp. 01-06, 2017.
[Google Scholar] [Publisher Link]

[16] Md. Moniruzzaman Hasan et al., “Analysis of Stability, Growth and Future Projections of Jute in Bangladesh,” Agrobiological Records, vol. 20, pp. 27-37, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[17] J. Gayathri, “A Trend Analysis of Area, Production, and Yield of Groundnut in India,” International Journal of Economics, vol. 6, no. 3, pp. 15-21, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[18] M. Sathish Kumar, Yogesh Lad, and Ashish B. Mahera, “Trend Analysis of Area, Production and Productivity of Minor Millets in India,” Biological Forum-An International Journal, vol. 14, no. 2, pp. 14-18, 2022.
[Google Scholar] [Publisher Link]

[19] S.B. Bhusanar, Satyveer Singh Meena, and Aditi Mathur, “Trend in Area, Production, and Productivity of Groundnut in Rajasthan,” Asian Journal of Agricultural Extension, Economics & Sociology, vol. 40, no. 7, pp. 103-108, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Daniel Kpienbaareh et al., “Estimating Groundnut Yield in Smallholder Agriculture Systems using Planetscope Data,” Land, vol. 11, no. 10, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Govind Prasad, KK Srivastava, and Vijay Kumar Patel, “Knowledge and Attitude of Farmers towards Groundnut Production Technology in Raigarh District of Chhattisgarh,” Journal of Pharmacognosy and Phytochemistry, vol. 8, no. 6, pp. 1290-1293, 2019.
[Google Scholar] [Publisher Link]

[22] I. Krishna Teja et al., “Growth Performance of Groundnut in Ananthapuramu District of Andhra Pradesh a Temporal Analysis,” Asian Journal of Agricultural Extension, Economics & Sociology, vol. 39, no. 7, pp. 81-87, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Radhika J. Bani et al., “Identification of Mutation Point and Trend in Export of Indian Groundnut,” Journal of Agriculture and Ecology Research International, vol. 26, no. 5, pp. 24-31, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[24] S. Senthilnathan et al., “Growth Performance of Groundnut in India-An Instability and Decomposition Analysis,” Asian Journal of Agricultural Extension, Economics & Sociology, vol. 41, no. 12, pp. 350-356, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[25] NN. Pusadekar et al., “Trend and Decomposition Analysis of Groundnut in Gujarat,” Journal of Pharmacognosy and Phytochemistry, vol. 9, no. S5, pp. 145-147, 2020.
[Google Scholar] [Publisher Link]

[26] Bhoomi Suthar et al., “Growth and Instability Analysis of Area, Production and Yield of Groundnut in Selected States of India,” Environment Conservation Journal, vol. 25, no. 1, pp. 192-198, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[27] A. Sandhya Neelima, and K. Nirmal Ravi Kumar, “Impact of Climate Change on Groundnut Yield and its Variability in Andhra Pradesh, India,” Asian Journal of Agricultural Extension, Economics & Sociology, vol. 41, no. 2, pp. 19-25, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[28] Paul K. Solmon Raju, “Change and Instability in Area and Production of Groundnut Crop in Andhra Pradesh,” Publication Division Directorate of Economics and Statistics Department of Agriculture and Co-Operation Ministry of Agriculture Government of India, 2013.
[Google Scholar]

[29] Gnyana Ranjan Bal, Aruna Sharma, and Shibani Sharma, “Impact of Climatic Factors on Crop Production Index in India,” Asian Journal of Management and Commerce, vol. 6, no. 2, pp. 667-673, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[30] Majed Alotaibi, “Climate Change, its Impact on Crop Production, Challenges, and Possible Solutions,” Botanical Notes of the Agrobotanical Garden of Cluj-Napoca, vol. 51, no. 1, pp. 1-30, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[31] S. Rani et al., “Influence of Climatic Factors on Productivity of Major Cultivated Crops of Perambalur District, Tamil Nadu,” International Journal of Research in Agronomy, vol. 8, no. S3, pp. 253-256, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[32] Erick O. Onyuka, Joash Kibbet, and Christorpher O. Gor, “Socio-Economic Determinants of Groundnut Production in Ndhiwa Sub-County Kenya,” International Journal of Agricultural and Food Research, vol. 6, no. 1, pp. 1-14, 2017.
[Google Scholar]

[33] Ibrahim Usman et al., “Socio-economic Factors Affecting Groundnut Production in Sabongari Local Government of Kaduna State, Nigeria,” International Journal of Food and Agricultural Economics (IJFAEC), vol. 1, no. 1, pp. 41-48, 2013.
[CrossRef] [Google Scholar] [Publisher Link]

[34] Assane Beye et al., “Socio-Economic Constraints of Adopting New Cowpea Varieties in Three Agro-Ecological Zones in the Senegalese Peanut Basin,” Sustainability, vol. 14, no. 21, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[35] Indiastat Website, 2024. [Online]. Available: https://www.indiastat.com/

[36] Szymon Greś et al., “Subspace-based Noise Covariance Estimation for Kalman Filter in Virtual Sensing Applications,” Mechanical Systems and Signal Processing, vol. 222, pp. 1-15, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[37] Wen Xu et al., “Improving Performance of Autoencoder-based Network Anomaly Detection on NSL-KDD Dataset,” IEEE Access, vol. 9, pp. 140136-140146, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[38] Abhilash Gogineni, M.K. Diptikanta Rout, and Kumar Shubham, “Machine Learning Algorithms for Predicting Compressive Strength of Concrete with Mineral Admixture using Long Short-Term Memory (LSTM) Technique,” Asian Journal of Civil Engineering, vol. 25, no. 2, pp. 1921-1933, 2024.
[CrossRef] [Google Scholar] [Publisher Link]