Creation of Farmers Awareness on Fall Armyworms Pest Detection at Early Stage in Rwanda using Deep Learning In recent years, research has proven that cuttingedge technology is being used to revolutionize the agriculture sector more effectively and efficiently in both advanced and developing countries. A great number of researches have been conducted, very little research, however, has addressed the use of machine learning in Rwanda to improve farmeru0027s knowledge and skills to be able to tackle agricultural issues related to crops pests. In this regards, we introduced the creation of extension material in the local language Kinyarwanda to enable smallholder farmers to find out the right pesticide to use after identifying the pest, how to fight the infection and how to prevent it from spreading. In order investigate the aforementioned method and proposed technology; we did binary classification where we collected unhealthy plant leaves images from the fields to be tested. We again obtained healthy plant leaves images without damage from a very wellknown research and development unit called Plant Village where we collected our data. The findings of the current study are based on the trained model with Keras. The result of the classification model provided a satisfying accuracy 0f 99.5% and that model was deployed in an Android Mobile App to be used for both pest detection and creation of farmersu0027 awareness on suitable crop pestsu0027 detection and diagnosis in a timely manner. Our findings contribute to the use of machine learning in agriculture literature and have important research in deep learning and practical implications.

Fusing multiscale contextaware information representation for automatic infield pest detection and recognition Abstract   Automatic infield pest detection and recognition using mobile vision technique is a hot topic in modern intelligent agriculture, but suffers from serious challenges including complexity of wild environment, detection of tiny size pest and classification of multiple classes of pests. While recent deep learning based mobile vision techniques have shown some success in overcoming above issues, one key problem is that towards largescale multiple species of pest data, imbalanced classes significantly reduce their detection and recognition accuracy. In this paper, we propose a novel twostages mobile vision based cascading pest detection approach (DeepPest) towards largescale multiple species of pest data. This approach firstly extracts multiscale contextual information of the images as prior knowledge to build up a contextaware attention network for initial classification of pest images into crop categories. Then, a multiprojection pest detection model (MDM) is proposed and trained by croprelated pest images. The role of MDM can combine pest contextual information from lowlevel convolutional layers with these in highlevel convolutional layers for generating the superresolved feature. Finally, we utilize the attention mechanism and data augmentation to improve the effectiveness of infield pest detection. We evaluate our method on our newly established largescale dataset InField Pest in Food Crop (IPFC) and sufficient experimental results show that DeepPest proposed in this paper outperforms stateoftheart object detection methods in detecting infield pest.