Agrovision: Enhancing Agricultural Precision through Machine Learning

Agrovision: Enhancing Agricultural Precision through Machine Learning

Abhishek K V

Information and Science and Engineering

Malnad College of Engineering Hassan-573202, India abhishekkv812003@gmail.com

Likhith H V

Information and Science and Engineering

Malnad College of Engineering Hassan-573202, India likhithhv8@gmail.com

Akhil Teotia

Information and Science and Engineering

Malnad College of Engineering Hassan-573202, India akhilteotia19@gmail.com

Sindhu K S

Information and Science and Engineering

Malnad College of Engineering Hassan-573202, India sk@mcehassan.ac.in

Chandan K

Information and Science and Engineering

Malnad College of Engineering Hassan-573202, India chandansatish1906@gmail.com

Abstract— 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.