Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors.

Machine learning with IoT practices in the agriculture sector has the potential to address numerous challenges encountered by farmers, including disease prediction and estimation of soil profile. This paper extensively explores the classification of diseases in grape plants and provides detailed information about the conducted experiments. It is important to keep track of each crop's current environmental conditions because different environmental conditions, such as humidity, temperature, moisture, leaf wetness, light intensity, wind speed, and wind direction, can affect or sustain the quality of a crop.

Grape dataset: A dataset for disease prediction and classification for machine learning applications through environmental parameters.

Grapes are indeed a vital crop for both the fruit and wine industries globally. They are cultivated in numerous regions around the world and contribute significantly to the economy and culinary culture of many countries. By prioritizing disease detection and implementing proactive management strategies, growers can effectively safeguard their grape crops, optimize yields, and sustain the productivity and profitability of the global fruit and wine industry.