Food is one of the most fundamental necessities and is crucial for survival. Loss of the food source due to pest infestation attributes towards destroying one-fifth of the yearly worldwide crop yield. The past few decades have witnessed a burgeoning trend of using computerized methods for discerning various diseases found in crops. The main advantage of digitizing the detection process is that it eliminates the errors and miscalculations associated with manual detection. With the advent of Object Detection and Artificial Intelligence, malady detection has not only been rapid but has also maintained the expected level of accuracy. The concepts and models of deep learning have been efficaciously applied and used to identify as well as classify plant diseases. In the scope of this research paper, we present a comprehensive digitized approach to detect plant diseases by utilizing image detection, computer vision, and deep learning models like the Convolutional neural networks, Inception model, and the Visual Geometry Group (VGG16) model. In addition to this, the performance of the above-mentioned models has been evaluated by the virtue of metrics like f1 score, accuracy, precision, and recall.
Applications of Artificial Intelligence for Precision Agriculture