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Download PDFOpen PDF in browserEnhancing Coffee Crop Management with IoT and Machine Learning: Automated Monitoring and Disease ControlEasyChair Preprint 98866 pages•Date: March 26, 2023AbstractA 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 Download PDFOpen PDF in browser |
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