Compared with field growing, currently, greenhouse growing is preferred by many crop growers. Growing crops in the greenhouse can extend their growing season, protect crops against temperature and weather changes and thus provide a safe growing environment. Moreover, environmental parameters (e.g., humidity, temperature radiation, carbon dioxide, etc. [1
] can also be controlled in the modern greenhouse to guarantee crops grow at the most appropriate environmental conditions.
Crop yield forecasting in greenhouses plays an important role in farming planning and management in greenhouses, and optimally controlling environmental parameters guarantees the maximum crop yield. Cultivators and farmers can utilize yield prediction in greenhouses to make knowledgeable management and financial decisions. However, it is an extremely challenging task. There are many factors that have an influence on crop yield in a greenhouse, such as radiations, carbon dioxide concentrations, temperature, quality of crop seeds, soil quality and fertilization, and disease occurrences (as shown in [3
]). It is not straightforward to construct an explicit model to reflect the relationship between such a variety of factors and crop yield.
In this work, we propose a deep neural network-based greenhouse crop yield prediction method, by combining two state-of-the-art networks for temporal sequence processing: recurrent neural network (RNN) and temporal convolutional network (TCN). The proposed deep neural network is developed for predicting future crop yields in a greenhouse based on a sequence of historical greenhouse input parameters (e.g., temperature, humidity, carbon dioxide, radiation) as well as yield information. According to the experimental evaluations of multiple datasets collected from multiple greenhouses in different time periods, it is shown that the RNN+TCN-based deep learning approach achieves more accurate yield prediction results with smaller root mean square errors (RMSEs), compared with both classical machine learning and other popular deep learning-based counterparts.
2. Literature Works
Although there is much research related to crop yield prediction for the farming field, a relatively small amount of works focus on greenhouse crop yield forecasting. Approaches that have been developed for greenhouse crop yield forecasting are divided into two main categories: the explanatory biophysical model-based approach and the data driven/machine learning model-based approach.
Explanatory biophysical model-based approach: Based on a series of ordinary differential equations (ODEs) reflecting a dynamic process, the explanatory model describes the relationship between some environmental factors and crop growth or morphological development. Different biophysical models have been applied for crop growth modelling which can thus be used for yield forecasting, based on greenhouse environmental parameters.
The Tomgro model is proposed by Jones et al. in [6
], which models the tomato growth and fruit yield with respect to dynamically changing temperature, solar radiation, and CO
concentration inside a greenhouse. A more complex Tomsim biophysical model is proposed in [7
], which contains multiple sub-modules developed for modelling different aspects (i.e., photosynthesis, dry matter production, truss appearance rate, fruit growth period and dry matter partitioning, etc.) related to tomato growth. A crop yield model that describes the effects of greenhouse climate on yield based on ODEs was described and validated in [4
]. This yield model was validated for four temperature regimes. Results demonstrated that the tomato yield was simulated accurately for both near-optimal and non-optimal temperature conditions in the Netherlands and southern Spain, respectively, with varying light and CO
concentrations. An integrated Yield Prediction Model [8
], which is an integration of Tomgro model [6
] and Vanthoor model [4
], is applied to predict the crop yield in greenhouses based on controllable greenhouse environmental parameters. Different biophysical models, including Vanthoor model [4
], Tomsim model [7
], Greenhouse Technology applications (GTa) model, the model proposed in [9
] and their combined version were compared in [10
]. The experimental studies show that the combined model can outperform original models with smaller root mean square errors (RMSEs) for yield prediction. The biophysical models proposed in [11
] describe effects of electrical conductivity, nitrogen, phosphorus, potassium, and light quality on dry matter yield and photosynthesis of greenhouse tomatoes and cucumbers, respectively.
The explanatory model is practical to reflect the actual growth process of crops, which is bio-physically meaningful and explainable. However, the aforementioned explanatory models suffer from the following two main limitations:
There are many intrinsic model parameters associated with a biophysical model and the performance of an explanatory model is highly sensitive to its model parameters (as shown in [13
]). Moreover, the model parameter setting suitable for predicting greenhouse crop yield in one region may not be workable for other regions [13
In addition, most biophysical models are also not universal and restricted to model the growth for a specific type of plant. For example, the Tomgro model [6
] and Tomsim model [7
] can only be used to model/predict the growth/yield of tomatoes.
Due to the limitations of the explanatory biophysical model-based approaches, in this work, we refer to another category of approach–machine learning model-based approach for greenhouse crop yield prediction. More details of the machine learning model-based approach are introduced as follows.
Machine learning model-based approach: Data driven/machine learning technique-based approaches have also been applied for greenhouse crop yield forecasting in many studies, which treat the crop yield output as a very complex and nonlinear function of the greenhouse environmental variables and historical crop yield information. In particular, linear and polynomial regression models are used in [14
] for strawberry growth and fruit yield using environmental data such as average daily air temperature (ADAT), relative humidity (RH), soil moisture content (SMC), and so on. However, an assumption of a linear or polynomial relationship between the crop yield and environmental factors is not always valid. Partial least squares regression (PLSR) has been applied in [15
], for modelling the yield of snap bean based on the data collected from hyperspectral sensing. Neural networks have also been widely applied for greenhouse crop yield prediction. For example, an artificial neural network (ANN) has been applied in [16
], for weekly crop yield prediction. While in [17
], ANN has been applied to predict the pepper fruit yield based on factors such as fruit water content, days to flowering initiation, and so on. An Evolving Fuzzy Neural Network (EFuNN) was proposed in [18
] for automatic tomato yield prediction, given different environmental variables inside the greenhouse, namely, temperature, CO
, vapour pressure deficit (VPD), and radiation, as well as past yield. A Dynamic Artificial Neural Network (DANN) [19
] was implemented to predict tomato yields, based on a series of predictors such as CO
fixation, transpiration, solar radiation as well as past yield. The findings show that the most important environmental variable for yield prediction was CO
fixation, and the least important was transpiration. Although ANN-based approaches have been widely applied for greenhouse crop yield prediction tasks as in [16
], their performance is highly sensitive to different choices of network architectures and network hyper-parameters settings. Furthermore, there is a lack of studies on optimally designing network architecture and tuning network hyper-parameters for the greenhouse crop yield prediction.
The aforementioned works focus on using classical machine learning approaches for greenhouse crop yield prediction. Given a certain amount of training data, classical machine learning models (such as linear/polynomial regression models, artificial neural network model, etc.) are constructed to predict greenhouse crop yields based on certain factors (such as environmental and past yield information). However, these works suffer from limitations due to the adoptions of simple and ‘shallow’ classical machine learning models, for example:
Features extracted from data for building the classical machine learning models may not be optimal and most representative, thus deteriorating the performance for yield prediction (as shown by our experiment, in most cases, the classical machine learning models perform worse than the deep learning-based ones).
The classical machine learning models cannot effectively handle data with either high volume or high complexity.
Deep learning is a very popular machine learning technique and it has been successfully applied in a variety of applications (e.g., image classification, computer vision, natural language processing, etc.) [20
]. Recently, deep learning technology has also been applied for crop yield prediction in the outdoor environment. For example, in [21
], a recurrent neural network deep learning algorithm over the Q-Learning reinforcement learning algorithm is used to predict the crop yield. The results show that the proposed model outperforms the existing models with high accuracy for crop yield prediction. CNN and LSTM are combined in [22
] for both end-of-season and in-season county-level soybean yield prediction, based on the remote sensing data in the outdoor environment. Compared with the outdoor application scenarios, there are very few works related to the applications of the deep learning approach for indoor greenhouse crop yield prediction. Some related works can be found in [5
], from which the researchers have adopted the recurrent neural network (RNN) model with long-short temporal memory (LSTM) units for tomato and ficus yield prediction. Furthermore, it can be seen from the evaluation results that the deep learning-based approaches adopted in [5
] outperform traditional machine learning algorithms, with more accurate prediction results and lower root mean square errors (RMSEs).