Transfer Learning-based Weed Classification and Detection for Precision Agriculture

Artificial intelligence (AI) technologies, including deep learning (DL), have seen a sharp rise in application in agriculture in recent years. Numerous issues in agriculture have led to crop losses and detrimental effects on the environment. Precision agriculture tasks are becoming increasingly complicated; however, AI facilitates huge improvement in learning capacity brought about by the advancements in deep learning techniques. This study examined how CNN and VGG16 (transfer learning) were used for weed classification for the application of spraying herbicides selectively in palm oil plantations based on the type of optimizer, values of learning rate and weight decay used on the models. The result shows that the VGG 16 BN model with Adagrad optimizer, learning rate value of 0.001 and weight decay of 0.0001 shows the average accuracy of 97.6 percent and highest accuracy of 99 percent.