An integrated approach for predicting and broadcasting tea leaf disease at early stage using IoT with machine learning – A review

Abstract Plants are considered to be vital as they are the resource of energy provider to mankind. Leaves can be affected at any time between sowing and harvesting. It can lead to huge loss on the production of crop and economical value of market. Therefore, leaf disease recognition plays a vital role in agricultural field. But, it requires enormous manpower, huge processing time and extensive knowledge about plant diseases. Hence, machine learning is applied to identify diseases in plant leaves as it analyzes the data from diverse aspects, and classifies it into one of the predefined set of classes. The morphological features and properties like color, intensity and dimensions of the plant leaves are engaged into consideration for categorization. This paper discusses a summary on diverse types of plant diseases and different classification techniques in machine learning which are used for identifying diseases in diverse plant leaves. The blending of internet of things (IoT) with ecological sensing and image processing mechanism has opened a new era to observer the health of plants. Taxonomy of plant diseases at the initial stages using image processing and analyzing environmental sensing data not only helps farmers to obtain healthy plants but also take full advantage of the production. To screen and classify plant diseases IoT is important to send images and give feedback on it.