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

Abstract

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. IoT will increasingly be used in precision agriculture and smart environments to detect, gather, and share data about environmental occurrences. The environmental factor that is active at all times and has an effect on a crop from its cultivation to harvest. With the aid of an IoT, we will monitor the following factors: temperature, humidity, and leaf wetness, all of which have an impact on the overall quality and lifespan of grapes. A Self-created database of weather parameter using sensors is introduced in this article. It consists of 5 categories with a total of 10,000 records. Here, experiment has been carried out using our dataset to predict grape diseases on various machines learning algorithm. The system receives overall accuracy of 98.25 % for Powdery Mildew, 98.85 % for Downy Mildew and 93.95 % for Bacterial Leaf Spot.

Keywords

• Agriculture
• Machine learning
• Disease classification
• Weather database