AI-Based Yield Prediction and Smart Irrigation

Yield prediction is the primary process of predicting the plant’s growth. It can be determined by different parameters such as temperature/humidity, soil mineral contents, soil pH level, salinity level, climatic conditions, moisture of the soil, etc. These parameters must meet the optimum level to make a desirable environment to sustain productivity. Failure to provide the required condition from any of the parameters will lead the plant to stress and deteriorate, potentially leading to a loss in productivity. Laborious ways of monitoring the agricultural parameters are inefficient and time-consuming. An automated irrigation system is essential for the proper growth of crops. Excessive watering of crops and crop water dehydration can lead to stresses that affect the growth of plants. Supplying an optimum amount of water is essential for the healthy development of plants. The IoT and artificial intelligence technologies are the most viable alternative solution for traditional yield prediction and irrigation because of the remote accessibility, interoperability, cognitive capability, etc. These technologies aid the farmers in monitoring agricultural parameters remotely from embedded devices, drastically reducing human efforts. It makes farming much efficient and a smarter farming experience and also increases crop productivity exponentially. This chapter has manifested the current environmental problems and the potential future challenges that may decrease crop productivity of the crops. It also shows how the sensor technology has gradually evolved the traditional farming experience to solve problems and the possibilities of transforming the current farming practices into much more innovative and eco-friendly with the emergence of artificial intelligence technologies. The main objective of this chapter is to spread knowledge and elevate the current farming practices and several challenges that the farmers are currently facing. It also projects how to bridge the gap between these agricultural problems and challenges with the emergence of the IoT and AI technologies to sustain precision agriculture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.