Crop recommendation with machine learning: leveraging environmental and economic factors for optimal crop selection

Department of Computer Science College of Engineering, Design and Physical Science Brunel University London steven.sam@brunel.ac.uk Abstract Agriculture constitut es a primary source of food production, economic growth and employment in India, but the sector is confronted with low farm productivity and yields aggravated by increased pressure on natural resources and adverse climate change variability. Efforts involv ing green revolution, land irrigations, improved seeds and organic farming have yielded suboptimal outcomes. The adoption of innovative computational solutions such as crop recommendation systems is considered as a new frontier to provide insights and help farmers adapt and address the challenge of low productivity. However, existing agricultural recommendation systems have predominantly focused on environmental factors and narrow geographical coverage in India, resulting in limited and robust predictions o f suitable crops with both maximum yields and profits. This work incorporates both environmental and economic factors and 19 crop varieties across 15 states as input parameters to develop and evaluate two recommendation module s - Random Forest (RF) and Support Vector Machines (SVM) - using 10 - fold Cross Validation, Time - series Split and Lag Variables approaches. Results show that the 10 - fold cross validation approach produced exceptionally high accuracy (RF: 99.96%, SVM: 94.71%), raising concerns of overfitting. However, the introduction of temporal order, which aligns more with real - world scenarios, reduces the model performance (RF: 78.55%, SVM: 71.18%) in the Time - series Split approach. To further increase the model accuracy while maintaining the temporal order, the Lag Variables approach was employed, which resulted in improved performance (RF: 83.62%, SVM: 74.38%) compared to the 10 - fold cross validation approach. Consequently, the study shows the Random Forest model developed based on the Lag Variables as the most preferred algorithm for op timal crop recommendation in the Indian context. Key words: Crop recommendation model; Random forest; Support vector machines; Indian agriculture; Exploratory data analysis 1. Introduction Agriculture is not only fundamental for food production but also constitutes a primary source for economic growth, employment and improvement of the wellbeing of many people globally. For example, the World Bank reports that agriculture constitutes about 4 % of the world's total gross domestic product (GDP), and in certain least developed nations, its contribution to GDP exceeds 25%.