# A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment
## 2 Related Works
### Deep Learning  
In reference [10], they perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. They examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Their findings indicate that deep learning provides high accuracy, outperforming the existing commonly used image processing techniques. The deep neural network based meta regression and transfer learning (DNN-MRT) technique is expressed by comparing statistical performance measures in terms of root mean squared error (RMSE), mean absolute error (MAE), and standard deviation error (SDE) with other existing techniques [11]. In reference [12], convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaf images of healthy and diseased plants, through deep learning methodologies. The training of the models was performed with the use of an open database of 87,848 images, containing 25 different plants in a set of 58 distinct classes of [plant, disease] combinations, including healthy plants. A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked auto-encoder model is used to learn generic traffic flow features, and it is trained in a greedy layer-wise fashion. To the best of knowledge, this is the first time that a deep architecture model is applied using auto-encoders as building blocks to represent traffic flow features for prediction [13]. There is an increasing amount of unstructured text data produced in cross-enterprise social interaction media, forming a social interaction context that contains massive manufacturing relationships, which can be potentially used as decision support information for cross-enterprise manufacturing demand-capability matchmaking. How to enable decision-makers to capture these relationships remains a challenge. The text-based context contains high levels of noise and irrelevant information, causing both high complexity and sparsity. Under this circumstance, instead of exploiting man-made features which were carefully optimized for the relationship extraction task, a deep learning model based on an improved stacked denoising auto-encoder on sentence-level features is proposed to extract manufacturing relationships among various named entities (e.g., enterprises, products, demands, and capabilities) underlying the text-based context [14]. In reference [15], they present an approach to learning several specialist models using deep learning techniques, each focusing on one modality. Among these are a convolutional neural network, focusing on capturing visual information in detected faces, a deep belief net focusing on the representation of the audio stream, a K-means-based \"bag-of-mouths\" model, which extracts visual features around the mouth region, and a relational auto-encoder, which addresses spatio-temporal aspects of videos. The utilization of a deep learning network (DLN) helps to the discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked auto-encoder (SAE) using a hierarchical feature learning approach. The input features of the network are power spectral densities of 32-channel electroencephalograph (EEG) signals from 32 subjects. To alleviate the over-fitting problem, principal component analysis (PCA) is applied to extract the most important components of the initial input features. Furthermore, a covariate shift adaptation of the principal components is implemented to minimize the non-stationary effect of EEG signals [16]. An improved stack auto-encoder based on the deep learning techniques is proposed to learn the driving characteristics of an autonomous car.