ML techniques along with image processing algorithm are used in precision agriculture to increase food production in agricultural fields. Previous data available at different stages of farming are used to predict the conditions to improve production by means of ML algorithms. ML algorithms are useful at different stages of agriculture, such as yield prediction, disease detection, soil management, water management, weed detection, crop and seed quality and livestock production, etc., to predict conditions for improvement of crop.
In order to increase productivity, yield prediction, one of the main topics in precision farming, is of great importance. Yield prediction is important for growth, crop supply matching demand and crop management. Such forecasts help farmers prevent market demand and supply imbalances triggered or accelerated by the crop quality. Disease detection and pest control both are important for improvement of productivity and crop quality. Pesticide spraying over the cropping area is an effective method for disease control. ML algorithms are useful for the automatic detection of infected plants and it is less time consuming compared with normal eye examination.
Weed detection and control is a big issue in cultivation. Precise weed identification is important for better agriculture, because it is difficult to detect and distinguish weeds from crops. In water management, evapotranspiration data are analyzed using ML algorithms for water resource management and crop production. ML algorithms are useful to estimate soil conditions, temperature and moisture content. ML algorithms are also applied for seed and crop quality estimation. The livestock production refers to studies designed to accurately predict and estimate farming parameters in order to maximize the production system’s economic performance.
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