A Comprehensive Review on Optimizing Agricultural Production Using Machine Learning and IoT
Keywords:
ML algorithm, Data Analysis, Big Data, Crop Recommendation.Abstract
"Optimize Agriculture Productionusing Internet of Things and ML is a rapidly expanding field in agricultural. Crop prediction holds utmost significance in production. The smart information system assists farmers by providing information relating to all environmental factors, suggestions and offer of crop sowing recommendation. Generally, Farmers choose their crops without taking the environment into account. Poor harvest results from it. These are the concerns that farmers and agriculturists are currently facing. These are the current issues of the agriculturists and farmers.Machine learningtechniques and IOT offer a promising solution by automating crop recommendations. This study reviews the production of crop using machine-learning technique and IOT. The suggested system makes accurate predictions about which crops would be most suited for a given site by utilising a number of features, such as soil and weather data.The potential for such a novel method to transform crop recommendation might help farmers to increasing crop production.With the help historical dataset, we trained and tested the ML algorithms with different parameters, ultimately achieving near-perfect accuracy. All models exhibit accuracy levels exceeding 94% on a consistent basis, with the best accuracy yet measured reaching an astounding 99.7%.This study presents perfectly accurate machine-learning models for crop recommendation. The method accurately predicts the most suited crops by utilising a variety of characteristics, including soil and weather data. This technology has the potential to be revolutionary in that it can improve agricultural yields, sustainability, and overall profitability, which will help farmers of all sizes. For higher production we have to move from traditional approach to advanced approach. We are convinced that with the help of latest approach, change crop recommendations and help guarantee a long-term. With more thaneight billion people on the planet, our dependence on agriculture for food necessitates the establishment of resilient and sustainable agricultural systems. The manuscript's future prospects include utilising our models to develop an end-to-end system and surveying farmers to obtain numerical estimates of the impacts.
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