Machine Learning and Deep Learning for Soil and Plant Nutrient Management in Agriculture

The integration of Machine Learning (ML) and Deep Learning (DL) technologies into agriculture, particularly for plant nutrition management, has the potential to enhance productivity and sustainability. This study aims to review and evaluate the use of ML and DL algorithms in predicting soil and plant nutrient levels, diagnosing nutrient deficiencies, and optimizing fertilization strategies. Various algorithms such as Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GANs) have shown success in processing multispectral imagery, UAV data, and soil sensor readings to improve the accuracy of plant nutrient assessments. However, challenges such as limited data availability, high computational demands, and the complexity of models hinder their widespread application. To address these challenges, techniques like transfer learning, domain adaptation, and Explainable AI (XAI) have been identified as promising solutions to enhance model accuracy and adaptability in diverse environmental conditions. This research highlights the potential for integrating ML/DL with the Internet of Things (IoT) and big data technologies to develop lightweight, energy-efficient models for more efficient and sustainable plant nutrition management. The findings suggest that these technological innovations are crucial for advancing precision agriculture, fostering higher productivity, and minimizing resource usage while promoting environmental sustainability.