DIGITAL TECHNOLOGIES IN AGRICULTURAL DEVELOPMENT

ABSTRACT

Technological advancement has significant impact in the digital agriculture. Artificial intelligence-based techniques, together with big data analytics, address the challenges of agricultural production in terms of productivity and sustainability. Emerging technological innovations transform agriculture from the traditional farm practices to a highly automated and data intensive industry. The spread of these cutting edge technologies, from GPS and remote sensing to big data, artificial intelligence and machine learning, robotics, and the Internet of Things (IoT), to agriculture adds value to farming, leads to increased yields, lower costs, and reduced environmental impact. Data-driven solutions are unlocking production potential in a sustainable and resource efficient way.

Other techniques are expected to emerge solve open problems in agriculture. Regarding to computer vision related problems, such as crop classification and disease/weed identification, new architectures, and capsule networks will address current CNN limitations.

On the other hand, the extraction of information from natural language documents containing policies and regulations could be addressed by a recurrent neural network (RNN). Specifically, a subtype of RNNs called long short-term memories have arisen in the past years as good at modeling varying length sequential data, achieving state-of-the-art results for many problems in natural language processing (NLP), such as machine translation, information extraction, and text classification.

Finally, another technique will be relevant in the near future: generative adversarial networks (GANs). This type of network could address the problem of data scarcity within agricultural computer vision when transfer learning and traditional data augmentation are not enough. The main advantage of GANs is that they can create pictures with synthetic “real” crops instead of just rotating or adding noise to the existing ones.

Keywords: CNN, RNN, NLP, GANs,Data, IoT, GPS