Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 55

The factors that responsible for crop yields, like soil type, climate variables, water application, and crop management, are nonlinear and complex. Traditional statistic applications lead inaccurate results in yield estimation. There are several studies reported about crop yield estimation using neural networks in response to climate, soil, genotypes, and crop management practices. A design to train an ANN to forecast the soybean production demand in Brazil was framed by adopting a nonlinear autoregressive solution [42]. They concluded an increase of about 26.5% for 2017 compared with 2016. An ANN is used to predict soybean yield and production and to compare with time series analysis [43]. A crop yield model corresponding to soil-related parameters was developed [44] by training a BP neural network. Different vegetation indices and plant density were analyzed from UAV to analyze grain yield of corn crop by using an NN model [45].

Maize yield estimated using time series data of different satellites and also radar with a neural network with an R² of 0.69 [46]. Chlorophyll data were used to estimate productivity of corn crop [47] resulting in an r² of 0.73. Vegetation indices and crop height were correlated with maize yield to predict maize yield using neural networks [48, 49]. Corn grain yield was calculated with RS-based plant density, canopy cover, and VI using neural networks [50]. Sugarcane yield was estimated using feed forward and back propagation neural network with the crop attributes derived from remote sensing data. They observed stable results with R² values of 0.916 and 0.924 during testing and training, respectively [51]. Although there are many applications of the ANN in yield prediction, the problems are area specific and needs simplified neural networks.