Analysis and Application of Regularized Neural Networks in Smart Agriculture

Crop related services like fisheries, sericulture hubs, animal husbandry, and agriculture, that is, traditional farming methods, play a highly vital role in the progression of the economies of the developing third world countries and are also responsible, to some extent, for the current status of the so-called developed countries. Good crop choice is a vital parameter that is directly proportional to the amount of yield of a particular crop a farmer gets in an agricultural year. Poor crop selection patterns that are not per external factors like rainfall, temperature, humidity, etc. lead to detrimental outputs and yields, which may even be a factor to some length, in the increasing debts that the Indian farmers are in for the past 8 years. Thus there are direct consequences of bad crop selection and poor yield to the social, economic, and mental wellbeing of the farmer. The Indian agriculture industry is heavily at the mercy of climate in different parts of the year. To this view, over the past years, many different Artificial Intelligence-based techniques have been introduced to try to revolutionize the farming industry in some way. These techniques come under the banner of Precision Agriculture. Concepts used in precision agriculture include Ensemble models, KNN based models, Similarity-based frameworks and many other techniques to mitigate traditional problems in farming. Along the same lines of thinking, we discuss in this paper, a regularized ANN-based method to better recommend crops based on selective factors like rain and temperature.