**1. Introduction**

In line with an annual increase in greenhouse planting area in recent years [1,2], the solar greenhouse, a relatively low-cost, environmentally controllable, and highly productive option for farmers, has become the predominant facility type used to provide year-round vegetable production in northern China [3,4]. A solar greenhouse has a large roof area along the south side which is passively heated by sunlight during the daytime [5]. Meanwhile, a thermal blanket is rolled over the greenhouse at night to hold heat inside the structure, and a northern brick wall preserves heat inside the structure [6]. Compared with Venlo greenhouses [7], passive solar greenhouses generally provide only basic environmental

**Citation:** Liu, L.; Yuan, J.; Gong, L.; Wang, X.; Liu, X. Dynamic Fresh Weight Prediction of Substrate-Cultivated Lettuce Grown in a Solar Greenhouse Based on Phenotypic and Environmental Data. *Agriculture* **2022**, *12*, 1959. https://doi.org/10.3390/ agriculture12111959

Academic Editor: Maciej Zaborowicz

Received: 12 October 2022 Accepted: 17 November 2022 Published: 20 November 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

control with low-cost equipment [8]. In addition, the use of advanced automated fertigators to ensure sufficient water and fertilizer absorption of vegetables in solar greenhouses has become popular owing to significant labor savings [9]. In production in a Venlo greenhouse, environmental control technology [10,11] is used to regulate indoor environmental parameters such as light, temperature, humidity, and carbon dioxide, making the vegetable growth environment close to optimal. Solar greenhouses have the disadvantages of large temperature fluctuation ranges and frequently weak solar radiation [12], which is not conducive to crop growth, making the crop growth models established in Venlo greenhouse systems unsuitable for application in solar greenhouses.

In a suitable environment, vegetable growth adheres to certain inherent laws throughout the plant's life cycle [13]. Scholars have studied many crop growth models [14–17] with the aim of guiding future crop production in greenhouse systems through regulation of the environment, water, and fertilizer. In one greenhouse crop growth system, a machine learning method based on the expectation maximum algorithm was applied to link environmental parameters with crop growth [18]. Based on only a small number of samples, future crop growth could be predicted several days in advance. Thus, the feasibility of using environmental parameters to predict vegetable growth in greenhouse systems has been verified. However, in the above-mentioned model, leaf area index, evapotranspiration, and dry weight were taken as crop growth indicators, and the leaf area index and dry weight were obtained by destructive methods at intervals of one week. For one thing, indicators obtained using destructive methods cannot provide growth indicators over the whole life cycle sequence of a specific plant, and indicators for shorter time intervals were not obtained. For another, many vegetables needed to be planted in order to assess indicators using destructive sampling during the vegetable growth period, and the process was inefficient and cumbersome. Moreover, the indicators used to measure vegetable growth could not directly reflect the current vegetable yield (i.e., fresh weight).

The fresh weight of vegetables is an important index for accurate evaluation of the growth process, so it is of great significance to apply the fresh weight index to the prediction of crop growth. Compared with hydroponic vegetables, the online, nondestructive monitoring of the fresh weight of substrate-cultured vegetables during the growth process is a challenge. In view of the importance of fresh weight, Yanes et al. [19] proposed a deep learning image segmentation method to obtain information from canopy images for the estimation of fresh weight of hydroponic lettuce, and a regression model relating lettuce size and fresh weight was established. Jung et al. [20] established a model of the relationship between the projected area of lettuce canopy and fresh weight in an environmentally controllable, water-based lettuce cultivation system based on the morphological analysis machine vision method. Jiang et al. [21] developed a fresh weight estimation system for hydroponic lettuce based on online image processing, which realized high-precision estimation of the fresh weight of lettuce and allowed environmental control for high-quality production. In hydroponic vegetable production systems, the plants can be removed from the nutrient solution temporarily and directly weighed without hindering their continuous growth. This is convenient for nondestructive calibration of fresh weight and makes it easy to realize nondestructive, high-precision fresh weight estimation. In substrate culture systems, the plants can be taken out of the substrate and directly weighed to accurately obtain the fresh weight. However, plants weighed in this way will not continue to grow [22,23], and the subsequent fresh weight growth cannot be obtained. It is difficult to achieve nondestructive estimation of the fresh weight of substrate-cultivated vegetables. In order to solve this problem, Liu et al. [24] proposed a fresh weight estimation method based on the fusion of phenotypic characteristics and environmental parameters, which was used to realize nondestructive estimation of the individual and population fresh weights of substrate-cultured lettuce in a solar greenhouse.

However, accurate prediction of dynamic fresh weight growth based on in situ sensing in solar greenhouse systems is still a challenge. Fresh weight growth of vegetables is affected by many complex environmental factors [25]. Large indoor temperature fluctuations and frequently weak solar radiation in solar greenhouse systems lead to differences in the fresh weight growth of different batches. There is a complex and uncertain relationship between vegetable fresh weight growth and environmental factors. Therefore, in contrast to the static modeling of fresh weight under hydroponic conditions [19], a novel prediction method for the dynamic growth of leafy vegetables based on phenotypic and environmental data of batches is proposed herein, which is able to predict the dynamic fresh weight of substrate-cultivated lettuce in a solar greenhouse system under normal water and fertilizer conditions.

The main contributions of this paper are as follows:

(1) Multibatch substrate-cultivated lettuce cultivation experiments were carried out, with the growth environment and lettuce canopy images monitored in real time. A dataset was built using phenotypic and environmental data of batches.

(2) Computation of the cumulative environmental factors and instantaneous fresh weight of batches of lettuce was achieved. The optimum response time was explored via the most significant correlations between cumulative environmental factors and fresh weight growth.

(3) A dynamic fresh weight prediction model was established using a naive Bayesian network, based on cumulative environmental factors, instantaneous fresh weight, and fresh weight increments of batches, which can be used to predict the dynamic fresh weight of substrate-cultured lettuce in a solar greenhouse system.

#### **2. Materials and Methods**

#### *2.1. Experimental Design*

The experimental site was Solar Greenhouse No. 6 in Shandong Agricultural University Science and Technology Innovation Park, located in Tai'an City, Shandong Province, China (36.16◦ N, 117.16◦ E). The greenhouse has a span of about 8 m, a height of about 4 m, and a length of about 50 m from east to west. The experimental material was Italian lettuce, which was produced by Hebei Maohua Seed Industry Limited Company. The main characteristics of this lettuce are a semi-erect form, plant height of about 26 cm, development of about 28 cm, and nearly round leaves. The color is emerald green, and the loose leaves do not form a ball. In order to improve the accuracy of the dynamic fresh weight prediction model, multiple batches of planting experiments were carried out. The same variety of lettuce was used for the multiple batches of planting experiments. When the lettuce seedlings in a batch had grown to five leaves and a heart, the batch was transplanted into a planting tank filled with substrate.

The aboveground growth environment of the lettuce was the closed microclimate environment of the passive solar greenhouse. Due to the structural characteristics of a passive solar greenhouse, only simple environmental regulation could be achieved during the lettuce growth process, barring the introduction of heating, fans, supplementary lights, etc. For example, in the morning, the thermal blanket was opened to allow storage of heat from the sunlight. At noon, the vent was opened to allow natural ventilation for dehumidification, cooling, and air exchange. In the evening, the thermal blanket was closed for insulation, so as to ensure a normal indoor lettuce growth environment and prevent frostbite of the lettuce plants. The underground growth environment of the lettuce plants was the substrate. The substrate had the characteristics of good ventilation and a good drainage effect, but the water retention effect was relatively poor. Therefore, Yamazaki formula nutrient solution at a 100% concentration was used for irrigation via the water and fertilizer application system in the greenhouse (Figure 1), ensuring normal water and fertilizer conditions throughout the lettuce cultivation experiment.

**Figure 1.** Lettuce cultivation experiment.

#### *2.2. Acquisition of Environmental Data and Lettuce Images in the Solar Greenhouse*

An environmental monitoring and image acquisition platform (Figure 1) was used to record the temperature, humidity, photosynthetically active radiation, carbon dioxide concentration, and lettuce canopy images in the solar greenhouse during the lettuce cultivation experiment. The platform was mainly composed of a support mechanism, guide rail slide, hanger, cross bar, sensor, and controller. The support mechanism was used to support the guide rail slide so that the guide rail slide could move horizontally in the north–south direction at a certain height from the ground. The guide rail slide was fixed at the upper end of the support mechanism and the cross bar equipped with the sensor was connected through the hanger, so that the sensor could move in the north–south direction synchronously with the cross bar. The height of the cross bar could be adjusted according to the current situation, and the cross bar and the guide rail slide were kept vertical in the horizontal direction. The guide rail slide was controlled by the controller and the cross bar equipped with sensors was moved to complete the environmental monitoring and image acquisition tasks in the upper part of the planting area.
