Artificial neural networks and adaptive neuro-fuzzy inference system networks’ application in crop production

In the dynamic realm of agriculture, where intricate interactions between environmental factors
and human interventions dictate crop outcomes, the pursuit of innovation has long been a driving
force. Within this context, artificial intelligence (AI) has emerged as a catalyst for precision and
efficiency, offering transformative potential in crop production. Among the diverse branches of
AI, artificial neural networks (ANNs) and their adaptive counterparts, particularly the fuzzy
logic/fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) emerged
as robust tools poised to revolutionize agriculture. Inspired by the complexities of the human
brain, ANNs represent a paradigm shift in understanding and optimizing crop production
systems, offering remarkable abilities to discern patterns, extract insights, and adapt to changing
environmental conditions. This chapter embarks on an illuminating journey into the realm of
artificial and adaptive neural networks, delving deep into their applications and implications in
crop production. Through a meticulous exploration of their architecture, functionality, and real-
world applications, the transformative potential of ANNs in optimizing yields, mitigating risks,
and fostering resilience in agricultural ecosystems is revealed. From predictive modeling and
precision agriculture to resource allocation optimization and decision-making enhancement,
ANNs and ANFISs emerge as catalysts of innovation, propelling the agricultural sector toward a
future defined by sustainability and productivity.