## 1 Introduction and Related Work  
In the process of plant growth, such as for a tomato plant, mineral nutrients play an important role. The essential mineral nutrients involve two kinds of elements: macro- and micronutrients. The macronutrients include Calcium, Potassium, Nitrogen, Magnesium, Sulfur, and Phosphorous. The micronutrients include Boron, Iron, Manganese, Copper, Chloride, Zinc, and Molybdenum. In addition, deficiency of these nutrients affects the growth, yield, and quality of tomato plants and crops. In this paper, we focus on collecting the dataset, diagnosing and predicting the symptoms of nutrient deficiencies that affect the tomato fruit. For example, deficiencies in the three main mineral elements can be observed by means of blossom end rot (BER) for Calcium, green/yellow shoulder and blotchy ripening for Potassium, and paler green and uniformly yellow/chlorosis on leaves for Nitrogen. The representations of nutrient deficiency for Calcium, Potassium, and Nitrogen are manifested early on in the development of tomato leaves and tomato fruits. These three mineral nutrients have an effect on the tomato leaves and fruits, and they directly affect the growth and quality of the tomato harvest, as shown in [1; 2; 3; 4]. Moreover, the analysis of the bad impacts caused by Nitrogen deprivation within 10 or 19 days by [PERSON] et al. in [5] suggested that they contained a higher level of chlorogenic acid and ruin. In general, symptoms of nutrient deficiency with respect to Calcium, Potassium, and Nitrogen will be visible in the growth of the tomato fruit. The appearance of similar symptoms in tomato fruits and leaves could improve our ability to distinguish and predict deficiencies through the process of farming. Recently, artificial intelligence (AI) has found widespread application in various fields, including industry, agriculture, biology, etc. Specifically, deep learning (DL) is increasingly being applied to speech recognition, visual object recognition, object detection, and the like. Moreover, [PERSON] et al. employed deep convolutional nets to achieve breakthroughs in the processing of images, video, and speech [6]. In particular, in the field of agriculture, DL models are can be applied for the forecast and classification of plant disease, management of crops, weed detection, farmland management, etc., as described in [7]. From a technical perspective, [PERSON] et al. carried out a survey of 40 studies using DL models in agriculture applications including several architectures such as the popular CNN architectures (e.g., AlexNet, VGG-16 and Inception-ResNet), the Long Short-Term Memory (LSTM) model, the Differential Recurrent Neural Network (DRNN) model, Scalable Vector Machines (VSM), and the like [8]. Based on visible range images or digital image processing, recent studies have focused on agricultural diagnosis and prediction for symptoms of disease and nutrient deficiency in plants using an artificial neural network [9]. [PERSON] et al. [10] reviewed the application of ANN to agriculture under a variety of conditions with functional ANN. For identification of plant diseases based on visible range images, the discussion by [PERSON] in [11] proposed a number of intrinsic and extrinsic factors, including image background, image capture conditions, symptom segmentation, etc. Recent studies using deep learning have focused on the identification and pathological analysis of plants. [PERSON] carried out research with several CNN-based models to identify leaf diseases in 29 crops based on a database of 87,848 images, and achieved a maximum success rate of 99.53%, as reported in [12]. [PERSON] et al. [13] used a neural network model to classify the diseases of plants, including tomato plants, based on images taken using mobile devices. In [14], [PERSON] et al. built a convolutional neural network from scratch, which was trained and tested on a total of 10,413 images containing 22 species of weeds and crops. Their study results achieved a classification accuracy of 86.2%. [PERSON] et al.