Farming Activities Improving with Intelligent Technology: A Review
Agriculture is full of complex problems, and for healthy output, there are a number of activities in the crop yield cycle, such as cultivation, monitoring, and harvesting phase. The primary issue is that farmers suffer a lot in performing activities and solving complex problems traditionally. Farmers need to overcome it by dealing with these activities smartly. Many researchers have worked on implementing modern technologies to deal with these problems. In this paper, we have highlighted the common activities in each phase of the crop yield cycle. These phases have several activities such as a selection of land and preparation style, planning of water irrigation, land preparation, seed sowing, watering, continuous monitoring, health monitoring of soil and crop, data collection, crop disease identification, pesticides spraying, weed control, segmentation, cutting, picking of crops, and fruit, and sorting activities. We have presented the researcher's study on agriculture activities and implementing AI techniques to solve the complexity of these activities. The AI techniques and their contribution in solving particular activity problems are given such as fuzzy logic (FL), genetic algorithm (GA), neural network (NN), artificial potential field (APF), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony algorithm (ABC), harmony search (HS), bat algorithm (BA), cell decomposition (CD), firefly algorithm (FA) and some other miscellaneous algorithms. In the last, we have presented a detailed analysis of techniques used and for which significant activity it is used, with the help of charts, followed by a conclusion of the proposed work.
Keywords - Agriculture activities; Agriculture problems; Artificial Intelligent techniques; Agriculture Robots