A rise in food production is necessary to keep pace with the rapid growth of the human population. Diseases with a high rateof spreading can severely reduce plant yields and even wipe out the entire plantation. One cannot overstate the value of early disease detection and prevention. Due to the increasing use of cellphones, even in the most remote areas, researchers have recently turned to automatic feature analytics as a technique for diagnosing crop disease. The convolutional, activation, pooling, and fully connected layers of the CNN have therefore been used in this work to create a disease identification approach.Predictions of soil factors including pH levels and water contents, illnesses, weed identification in crops, and species recognition are the sectors that have received the most attention. The micro-controller system keeps track of meteorological and atmospheric changes and uses sensors to estimate how much water should circulate in accordance. If a pesticide sprayer is attached to the hardware, the technique can also treat plant diseases.Data from the system is tracked and documented using a mobile application.Future farmers will benefit intelligently from the proposed methodology.

Machine Learning, Convolutional Neural Network (CNN), Automatic Coffee Disease Prediction, Image Processing.