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Predictive Modelling Accuracy Analytics for Harvest Forecast and Agricultural Sustainability
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 101 |
| Year of Publication: 2026 |
| Authors: M. Fatima, Vineet Gupta, Vinita Jain, Neha Sharma, Suti Raj, Ruchi Gupta |
10.5120/ijca29c29d078aac
|
M. Fatima, Vineet Gupta, Vinita Jain, Neha Sharma, Suti Raj, Ruchi Gupta . Predictive Modelling Accuracy Analytics for Harvest Forecast and Agricultural Sustainability. International Journal of Computer Applications. 187, 101 ( May 2026), 17-23. DOI=10.5120/ijca29c29d078aac
Abstract
Harvest Forecast and estimation is important for food security and efficient resource management. It helps policymakers make decisions about agriculture. This paper employed two machine learning techniques, Random Forest (RF) and Support Vector Machine (SVM), to assess the capacity for Harvest (Crop Yield) Forecast. The dataset consisted of 19,689 records gathered from various regions of India between 1997 and 2018 on 17 distinct crops. The dataset included both categorical and numerical data such as harvest type, season, and state as well as quantitative measures like cultivated area, production levels, rainfall, fertilizer use and pesticide use. The dataset was divided into two parts: training and testing, with an 80:20 ratio. A 5-fold cross-validation method was used during model training to make sure that the results were accurate. We used three common metrices to measure efficacy of model: coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The paper demonstrated that the Random Forest model is better than the SVM model when it comes to make accurate predictions and reducing errors. The results also show that the Random Forest model does a better job for harvest forecast than earlier studies. It got a R² value of 0.97, which is higher than the usual range of 0.88 to 0.96 for tree-based models that are similar. This demonstrated that how well it can find complex patterns in agricultural datasets.
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