Plants health monitoring and prediction for precision horticulture

Abstract

The hydroponic farming industry is growing rapidly, and it can solve major world food crisis problems. Health monitoring and diseases identification of the plant is very important for agriculture. Monitoring of health and disease on plant plays an important role in successful cultivation of crops in the farm. Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. It is very difficult to monitor the plant diseases manually. Some diseases spread so rapidly they can affect an entire plant and crop. Early detection of a diseases can prevent an entire crop from harm and attention a farmer would need to place on their system. The proposed system is capable of detecting the disease at the earlier stage as soon as it occurs on the leaf, hence saving the loss and reducing the dependency on the expert to a certain extent is possible. The aim of this study is to design, implement and evaluate an image processing-based software solution for automatic detection and classification of plant leaf diseases. This technology helps the farmer to identify what type of diseases that the plant is being affected. The image has been processed in MATLAB and the status of the leaf has been identified with the help of neural network classification. Then the environment circumstances such as temperature, humidity and moisture has been monitored. After the image has been processed in the software it sends SMS to the user by using Global System for Mobile Communication (GSM). The SMS contains leaf status, particular solution and environmental conditions.