The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R2). LSTM outperformed ARIMA and Random Forest with a higher R2 value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions.

The process of authoring a Software Requirements Specification (SRS) document is a resource-intensive task in software development that requires coordination among multiple stakeholders and is often time-consuming, costly, and prone to human error. Latest advancements in artificial intelligence have enabled the generation of specification documents using Large Language Models (LLMs). However, such approaches still depend on manual prompt engineering and prompt optimization to extract relevant knowledge and do not consistently ensure structural coherence, completeness, and reliability. This paper presents a systematic approach to SRS generation in which input requirements from stakeholders are classified into semantically meaningful topics, followed by the construction of an initial skeleton document based on these topics. The document is then incrementally expanded using reinforcement learning to improve consistency, completeness, and coverage. The proposed approach also improves the dependability of the output by reducing hallucinations that may arise from the unstructured, raw nature of user inputs. The experimental evaluation of the proposed framework increases topic classification accuracy from 0.40–0.70 to 0.75–0.95 across six requirement topics, thereby improving document structure and generation quality. Compared with a ChatGPT Model 5.2 baseline, the framework achieved significant improvements in key text-generation metrics, including a 19.8% increase in ROUGE-L and an 11.8% increase in METEOR, while maintaining contextual relevance with an average semantic cross-similarity score of 0.615. These results indicate that the proposed method can produce SRS documents that are contextually reliable and structurally coherent while requiring less manual prompts such as requirements from analysts or stakeholders.