Agriculture or farming as it is commonly known, is the practice of growing crops and raising cattle. When people began growing crops, they also began herding and breeding animals. Many of them are also sources of meat, milk, cheese, and butter. Agriculture provides food as well as raw material for the industry such as wood, cotton, leather, wool, and paper products. Agriculture also helps for producing resources needed for creating commercial products. It contributes greatly to a country’s economy. Conventional farming is mostly practiced all over the world. It involves techniques suggested by experienced farmers. In conventional farming, farmers need manual labor to handle crops and livestock, often leading to inefficient resource use. All over the world, there is a desperate need to increase the productivity of agriculture products due to the following reasons:

Demand for agricultural products is rising exponentially with the increase of the human population. According to a study [1], the global population is expected to rise from 1.8 billion in 2009 to 4.9 billion in 2030, leading to a drastic rise in demand for dairy products.

Farmers need to learn the techniques or methods to increase the crop yield and to maximize the income. They must have proper information such as temperature, humidity, soil PH, etc., about all the factors that are involved in making a decision on suitable crop selection for their field [2–5]. The factors that are crucial for crop growth after sowing seeds should also be known and considered by the farmers. The health of the crop, higher yield, and maximum income can be guaranteed by the consideration of all relevant parameters. The agriculture sector is suffering because of the inability of useful farming decisions and insufficient resources. Smart farming can manage this downside of conventional farming by engaging modern technologies such as machine learning algorithms [6] and IoT for its appropriate replacement. Recent trends such as IoT [7], data science and big data analytics [8–10] have a great impact on people’s daily lives and other fields such as health and agriculture. The IoT-based agriculture system ensures the continuous monitoring of the field and these systems can enhance the crop yield [1].

Farmers being the main stakeholders do not have the proper information to make the best suitable farming decision. An automated decision-making method is required to choose suitable crops according to the soil conditions and climatic changes. These systems complement the knowledge and experiences of farmers; they have been through the generations [11]. The performance of a decision support system (DSS) depends upon the accuracy of inputs and the efficiency of machine learning (ML) algorithm for data analysis. Temperature, humidity, soil PH, rainfall, and light are the major factors to be considered to monitor crop growth and improvement in crop yield. The automated monitoring system takes inputs at regular intervals. These automated systems work with less or no human intervention.

Climatic conditions vary due to global warming, particularly affecting water levels and it is difficult to find these variations but IoT is very helpful to measure air humidity, temperature and soil moisture [12]. To monitor the growth of crops to increase the yield it is very important to know the soil moisture content, soil PH, temperature and weather conditions. The fuzzy logic-based irrigation system makes a decision using data loggers and sensors [13]. The irrigation system uses IoT to sense environmental factors and send data to the proposed system to control the water pump [14]. The researchers in [15] studied crop yield estimation under three parameters such as moisture, humidity, and temperature using fuzzy logic.

Machine learning has an incredible role in agricultural applications to deal with the problems of conventional farming. Machine learning-based applications have different phases, such as the acquisition of data, data preprocessing/data preparation, picking the model and training the model, evaluation of the model, tuning of parameters, and finally prediction of dependent attributes. Machine learning may be used to predict different attributes of agriculture parameters such as yield, crop growth, fertility of soil, plant disease, and identification of species. The study [16] used data (temperature and rainfall) and then analyzed the data using different machine learning algorithms which is very useful for countries having low to middle income.

The motivation behind this research is to produce a system that overcomes the conventional agricultural challenges for example high cost, time-consuming, and low yield, which is difficult for the farmers due to poor knowledge of soil, environmental factors, and their effects. Our proposed system inputs the air temperature, air humidity, rainfall, soil PH, soil moisture, nitrogen (N), potassium (K), and phosphorous (P) as static data and real-time data using sensors to predict the suitable crop using ML and EL (Ensemble Learning). ML and EL have an incredible role in agricultural applications to deal with the problems of conventional farming. Fig. 1 shows the diagram of the proposed model. The input variables are environmental factors and soil characteristics, and the output variable is the suitable crop for a particular block field. The output is predicted through input variables.

The main contributions of the proposed approach are as follows:

To design a crop prediction system using static data and sensor’s input data with ML and EL.

There are the following sections in this manuscript: Section 2 discusses the related works on IoT-based, ML, and EL-based systems for smart farming. Section 3 explains the details of the proposed methodology including proposed architecture, techniques of data analysis, and used hardware. Section 4 presents the results and evaluation of the proposed approach and finally, Section 5 presents the conclusion and future work.

The agriculture sector demands a smart farming system to increase the yield of agricultural products by automatically monitoring and controlling environmental factors and soil characteristics. In this domain, researchers have proposed different systems for open-field farming. The existing systems considered various factors that are relevant to smart farming such as air temperature, air humidity, and soil moisture effects on crop health and crop yield. Artificial intelligence (AI) and IoT are technologies that can play a vital role in building sensor-based smart farming systems that can predict the suitable crop for a particular field area. The authors highlighted the leadership role of artificial intelligence in current as well as future endeavors [17]. The weather sensor-based smart system that monitored the health status of vineyards to improve the quality of wine [18]. It involved various operations: data analysis was performed remotely, vegetation analysis was done by video processing, data was exchanged wirelessly and finally monitoring evaluation of data. This system used various subsystems and it is tested in real scenarios by using two pilot sites. The AI also suffered several issues, the methodology presented is based on artificial intelligence to identify and counter cyber threats/risks [19].

To increase the yield of the crop, an agriculture system is required that can monitor the plant growth to adjust the supply of water, the use of fertilizer according to the need of the crop and control the environmental factors. Leaf health monitoring can be very useful in predicting the maturity and growth of leafy crops. The smart camera-based system is proposed that takes the leaf dimensions by using ultrasonic sensors to analyze the age and maturity of crops to predict leaf growth [20]. The system is implemented and tested on the available dataset of tomato leaf growth. The results are lacking to show the longer times growth phase due to the unavailability of the crop dataset. The IoT-based protocol is designed to monitor the farm remotely and make a decision accordingly for smart agriculture applications [21]. The energy consumption, latency, and delay of network communication are controlled using routing and clustering algorithms. K-means algorithm used for the formation of clusters.

Smart agriculture systems can be more advanced using image processing techniques, the cameras are installed in crop fields to take images of the crop field, and then image processing techniques are used to classify the images, extract the features and patterns as per requirement. The research proposed a system that combined the IoT and image processing to get real-time data using sensors and cameras and then analyzed the data to make decisions and communicate with farmers [22]. The CC3200 chip-based system is proposed that has a network processor, Wi-Fi unit, and microcontroller that provides accurate information such as humidity and temperature of air to the farmer [23]. The IoT-based system PMAS collects various kind of data from multiple sources that decides the process of agriculture production [24].

The soil deterioration reduced the economic benefits of agricultural products, as it forced the farmers to use more water, fertilizer, and nutrients. The causes arise due to farmer’s unawareness, they do not make a selection of crops according to field conditions, environmental factors, and soil characteristics. So there is a desperate need to address the issues of farmers that result the crop mismanagement and nutrition extraction. The equation is proposed to control the erosion of wind and degradation of soil [25]. The research proposed an AI-based technique that used soft sensors and deep learning [26]. The input pre-processing is performed to remove noise and clean the data, the neural network is used for feature representation and finally, classification is done using auto encoder and kernel-based convolution network architecture.

The IoT-based approach is proposed to predict the sugar yield from sugarcane. The process output in the prediction model is investigated using a machine learnin based approach. All the methods were assessed and it was found that the proposed method is the best and most efficient [27]. The authors predicted the collapsibility of loessial soils using machine learning algorithms [28]. The smart agriculture system predicts the crop yield and also suggests the crop based on previous crop sowing in the farmland to utilize the soil nutrients according to the need of the crop [29].

Ensemble learning model using different machine learning models as base-level models to predict the leaf area index of summer maize under various fertilizer and water conditions [30]. The stacking technique of ensemble learning is used. The system is evaluated using different metrics such as Root Mean Square Error (MSE) and R2. The research for the analysis of user behavior and tracking the particular audience to show their content using ensemble learning [31]. This system improved behavioral analytics by classifying users’ retail behavior. Table 1 shows the comparison of the proposed study with previous smart farming approaches.

Existing studies used just a few algorithms for prediction but our system used 14 algorithms.

The system used a static dataset for module-1 and hybrid data collection for module-2 using various sensors to collect real-time data and manual data in different regions of Punjab, Pakistan. Fig. 2 shows the architecture of the proposed methodology.

To evaluate the performance of our proposed approach, we have calculated the accuracy, precision, recall, and f1-score of the proposed approach. We have evaluated the proposed approach performance with all machine learning algorithms such as KNN, DT, RF, SVM, LR, and GB. We have analyzed all algorithms by performing the same steps that we have performed for the KNN algorithm and compared the results.

Farmers use traditional methods that are time-consuming and expensive, expensive as well as they also suffer economically due to the involvement of manual labor and incorrect selection of crops without knowing the environmental factors, soil characteristics, and nutrition. Our proposed approach is really helpful for the farmers to get maximum yield by making the correct selection of crops for the field. our approach address this issue with ML and EL algorithm, module-1 uses static data input of twenty-two crops, perform data analysis, and predict the suitable crop, and module-2 uses sensor-based and manual data collection of six crops for prediction. The experimental results show that the system is intelligent enough to predict the suitable crop and plays a vital role in increasing the earnings of farmers by maximizing the yield of crops as well as making the agriculture profession competitive. Our system is very cost-effective as all the hardware is available at low cost. Our study collected the data from sensors and farmers but did not use social networking and business data that can also be helpful to further increase the yield at a low cost. The proposed approach considered the environmental factors but did not consider the data according to the weather of any particular zone. The main concern in data collection is the quality of data, the complex data set may make it difficult to execute smart farming procedures. The ML does not require to be explicitly programmed but on the other hand, there is a problem of interpretability. We have used advanced machine learning algorithms which are difficult to interpret but outperform. Overfitting and underfitting of trained model can affect the model accuracy and as a result system performance can be affected, our proposed system addresses this issue and tries at best to minimize it.

Although our proposed system is beneficial for the farmers it is difficult for the farmers to adapt to new farming environments where they have no role in decision making. The economic benefit of the farmer is the big motivation to adapt our proposed system. There is a need to train and educate the farmers to understand the use of the system to get its maximum benefit.

In the future, more algorithms can be used such as neural networks and we can use real-time datasets with more features and more crops. We can also develop more modules such as crop growth monitoring with sensor cameras and crop plant disease detection.