An Intensive Study on Precision Agriculture: Crop Yield Prediction
Resumen
Farming is largely a challenging and cumbersome profession. The unstable and volatile commodities market squeezes the life out of a farmer who already is experiencing unprecedented scarcity of water on top of ever-rising operational costs. Farmers are constantly subjected to restrictive regulations on irrigation, pesticide use and fertilizer application, which leads them to explore and find new ways to boost the agricultural yield. Fortunately, a huge amount of data is available on modern farms ranging from yield monitors to infrared imaging, but the sad state of affairs that agricultural profession is ages behind other industries in utilizing data to make professional decisions. Soon using data to optimize decision making will no longer be a novelty, but an essential practice to stay afloat in business. This paper discusses different decision making algorithms such as Support Vector Machine (SVM), Bayes Model, Neural Network (NN), Random Forest (RF), and methods. The challenge identifying predictive abilities using promising methods with a small dataset and the characteristics of different machine learning algorithms have been discussed. It has many issues such as learning performance, computation time and scalability. These issues are also discussed detail in this review work.
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