An Integrative Approach Towards Recommending Farming Solutions for Sustainable Agriculture

Sustainable Agriculture is rapidly emerging as an important discipline to meet societal needs for food and other resources by adopting paradigms of conserving natural resources while maximizing productivity benefits. This paper proposes an integrative methodological approach for critically analyzing Precision Farming (PF) paradigms and Zero Budget Natural Farming (ZBNF), providing sustainable farming solutions and achieving productivity and profitability. This paper analyses the productivity of crops in PF using various machine learning (ML) algorithms based on different soil and climatic factors to identify sustainable agricultural practices for maximizing crop production and generating recommendations for the farmers. When implemented on the collected dataset from various Indian states, the Random Forest (RF) model produced the best results with an AUC-ROC of 95.7%. The Juxtaposition of ZBNF and non-ZBNF is evinced. ZBNF is statistically (p<0.05) observed to be a cost-efficient and more profitable alternative. The impact of ZBNF on soil microbial diversity and micro-nutrients is also discussed

Recent Trends in Deep Learning for Conversational AI

Conversational AI has seen unprecedented growth in recent years due to which Chatbots have been made available. Conversational AI primarily focuses on text or speech inputs, identifying the intention behind them, and responding to users with relevant information. Natural Language Processing (NLP), Natural Language Understanding (NLU), Machine Learning (ML), and speech recognition offer a personalized experience that mimics human-like engagement in conversational AI systems. Conversational AI systems like Google Meena, Amazon’s Alexa, Facebook’s BlenderBot, and OpenAI’s GPT-3 are trained using Deep Learning (DL) techniques that mimic a human brain-like structure and are trained on huge amounts of text data to provide open-domain conversations. The aim of this chapter is to highlight Conversational AI and NLP techniques behind it. The chapter focuses on DL architectures useful in building Conversational AI systems. The chapter discusses what are the recent advances in Conversational AI and how they are useful, what are the challenges, and what is the scope and future of conversational AI. This will help researchers to understand state-of-the-art frameworks and how they are useful in building Conversational AI models