## 1 Introduction  
Tomato is the world's most important fresh and processed fruit, and the second-most cultivated vegetable, after potatoes, with an annual worldwide production of 182 million tonnes [1]. It is rich in vitamins (A, C, and E), protein, vital amino acids, and minerals, all of which are healthy for the human body and prevent disease [2, 3]. The high nutritious value of tomato fruit is directly correlated with its production and consumption. Various health-promoting compounds that exist in tomatoes make a significant contribution to their nutritional content [4]. Sugars, acids, and volatile compounds are the primary components that determine flavour [5]. Total soluble solids (TSSs), which express the percentage of lysed solids in a solution, have a significant impact on determining the overall quality of fruits, including both freshly marketed and processed tomatoes [6, 7], and they consist mainly of sugars [8]. The sugar content of fruit is influenced by genotype and is associated with factors such as birx, pH, and fruit size [9]. Throughout the process of domestication, selecting for increased fruit weight has been observed to result in a reduction in sugar content [10]. Sugar content determines how sweet a tomato is, and the primary goal of tomato breeding is to produce tomato with a high sugar concentration [11] because higher TSS significantly improves consumer fruit likeability [6]. Breeding objectives of tomato now focus on improving taste and aroma in response to customer requirements. As a widely generated and ingested crop, tomatoes must have appropriate colour, shape, and texture, as well as appropriate inner qualities such as TSS, acidity, and significant flavour and aroma components. Refractometers are typically used in the process of determining TSS [12]. Nevertheless, this approach is destructive, results in fruit losses from the sampled fruit, and prevents fruit characterisation to guarantee individual quality. In the meantime, performing these analyses requires a lot of time, manpower, and cost. Reducing crop production costs without compromising quality or yield is one of agriculture's primary objectives [13; 14]. In this period, when high efficiency at low cost is desired, it is quite beneficial to use non-conventional, non-destructive approaches as a substitute for typical analysis techniques. These contemporary, non-traditional techniques ensure an effective solution to save resources and minimise human error by providing exact and accurate projections of the desired characteristics. It is also useful in quickly converting huge data sets consisting of a wide variety of data collected from many sources into important information. Predicting with ML (ML) techniques and multi-linear regression (MLR) models are the most widely used methods recently, and have proven to be incredibly efficient, fast and accurate methods of problem-solving in recent years [14; 15; 16]. Regression problems can be effectively solved by ML approaches, as demonstrated by a multitude of research articles. When the models are assessed using the same criteria as regression models, they show minimal RMSE and MAPE values and high R\({}^{2}\) values. The application of techniques for ML in many areas of agricultural production, such as yield, quality, chemical content, maturity estimation, disease and weed detection, correct product selection and classification, has become increasingly widespread in the past few years, and the quantity of research conducted on this topic has also enhanced significantly [17; 18]. Research has been conducted using ML algorithms on many agricultural products such as barley, potatoes, cotton, canola, rapeseed, grapes, tomatoes, soybeans, rice, wheat, and corn [19]. A study on the calculation of the content of soluble solids in peaches utilising hyperspectral images was carried out by [PERSON] et al. in 2020 [20]. An algorithm for distinguishing between weeds and crops utilising ML was created by [PERSON] et al. (2018) [21]. Rice yield prediction was performed in this study by [PERSON] et al.