Plant or crop diseases are important items in the reduction of quality and quantity in agriculture. Therefore, the detection and diagnosis of these diseases are very necessary. The appropria te classification with smalt datasets in Deep Learning is a major scientific challenge. Furthermore, it is difficult and expensive to generate labeled data manually according to certain selection criteria. The approaches using transfer learning aims to resolve this problem by recognizing and applying knowledge and abilities learned in previous tasks to nove! tasks (in new domains). Convolutional neural network Feature extraction Classification Precision agriculture Mildew disease In this paper, we propose an approach using transfer learning with feature extraction to build an identification system of mildew disease in pearl millet. The deep learning facilita tes a practically fast and interesting data analysis in precision agriculture. The expected advantage of the proposai is to provide support to stakeholders (researchers and farmers) through the information and knowledge generated by the reasoning process. The experimental result gives an encouraging performance that is the accuracy of 95.00%, the precision of 90.50%, the recall of 94.50% and the fl score of 91.75%.
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