Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

The use of sensors and the Internet of Things (IoT) is key to moving the world\u27s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system

AI Enabled Ensemble Deep Learning Method for Automated Sensing and Quantification of DNA Damage in Comet Assay

Comet assay is a widely used technique to assess and quantify DNA damage in individual cells. Recently, researchers have applied various deep learning techniques to automate the analysis of comet assay. Image analysis using deep learning allows combining multiple parameters of images and performing computation at a pixel level to provide quantifiable information about the comets. The current deep learning analysis algorithms use a single neural network as a standard method, which relies on many comet images and prone to high variance in predictions. Here, we propose a new ensemble model consisting of a collection of deep learning networks with different configurations and different initial random weights trained on the same dataset to calculate one weighted prediction for DNA damage quantification. To develop this model, we curated a trainable comet assay image dataset consisting of1309 images with 9204 extracted features of cell head and tail length, area, etc With the proposed method we could achieve significantly higher accuracy (R2 = 89.3%, compared to 74% with the standard single neural network as reported in data published by M. D. Zeiler and R Fergus (European conference on computer vision, pp. 818–833 2014). Furthermore, deep regression with the proposed architecture produced much more reliable and accurate results than conventional method