Agriculture Pest Classification using Deep CNN Model

Agricultural pests are spurs of economic, social and sorrowful environmental impacts around the globe. To control these pests proper identification and categorization is prudent in strategies used to tackle them. This work highlights the DeepPestNet, a CNN built specifically for accurately identifying nine classes of pests important in agriculture. Base onto the transfer learning of EfficientNetB0 which was developed to boost the performance of pest recognition, DeepPestNet has more convolutional and attention layers incorporated into the framework. Training and evaluation on this broad set shows here DeepPestNet got a test accuracy of 97%. To this end, the algorithm demonstrated an average accuracy of 33% while having stable performance indicators, such as the precision, recall, and F1-scores, for every type of pest. That is why, DeepPestNet is proved as an effective tool in developing new methods of automated pest recognition systems, giving farmers and agricultural specialists the opportunity to reduce crop losses and pesticides application