Abstract: The agriculture sector, a linchpin of global economies, finds itself at a crucial juncture where entrenched traditions intersect with the transformative waves of technology. This research endeavorsto address pressing challenges within agriculture, chief among them being low yields and the distressing plight of farmers. The proposed solution takes the form of a neural network-based agricultural yield forecast system, wielding the power of modern technology to offer innovative pathways forward.Harnessing the potential of a user-friendly smartphone application, the system establishes direct communication channels with farmers. Leveraging GPS technology, it not only identifies their precise locations but also captures crucial information vital for precision agriculture. Machine learning techniques, prominently featuring the Random Forest algorithm, form the backbone of this system. Impressively, it achieves an accuracy rate of 95% in predicting agricultural yields, marking a significant leap forward in predictive analytics for farming outcomes.This novel approach seeks to narrow the chasm between conventional farming methods and the rapid strides of contemporary technology.
Keywords: Crop Recommendation, Machine Learning, Random Forest, Decision Tree, Logistic Regression, XGBoost, Data Analysis, Data Visualization
|
DOI:
10.17148/IJARCCE.2024.13456
