1. Introduction

2. Related Work

Figure 1. Flowchart followed by Taha, M.F., et al for their proposed system with permission, this image has been reproduced[31].

Figure 1. Flowchart followed by Taha, M.F., et al for their proposed system with permission, this image has been reproduced[31].

Figure 2. A healthy and fully nourished plant that covers the entire region of Green House With permission, this image has been reproduced [33].

Figure 2. A healthy and fully nourished plant that covers the entire region of Green House With permission, this image has been reproduced [33].

Table 1. Machine Learning Approaches for Agriculture Industry.

2.1. Approaches for Farming

2.1.1. Urban Horticulture

In rapidly urbanising areas, urban horticultural cropping systems present a promising alternative for sustainable agriculture and food security. This essay examines their changing surroundings, highlighting the patterns and difficulties that have shaped their growth and influence on metropolitan settings. Urban horticulture's major themes include vertical farming, rooftop gardens, hydroponics and aeroponics, Internet of Things (IoT), integration of optimised space and resources, and year-round growing [52,53,54].

2.1.2. Vertical Farming Technique

The most advanced agricultural technique available today increases crop yield per unit area through vertical farming. The goal of this effort is to provide a regulated environment for plant growth in a smart vertical farming system that is facilitated by the Internet of Things. This system balances pH and total dissolved solids (TDS) automatically, and it makes use of many sensors as well as the hydroponic Deep Flow Technique (DFT). In terms of plant development characteristics including plant height, maximum leaf length, maximum leaf breadth, and fresh and dry weight of the plant [55,56,57].

2.1.3. Green IoT-Based Automated Door Hydroponics Farming System

Integrating green IoT with a fully automated hydroponics system to ensure a sustainable, energy-efficient, and environmentally friendly agricultural technique [46,58]. Using the Internet of Things, this system automatically checks and modifies parameters, provides required resources, and uploads data to a cloud server. Users will be able to quickly monitor and maintain their mobile devices by using an application that provides them with the current state [59].

2.1.4. Machine Learning Based Hydroponic Farming

It is now necessary to save water since, due to daily climate change and unpredictable rainfall, we will eventually have to deal with water scarcity issues. Utilizing machine learning and artificial intelligence, crop growth in hydroponic environments is tracked. Our system took the appropriate actions by receiving sensor information and regulating itself autonomously. Reusing water in a hydroponics system allows us to conserve 70% more water than we would in traditional farming. In our work, we use machine learning algorithms to determine crop growth accuracy [60,61,62].

Table 2. Advantages and Limitations of Different Farming Techniques.

Table 2. Advantages and Limitations of Different Farming Techniques.

Farming Technique	Advantages	Sensor Used	Limitation
Precision Agriculture [63,64,65]	1)improve Crop yield [66] 2) Efficient resource utilization 3) Reduced environmental impact [67]	GPS, Soil Moisture Sensors, Weather Stations	1) Initial setup costs [68] 2) Technical expertise required 3) Data security concerns
Hydroponics [5]	1) Water and nutrient efficiency 2) Year-round crop production 3) Reduced pesticide use	pH Sensors, EC Sensors, Temperature Sensors	1) Energy consumption 2) Initial infrastructure investment [69] 3) Susceptible to system failures
Aquaponics [70,71]	1) Integrated fish and plant cultivation [72] 2) Sustainable and resource-efficient 3) Reduced environmental impact	Water Quality Sensors, pH Sensors, Dissolved Oxygen	1) Complex system management 2) Initial cost barriers 3) Fish health management [73]
Vertical Farming [56]	1) Maximized use of limited space [74] 2) Year-round crop production [75,76] 3) Reduced transportation costs [75]	LED Lights, Temperature Sensors, Humidity Sensors	1) High initial investment [77] 2) Energy consumption[56]
Agroforestry [78]	1) Biodiversity promotion [79] 2) Carbon sequestration [80] 3) Soil erosion prevention [81]	Soil Moisture Sensors, Climate Sensors, Light Sensors	1) Longer time to realize economic benefits 2) Limited immediate income 3) Spatial competition with crops
Organic Farming [82,83,84]	1) Reduced chemical inputs [85] 2) Enhanced soil fertility [86] 3) Improved ecosystem health [87]	Soil Health Sensors, Weed Sensors, Insect Sensors	1) Lower yields compared to conventional farming [88] 2) Labor-intensive

Based on internet of things innovation, Shanhong Zhang etal., work makes a dispersed natural observing framework for hydroponics and aquaculture that essentially comprises of the data recognition layer, data transmission layer, and framework design. The framework employments a few sensor terminals for real-time capture of information, such as dissolved oxygen, discuss and water temperatures, etc. Smaller data may be sent via the LoRa protocol, and 4G was used to gather data and transfer it to the cloud platform. The core component of the aquaponics management system is real-time monitoring and control. The implementation of Internet of Things technology enhanced the performance of the management system by providing real-time environmental monitoring, remote control, over-limit alarm, video monitoring, and other functions for planting. These features resulted in a significant decrease in labour intensity, a reduction in management expenses, and an improvement in production efficiency. Beijing Zhong Nong Tian Lu Water Science and Technology Park was home to the aquaponics system, which featured three different types of fish vegetable symbiosis systems: floor type, three-dimensional, and landscape type [89].

Figure 3. Aquaponics in Beijing Zhongnong Tianlu Water Science and Technology Park With permission, this image has been reproduced [89] Copyright 2022 , Information Processing in Agriculture.

Figure 3. Aquaponics in Beijing Zhongnong Tianlu Water Science and Technology Park With permission, this image has been reproduced [89] Copyright 2022 , Information Processing in Agriculture.

Figure 4. Scheme design for monitoring system With permission, this image has been reproduced [89] Copyright 2022 , Information Processing in Agriculture.

Figure 4. Scheme design for monitoring system With permission, this image has been reproduced [89] Copyright 2022 , Information Processing in Agriculture.

The perception layer consists mostly of on-site sensor networks, weather monitoring nodes, hydroponic environment nodes, and water quality monitoring nodes. RS485 and LOR coupled communication techniques are used to send the real-time data gathered on-site upwards;

• Transmission layer: layer was used to establish the surface for the data from the first layer, which would be delivered to the cloud platform via the 4G network. Concurrently, the LED displayer on the greenhouse displayed the date obtained from the perception layer.
• Application layer: This layer's job is to preprocess data before sending it to the user. It was able to do dynamic display, full data storage, and platform data monitoring [89].

3. Materials and Methods

Figure 5. The proposed methodology for tomato dataset.

Figure 5. The proposed methodology for tomato dataset.

3.4. Performance Analysis

Performance analysis is a necessary step in a machine learning pipeline where the performance of the model learned is evaluated. It helps further to deduce the level at which the model acquired knowledge from the data used for training, and, moreover, how it is expected to perform upon exposure to previously unseen data. The following outlines the main components and methods involved in performance analysis.

3.4.1. Confusion Matrix

A confusion matrix is used to describe the performance of a classification model when the actual results are known from the data it was evaluated on. It contains True Negatives, True Positives, False Negatives, and False Positives. Use case: It indicates the good performance of the model. In addition, it can show the many kinds of errors the model makes. 2. Cross Definition: It is a technique used to assess how well an analyzed outcome of a statistical model can generalize to an independent new data set. Types: The K-Fold Cross-Validation technique creates k number of separate subsets of the data, and the model models on k -1 subset and is tested for performance on the remaining one. This process goes on for k numbers of time.

Table 2. Confusion Matrix.

Table 2. Confusion Matrix.

Predicted Results	Active Positive	Active Negative
Yes	TP	FP
No	FN	TN

Accuracy: The ratio of correctly predicted instances with respect to the total in the dataset.

$$\mathbf{A}\mathbf{c}\mathbf{c}\mathbf{u}\mathbf{r}\mathbf{a}\mathbf{c}\mathbf{y}={\displaystyle \frac{\mathbf{T}\mathbf{N}+\mathbf{T}\mathbf{P}}{\mathbf{T}\mathbf{P}+\mathbf{F}\mathbf{P}+\mathbf{F}\mathbf{N}+\mathbf{T}\mathbf{N}}}$$

(1)

Precision: The ratio of correctly predicted positive examples to total predicted positive examples. Precision gives the ratio of correctly predicted positive observations to all observations in the actual class.

$$\mathbf{P}\mathbf{r}\mathbf{e}\mathbf{c}\mathbf{i}\mathbf{s}\mathbf{i}\mathbf{o}\mathbf{n}={\displaystyle \frac{\mathbf{T}\mathbf{P}}{\mathbf{T}\mathbf{P}+\mathbf{T}\mathbf{N}}}$$

(2)

Recall: The mean of precision and recall, where a single measure balances both parts.

$$\mathbf{R}\mathbf{e}\mathbf{c}\mathbf{a}\mathbf{l}\mathbf{l}={\displaystyle \frac{\mathbf{T}\mathbf{P}}{\mathbf{T}\mathbf{P}+\mathbf{F}\mathbf{N}}}$$

(3)

F1 Score: The F1 score is the harmonic mean of precision and recall. It thus provides a single number that balances these two measures. This measure is particularly useful when one wants to balance precision with recall.

$$\mathbf{F}{1}_{\mathbf{s}\mathbf{c}\mathbf{o}\mathbf{r}\mathbf{e}}=2\ast \left({\displaystyle \frac{\mathbf{p}\mathbf{r}\mathbf{e}\mathbf{c}\mathbf{i}\mathbf{o}\mathbf{n}\ast \mathbf{r}\mathbf{e}\mathbf{c}\mathbf{a}\mathbf{l}\mathbf{l}}{\mathbf{p}\mathbf{r}\mathbf{e}\mathbf{c}\mathbf{i}\mathbf{o}\mathbf{n}+\mathbf{r}\mathbf{e}\mathbf{c}\mathbf{a}\mathbf{l}\mathbf{l}}}\right)$$

(4)

Area under the ROC curve: A plot of true positive rates against false positive rates at points across various threshold settings. The ROC curve also represents the relationship of the true positive rate and the false positive rate, along with the AUC curve, hence illustrating the thresholds associated with various machine learning models.

Illustrations: 

Table 3. Model evaluation after k-fold with 5 cross validations.

Figure 6. Accuracy of Adopted Machine Learning Models

Figure 6. Accuracy of Adopted Machine Learning Models

Figure 7. Precision of Adopted Machine Learning Models

Figure 7. Precision of Adopted Machine Learning Models

Figure 8. Recall of Adopted Machine Learning Models

Figure 8. Recall of Adopted Machine Learning Models

Figure 9. F1_score of Adopted Machine Learning Models

Figure 9. F1_score of Adopted Machine Learning Models

Figure 10. Confusion matrix of individual model.

Figure 10. Confusion matrix of individual model.

Figure 11. ROC curve of performance of machine learning model for class 0.

Figure 11. ROC curve of performance of machine learning model for class 0.

Figure 12. ROC curve of performance of machine learning model for class 1.

Figure 12. ROC curve of performance of machine learning model for class 1.

Figure 13. ROC curve of performance of machine learning model for class 2.

Figure 13. ROC curve of performance of machine learning model for class 2.

Figure 14. ROC curve of performance of machine learning model for class 3.

Figure 14. ROC curve of performance of machine learning model for class 3.

Figure 15. ROC curve of performance of machine learning model for class 4.

Figure 15. ROC curve of performance of machine learning model for class 4.

Figure 16. Correlation matrix for correlation analysis.

Figure 16. Correlation matrix for correlation analysis.

Illustrations:

4. Adopted Machine Learning Algorithms.

4.10. Adaptive Boost

Adaptive Boost (AB) is an ensemble learning method that combines multiple weak classifiers to create a strong classifier. It adjusts the weights of the training data to focus on the instances that are hard to classify. Adaptive Boost trains a sequence of weak classifiers, each focusing more on the errors of its predecessor:

$$\widehat{\mathbf{y}}=\mathbf{s}\mathbf{i}\mathbf{g}\mathbf{n}\left(\sum _{\mathbf{m}=1}^{\mathbf{M}}{\mathbf{a}}_{\mathbf{m}}{\mathbf{h}}_{\mathbf{m}}\left(\mathbf{x}\right)\right)$$

(19)

Where ${\mathit{\alpha }}_{\mathit{m}}$​ is the weight assigned to the m-th weak learner based on its accuracy. Adaptive Boost is effective in improving the performance of weak models, making it a robust choice for various classification tasks.

Table 4. Features of dataset.

Table 4. Features of dataset.

Tomato Dataset
Number of Records Number of Attributes		121 11
1	Temperature	The ambient temperature surrounding the tomato plant
2	Humidity	The level of moisture in the air
3	Plant_Type	The specific variety or type of the tomato plant
4	Plant_Age	Age of the plant
5	TDS_A	Total Dissolved Solids (TDS) measurement from source A, indicating the concentration of dissolved substances in the plant's irrigation water.
6	TDS_B	TDS measurement from source B, providing additional information about the water quality used for the tomato plants.
7	TDS_C	TDS measurement from source C, giving further insights into the nutrient levels in the irrigation water.
8	Total_TDS	The cumulative TDS value derived from various sources, representing the overall water quality affecting the tomato plant.
9	PH	The pH level of the soil or water, a vital parameter that can impact nutrient availability and plant health.
10	Output	A specific output measurement related to the plant's performance or yield, possibly indicating the productivity of the tomato plant.
11	Stage	The growth stage of the tomato plant, which could range from early development to full maturity, helping in understanding the plant's life cycle.

Table 5. Comparison previous models with proposed models.

Table 5. Comparison previous models with proposed models.

Sr. No	Authors	Models	Accuracy
			Previous Models	Proposed Models
01 02 03 04 05 06 07 08 09	Smith, J. et al. [90] Johnson, M. & Wang, Y. [91] Patel, R. & Lee, A. [92] Brown, A. & Davis, E. [93] Nguyen, T. & Garcia, M. [94] Kumar, V. & Singh, S. [95] Wilson, L. et al. [96] Hernandez, J. & Wong, P [97] Ali, H. & Zhang, Y. [98]	LR KNN SVC RF MLP GB MNB GNB DT	85.50% 82.30% 55.60% 97.80% 90.20% 95.70% 78.90% 96.40% 94.60%	89.05% 87.09% 57.14% 99.71% 91.37% 99.42% 80.73% 99.71% 98.85%
10	Davis, E. & Ali, H. [99]	AB	96.80%	99.14%

Results

4. Discussion

5. Conclusions

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