Enhancing Coffee Crop Management with IoT and Machine Learning: Automated Monitoring and Disease Control
EasyChair Preprint 9886
6 pages•Date: March 26, 2023Abstract
A rise in food production is necessary to keep
pace with the rapid growth of the human population.
Diseases with a high rate of 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 cell phones, 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.
Keyphrases: Automatic Coffee Disease Prediction, Convolutional Neural, Network (CNN), image processing, machine learning