# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 4 Applications of Deep Learning in Agriculture
### Crop Identification/Classification  
Once a crop is properly cured of all kid of illness and stress, the next critical step for the former is to harvest it at the proper time and market demand. Deep learning can also play an essential role in making a harvesting plan by considering different parameters such as soil type/quality/pH, weather forecast (including temperature, precipitation, humidity, and hours of sunlight), and fertilizers schedule. A research paper [36] is one of this kind. A multi-layer DL architecture was proposed in [37]. It can classify different crop species in a land-covered area by taking satellite imagery from numerous sources. An ensemble of 1-D and 2-D CNNs beat the RF classifier and a chorus of MLPs, facilitating a more accurate classification of summer crops, mainly maize and soybeans. CNN models offer more than 85% classification accuracy for main crops (wheat, corn, sunflower, soybeans, and sugar beets). The authors in [38] suggested a deep residual CNN (ResNet-18) for weed and crop detection using unmanned aerial vehicle (UAV) photos. The goal was to obtain appropriate performance on an embedded device while retaining the same ResNet-18 model features as the basis for quick UAV mapping. This model enabled the detection and mapping of weeds during UAV flight operations and attained an overall accuracy of 94% [38]. In [39], the authors provided a deep learning classification system for various plants using the Seed-lings dataset, which contains photos of approximately 960 different plants from 12 species at various stages of growth. Three pre-trained models, InceptionV3, VGG16, and Xceptionare were used for the said purpose, but Xception was proven to be the best classifier with an accuracy score of 86.21%. A prototype for the CNN model was developed in a study [40] to classify different flowers. It used the transfer learning technique, VGG16, MobileNet2, and Resnet50 architectures to classify publicly available flower datasets, and it produced good results and acceptable accuracy. A hybrid MLR-ANN model for effective agricultural yield forecasting was proposed in [41]. It takes input from MLR intercept and coefficients as weights and bias. A feed-forward artificial neural network with a backpropagation training technique estimates the yield of rice crops. The obtained results showed that the hybrid MLR-ANN model outperforms the traditional models in terms of accuracy. Another deep kind of learning algorithm for classifying plant images to identify plant species based on leaf photos was presented in [42]. CNN and transfer learning both were used as classification techniques. The experimental findings demonstrated that the suggested model effectively extracted and classified characteristics from images. The article referenced [43; 44] presented EVI time series to categorize summertime crops using deep neural networks. Two new DL models were developed called Landsat enhanced vegetation index (EVI) time series and long short-term memory (LSTM). Three gradients boosting machines were used as a classifier, including random forest (RF), XGBoost, and support vector machine (SVM). This model claimed to outclass performance in terms of overall accuracy and ability to identify different crop kinds. A study [45] developed a novel crop-classification method by combining optimal feature selection and the hybrid CNN-RF networks using multi-temporal Sentinel-2 images to classify summer crops. In this study, optimal features were selected using the OFSM model, and both temporal and spatial dimensions were used to get satisfactory classification results in terms of the overall accuracy up to 94.27%. Another interesting paper [46] developed a deep neural network using the R-CNN concept and was able to identify the fruit type and can classify healthy and unhealthy fruits. The model achieved the optimum result with a grading accuracy of over 99% and 97.86% using CNN and R-CNN methods.