Agriculture intensification has a paradoxical effect, as it increases food production and productivity by increasing farmer's return on investment while instantaneously posing a serious threat to long-term sustainability like depletion of resources, soil degradation, water scarcity and finally environmental pollution. All these challenges have flickered concerns about the quality of life. To bash all these concerns, the precise and judicious use of agricultural inputs is necessary. Bespoke solutions (Site-Specific) tailored to specific problems can optimize resource utilization while minimizing negative impacts. Integrating advanced technologies like automation by the use of sensors, drones and robotics guarantees solutions in the context of availability and efficiency of agricultural labour decline. This technology-driven approach can reform agriculture. So, the holistic approach of using technological advancements with sustainable practices is necessary for a long-term ecological balance with enhancement in productivity. The integration of driven solutions allows farmers to obtain real-time insights into soil health, water availability and nutrient status facilitating sustainable farming practices. The main goal of this manuscript is to review the applications of AI in agriculture for crop monitoring with sustainable use of resources such as soil, water, and nutrients, as well as to elevate food production with better quality maintenance. This article scrutinizes the findings of several researchers to get a brief outline of the subject of the recent execution of automation in agriculture and compares it with conventional methods followed by the farmer.

Unforeseen failures during flight can lead to Loss of Control In-Flight, a significant cause of fatal aircraft accidents worldwide. Current offline synthesized flight control methods have limited capability to recover from failures, due to their limited adaptability. Incremental Approximate Dynamic Programming (iADP) control is a model-agnostic online adaptive control method, which integrates an online identified locally linearized incremental model, with a Reinforcement Learning (RL) based optimization technique to minimize an infinite horizon quadratic cost-to-go. A key challenge for adopting these self-learning flight control methods is validation through flight testing. This paper presents the iADP flight control law design for CS-25 class aircraft to achieve rate control. It outlines the controller evaluation strategy, controller integration, verification & validation procedures, and a discussion on flight test results. To the author’s understanding, this flight test marks the world’s first demonstration of an online RL based automatic flight control system for this aircraft category, demonstrating real-time learning and adaptation capabilities to aircraft configurations.