To combat the problem caused by the Fall Army Worm in the country there is a need to come up with robust early warning and monitoring systems as the current manual system is labor intensive and time consuming. The automation of the identification and classification of the insect is one of the novel methods that can be undertaken. Therefore this paper presents the results of training a Convolutional Neural Network model using Google's Tensorflow Deep Learning Framework for the identification and classification of the Fall Army worm moth. Due to lack of enough training dataset and good computing power, we used transfer learning, which is the process of reusing a model trained on one task as a starting point for a model on a second task. Googles pre-trained InceptionV3 model was used as the underlying model. Data was collected from four sources namely the field, Lab setup, by crawling the internet and using Data Augmentation. We Present results of the best three trials in terms of training accuracy after several attempts to get the best metrics in terms of learning rate and training steps. The best model gave a prediction average accuracy of 82% and a 32% average prediction accuracy on false positives. The results shows that it is possible to automate the identification and classification of the Fall Army worm Moth using Convolutional Neural Networks.
To combat the fall Army worm (FAW-Spodoptera frugiperda) pest which has a negative impact on world food security, there is need to come up with methods that can be used alongside conventional methods of spraying. Therefore this paper proposes a machine learning based system for automatic identification and monitoring of Fall Army worm Moths. The system will aim to address challenges that are associated with trap based FAW monitoring such as manual data collection as the system will automate the data collection process. The study will aim to automate the data collection process by developing a machine learning algorithm for FAW moth identification. The study will develop web and mobile applications integrated with Geographic information system (GIS) technology in addition to trap automation. The tools developed in this study will aim to improve the accuracy and efficiency of FAW monitoring by reducing the aspect of human intervention. At the time of writing this paper, only the web based tool prototype has been developed, therefore this paper mostly focuses on the design of the web based tool. The paper also provides a brief quantification of the chosen machine learning technique to be used in the study.
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