Future Intelligent Agriculture with Bootstrapped Meta-Learning and є-greedy Q-learning - Sasikala, D.; Sharma, K. Venkatesh (Journal of Artificial Intelligence and Capsule Networks, 2022)
Agriculture is a noteworthy and vibrant domain in the fiscal evolution of the globe. With population in progress, climatic situation and assets, and agriculture turn out dazed to be a crucial task to realize the necessities of the future population. Intelligent precision agriculture/intelligent smart farming has transpired as an innovative tool to tackle hovers of the future ahead in automated agricultural sustainability by leading Artificial Intelligence (AI) in agriculture automation. AI unravels critical farm labor challenges by improving or reducing work and lessening the necessity of numerous workers. Agricultural AI aids in reaping harvests quicker than human employees at a greater quantity, further precise in categorizing and eradicating unwanted plants, also dropping cost and menace. This process motivates the cutting-edge technologies capitulating the machine capability to learn by sourcing Bootstrapped Meta-learning also reinforcing with rewards as maximum crop yields and minimum resource utilizations as well as within time limits. AI empowered farm machinery is the key constituent of the future agriculture revolution ahead. In this exploratory work, an efficient automation of AI application in the field of agriculture sustenance is ensured for receipt of the most obtainable aids as outcomes and inhibiting the applied assets. Fixing the precise real-time issues trailed by unravelling it for agricultural augmentation or amplification thereby leads to the global best future agriculture.