Plant Growth Monitoring System Using Artificial Intelligence

he percentage of fertile land for agriculture in India is above 50%, but the agricultural production, despite having such a high percentage of fertile land for agriculture, is not at the level expected. As most of the times, the expectations fail to be fulfilled as a result of the various plant diseases that affect the production of food. The purpose of this study is to propose a multilayered convolutional neural network algorithm that is used to predict the type of disease, type of fertilizer, and pesticide that should be used, thus helping farmers to initiate necessary action at the earliest stages of disease. As a result of using datasets that are readily available on GitHub, the proposed system is able to predict the disease on the basis of the datasets. A convolutional neural network algorithm is used for the implementation of the system in order to classify the diseased leaves of the plants using a convolutional neural network algorithm. Using the high-resolution camera, real-time images are taken of the plant leaves and are compared with the available data sets that have been collected for healthy and unhealthy plant leaves. Predictions are then made based on the test vectors from the trained model with the optimum outcomes. Depending on whether or not the plant is healthy, the prediction results are displayed on the screen.