## 1 Introduction and Related Work
[15] developed a model that was able to recognize 13 different types of plant disease. By using Caffe, a deep learning framework based on CNN, their study achieved precision of between 91% and 98% for separate class tests. [PERSON] et al. [16] proposed a robust DL-based detector for real-time disease and pest recognition in tomato plants. The authors considered the combination of three families of detectors, including Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD). Using several models, such as Visual Geometry Group (VGG-16), ResNet-50,ResNet-101, and ResNet-152, the authors compared the prediction results for nine different types of disease and pest. [PERSON] et al. conducted a survey of machine learning approaches for crop yield with respect to estimation of nitrogen status in precision agriculture, which included various technologies and ML techniques, such as back-propagation Neural Network, combination of CNN or LSTM with Gaussian Processes, M5-Prime Regression Trees, and Least Squares Vector Machine [17]. [PERSON] et al. estimated crop nitrogen status during side-dressing operations [18]. The authors used a neural network model to evaluate maize leaf based on the sensed reflectance of the maize canopy in three channels, including green and red colors and the near-infrared (NIR) range of a multi-spectral charge-coupled device camera. Using aerial images and an artificial neural network, [PERSON] et al. applied two neural network architectures--multilayer perceptron and radial basis function--to predict leaf nitrogen content in corn plants under field conditions [19]. The authors achieved a root mean square error of prediction (RMSEP) of 6.6%, and a minimum prediction accuracy (MPA) of 88.8% for predicting leaf nitrogen content. In [20], [PERSON] et al. described an approach for identifying nitrogen and potassium deficiency in tomatoes with an accuracy of 82.5% using a Genetic Algorithm (GA) based on the color and texture features of leaves. On the other hand, [PERSON] et al. developed an ANN model prototype to estimate the leaf area of tomato growths under changes in climate and salicylic acid in [21]. In addition, a study based on a Support Vector Machine (SVM) was reported by [PERSON] et al. in [22], which improved the methodology by introducing the near-infrared spectrogram of the tomato leaves under investigation in order to classify anti-nematode tomatoes and normal tomatoes. [PERSON] et al. estimated leaf nitrogen status with a reflectance spot sensor and using [PERSON] theorem for the prediction model with multispectral imaging, which exhibited a root mean square difference (RMSD) approaching 4.9% and a coefficient of determination of 0.82, as discussed in [23]. Furthermore, the method proposed by [PERSON] et al. in [24] discussed a novel computational intelligence in vision sensing for estimating nutrient content in wheat leaves. To evaluate the nutrient content, the authors applied deep spare extreme learning machines (DSELM) and a genetic algorithm (GA) to normalize plant images and reduce color variability due to a variation of sunlight intensities. The mean absolute percentage error (MAPE) of the GA-based committee machine with four DSELMs was approximately 2.78%. A method for fusing the deep learning multilayer perceptron (DL-MLP) by means of committee machines to achieve color normalization and image segmentation was proposed by [PERSON] et al. The authors optimized the system architecture for high performance of color normalization and nitrogen estimation using a genetic algorithm (GA). In addition, the authors affirmed that they built a robust statistical model for prediction, rather than recognizing the images. To estimate the nitrogen content, the authors used several standard MLPs to extract the images for three colors (RGB) [25]. [PERSON] et al. [26] also demonstrated a low-cost, simple, and accurate approach for achieving image-based nitrogen amount estimation.