Mr. M. Harish (2026) Turning Field, Soil, Weather and Crop Data Into a Predictive Cultivation Planning Pipeline for Smarter Agricultural Decisions - (farm2forecast)

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: International Journal of Engineering Research and Science & Technology
• Year: 2026
• Date: 2026-10-08
• Authors: Mr. M. Harish, Sriram Ganesh, Kunduru Mahesh Reddy, S Ramcharan Teja, Lingaswamy M, Rajaboina Arun
• DOI: None

Research Groups

The paper describes the Farm2Forecast system, implying development by a multidisciplinary team with expertise in agricultural science, data science, and software engineering. Specific research groups, labs, or departments involved in its creation are not explicitly named in the provided text.

Short Summary

This paper introduces Farm2Forecast, a data pipeline and predictive analytics system designed to assist small and medium Indian farmers with cultivation planning by integrating diverse data sources and providing recommendations on crop suitability, yield, sowing, nutrient application, and economic returns.

Objective

• To develop and present Farm2Forecast, a data pipeline and predictive analytics system that integrates field records, soil tests, weather data, and historical crop data to provide actionable cultivation planning support for small and medium farms in India, thereby addressing challenges posed by unpredictable rainfall, soil variability, and market fluctuations.

Study Configuration

• Spatial Scale: Individual farm fields (small and medium farms) for specific recommendations, extending to district-level for historical crop yield and market price data.
• Temporal Scale: Daily for weather observations and forecasts, seasonal for cultivation planning, and historical for crop data and market prices. Medium-range forecasts are utilized for sowing window recommendations.

Methodology and Data

• Models used:
  • Crop suitability model (ranks candidate crops based on soil, climate, and water availability).
  • Yield prediction model (gradient boosting on soil, weather, and management features).
  • Sowing window model (combines forecast rainfall and soil moisture estimates).
  • Rule-based nutrient module (converts soil test values into fertiliser recommendations).
  • Economic layer (estimates likely return using expected yield and recent market prices).
• Data sources:
  • Field details (location, area in square metres, irrigation source, past crops) registered via a mobile application.
  • Soil Health Card results (nitrogen, phosphorus, potassium, pH, organic carbon, and micronutrient values).
  • Daily weather observations and medium-range forecasts (public meteorological services).
  • District-level crop yield and market price records (government open data portals).

Main Results

• Farm2Forecast successfully integrates diverse data sources—including farmer-input field records, soil test results, real-time weather observations and forecasts, and historical crop and market data—into a unified, partitioned analytical warehouse.
• The system generates comprehensive cultivation recommendations, providing farmers with insights into optimal crop suitability for their specific fields and seasons, predicted yields, favorable sowing windows based on rainfall and soil moisture, precise fertiliser application guidance, and estimated economic returns for various cultivation options.

Contributions

• Development of Farm2Forecast, a novel and comprehensive data pipeline and predictive analytics system specifically tailored to support cultivation planning for small and medium farms in India.
• Integration of disparate and often informal data sources into a coherent, actionable system, addressing the challenges of data fragmentation in agricultural decision-making.
• Provision of multi-faceted decision support, encompassing crop selection, yield forecasting, optimal sowing times, nutrient management, and economic viability, which is crucial for farmers facing increasing environmental and market unpredictability.
• Offers a practical solution to enhance agricultural resilience and productivity for a critical demographic of farmers in a region highly vulnerable to climate change impacts.

Funding

The provided paper text does not mention any specific funding projects, programs, or reference codes.

Citation

@article{MrMHarish2026Turning,
  author = {Mr. M. Harish,  Sriram Ganesh,  Kunduru Mahesh Reddy,  S Ramcharan Teja,  Lingaswamy M,  Rajaboina Arun},
  title = {Turning Field, Soil, Weather and Crop Data Into a Predictive Cultivation Planning Pipeline for Smarter Agricultural Decisions - (farm2forecast)},
  journal = {International Journal of Engineering Research and Science & Technology},
  year = {2026},
  url = {https://openalex.org/W7221214173}
}