Plant Disease Prediction using the Internet Of Things. (IOT) and Machine Learning - Blister Blight in the Tea Plant.

MCA (MASTER OF COMPUTER APPLICATION)

Crop plant diseases seriously jeopardize agricultural productivity and long-term expansion. By taking preventative actions against their attacks, disease control benefits from early anticipation of illness assaults. Modern information and communication technologies (ICTs) are used in precision agriculture (PA) applications to improve sustainable development. Solutions for early disease prediction and proactive disease control against plant disease attacks are in high demand. The sickness can only be detected using the existing computer vision-based illness detection technology after it has already manifested. Using Internet data, these works aim to propose a machine learning technique for estimating the likelihood of a disease attack. The life cycles of plant diseases are closely correlated with environmental conditions. Utilizing environmental aspects of agricultural fields, plant disease incidence can be anticipated. The Multiple Linear Regression (MLR) model is utilized as the ML model since disease attack and environmental factors have a linear relationship. Using the ML approach, environmental parameters in the agricultural field based on the leaves.

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