The earlier detection of plant disease and recommending pesticides

Agriculture’s production is heavily impacted by the state of the economy. Plant disease is increasingly common in agricultural settings and is now easier to detect as a result of the previously described issue. It is receiving more attention due to the monitoring of crops in a wide range of settings. Farmer transitions between different disease control strategies and encounters significant challenges. We are able to identify or detect leaf illnesses, which is how surveillance and monitoring experts usually find them. If the appropriate control measures aren’t followed, the plants suffer a major consequence, which symbolizes how the quality of the plants’ production will be impacted. The adoption of a mechanical approach or method for identifying diseases is effective and beneficial since it lessens the burdensome work of surveillance in the vast cultivation. The early stages of plant illnesses allow us to identify the signs even before they manifest on the plant’s leaves. The early stage denotes the starting period which is one or two days after affecting the disease. The paper’s primary objective is to use ResNet50 for early detection of plant disease. ResNet50 has been successfully applied to plant disease image classification tasks and has shown promising results. ResNet architecture, with its deep layers and residual connections, has the ability to capture intricate patterns and features, which is useful for identifying the differences in plant images. The results proved that the ResNet 50 has better accuracy, precision and recall than Support vector machines (SVM), artificial neural networks (ANN), Convolutional neural networks (CNN) for plant disease prediction.