  Precision agriculture system is an arising idea that refers to overseeing
farms utilizing current information and communication technologies to improve
the quantity and quality of yields while advancing the human work required. The
automation requires the assortment of information given by the sensors such as
soil, water, light, humidity, temperature for additional information to furnish
the operator with exact data to acquire excellent yield to farmers. In this
work, a study is proposed that incorporates all common state-of-the-art
approaches for precision agriculture use. Technologies like the Internet of
Things (IoT) for data collection, machine Learning for crop damage prediction,
and deep learning for crop disease detection is used. The data collection using
IoT is responsible for the measure of moisture levels for smart irrigation, n,
p, k estimations of fertilizers for best yield development. For crop damage
prediction, various algorithms like Random Forest (RF), Light gradient boosting
machine (LGBM), XGBoost (XGB), Decision Tree (DT) and K Nearest Neighbor (KNN)
are used. Subsequently, Pre-Trained Convolutional Neural Network (CNN) models
such as VGG16, Resnet50, and DenseNet121 are also trained to check if the crop
was tainted with some illness or not.
