Artificial Intelligence and Machine Learning for Rice Improvement

Food security poses a major challenge to developing and underdeveloped nations worldwide because of rapid growth of the world population, necessitating increased food production. This escalating demand exerts pressure on agricultural lands as farmers and breeders strive to boost food grain output. Ninety percent of the world’s rice production and consumption takes place in Asia, where rice is a vital staple for 1.3 billion people. In developing countries, many farmers still rely on traditional farming techniques that prove inadequate in meeting the escalating demand for food grains. These outdated methods often involve excessive use of harmful pesticides and chemical fertilizers, leading to deteriorating effects on soil microorganisms and flora, resulting in soil degradation and reduced fertility. The forthcoming agriculture hinges on embracing automation to ensure food security for growing populations. This chapter delves into the assessment and exploration of various solutions, methodologies, and perspectives related to artificial intelligence, machine learning, big data applications, and high-end computing. These innovations present viable paths for data-intensive, transdisciplinary agro-technology research. The urgent requirement for diverse automation approaches is evident in addressing challenges related to rice and other crop cultivation, farm management, agricultural practices, irrigation, fertilization, weeding, harvesting, marketing, food distribution, pesticide management, drainage control, emissions reduction, and environmental impact. Implementing automated farming practices not only enhances soil health and fertility but also streamlines agricultural operations. By leveraging advanced technologies and innovative solutions, the agricultural sector is poised to enter a new era of data-driven exploration, paving the way for sustainable and efficient food production systems.