Agrovision: Enhancing Agricultural Precision through Machine Learning

Machine learning (ML) and Deep learning (DL) have transformed agriculture by addressing challenges such as soil fertility assessment, crop recommendation, yield prediction, and plant disease detection. These technologies enable farmers to make informed decisions, enhancing productivity and sustainability. Notable advancements include Random Forest achieving 84% accuracy in soil fertility prediction and CNN- based soil classification reaching 95.21% accuracy, both integrated into accessible web platforms. Yield prediction models, such as Random Forest and LSTM, have delivered up to 98.96% accuracy, while innovations in disease detection in plants using CenterNet with DenseNet-77 achieved a mean Average Precision (mAP) of 99%. These tools empower farmers by streamlining processes, optimizing resource utilization, and reducing losses due to uncertainties. Collectively, the integration of ML and DL is shaping a more resilient and efficient agricultural sector. Keywords— Machine learning, deep learning, soil fertility, crop recommendation, yield prediction, plant disease detection, precision agriculture.