Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 54

Various ANN techniques are suitable to use in agriculture. The widely accepted neural network includes radial basisfunction neural network (RBFNN), backpropagation neural network (BNN), convolutional neural network (CNN), recurrent neural network (RNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for different applications. These NNs are also used in combination with remote sensing classification, rice mapping [17, 18]. Rice mapping is done by combining MS Sentinel 2 and Sentinel 1 SAR band using the LSTM network and dynamic time warping distance approach [19]. Detailed crop classification and mapping is the area for high scope of NN application [20, 21]. A ANN-based model combining a CNN and generative adversarial network (GAN) is used for crop classification with advantages of few data requirement and high flexibility [22]. A CNN-based approach with fused MODIS NDVI data, multitemporal Landsat, and synthetic phenological variable classifies a land accurately into small patches [23]. The methodology for crop classification using deep learning models was framed [24]. They proposed CNN model from multiple Landsat and Sentinel-1 images. Cropland was classified by a LSTMRNN model using Landsat imagery with high accuracy [25]. Wheat production was forecasted and compared the performance of different training methods using an ANN model [26].

Plant identification is done using deep learning neural networks and showed good performance [27, 28]. Deep learning algorithm is used for mango fruit identification and fruit classification [29]. Support vector regression (SVR) and genetic algorithm (GA)–based BPNN were combined to develop a spectral model for evaluating cultivated land quality based on MODIS-GPPs on late rice phenology [30]. SVM and ANN combined to predict crop with accuracy of 86.80% in India adopting the factors like temperature, rainfall, relative humidity, soil, and so on [31]. Five remote sensing (RS) data-derived parameters like VI, slope, vegetation dryness index, temperature, patch-fractal dimension, and road accessibility were used, and they found that cultivated land quality was significantly and nonlinearly correlated [32].

Machine learning methods were also used in pest classification of crops [33, 34]. The CNNs were used to identify the plant pests from leafs [35, 36], weed classification [37], crop quality evaluation [38], and field pest classification. Many agricultural-related research surveys were done to provide comprehensive results [39]. An ANN model used was for soil moisture estimation using temperature of soil, temperature of atmosphere, and RH. Artificial neural networks were adopted to predict the production of biofuel from cow manure and agricultural wastes at high accuracy [40]. The study helped in identifying favorable conditions to predict the behavior of biofuel production in a short time [41].