Machine learning (ML) is increasingly being used to improve agricultural practices, by enabling farmers and agriculture professionals to optimize operations, increase productivity, and reduce waste. The use of ML in agriculture can include tasks such as crop yield prediction, pest and disease detection, precision agriculture, and livestock management. By leveraging advanced algorithms and data analytics, ML is helping to revolutionize the agricultural sector, improving the efficiency and sustainability of food production. This is implemented using an optimization algorithm. Random Forest is a machine learning algorithm that can be used to enhance productivity in agriculture by analyzing large amounts of data and providing valuable insights into crop production. The algorithm works by creating multiple decision trees, each of which independently analyzes a subset of the data [21]. The results of these trees are then combined to make a more accurate prediction. Certainly, the following five categories are employed in agriculture to boost production using machine learning as shown below in Fig. 2:
A brief overview of machine learning:
The development of models to mimic human learning and decision-making can be credited with giving rise to machine learning in the 1940s and 1950s. The creation of the perceptron algorithm by Frank Rosenblatt in 1957 is one of the early instances of machine learning. A particular kind of artificial neural network known as a perceptron was capable of learning to divide inputs into two categories using a set of weights and biases. The development of decision trees and rule-based systems in the 1960s and 1970s contributed to the advancement of machine learning research [22]. These techniques were used to create expert systems that can decide based on a set of guidelines and information. Machine learning began concentrating on statistical techniques and algorithms in the 1980s and 1990s, like logistic regression and linear regression. Classification, regression, and clustering challenges all used these techniques. Machine learning regained prominence in the 2000s with the rise of big data and improvements in computing power [23]. Numerous new methods, including deep learning and reinforcement learning, have been created and used in a variety of fields, such as autonomous cars, natural language processing, and picture identification. Machine learning is still a discipline that is quickly developing today, with new methods and programs being created and used in sectors including healthcare, finance, and cyber security.
Following are the steps that machine learning uses to operate:
1. Data Gathering: Gathering pertinent data from diverse sources is the initial stage in machine learning. Depending on the issue at hand and the desired solution, this data may be presented in either structured or unstructured form.
2. Data processing: After the data has been gathered, it must be prepared for analysis. To prepare the data for use by machine learning algorithms, the data must be cleaned, missing values must be removed, and any outliers must be removed.
3. Feature extraction: The process of selecting and engineering relevant features from the data aims to provide new features that may be more advantageous for machine learning algorithms.
4. Model Selection: After the data is ready, the type of problem being tackled is taken into consideration and a suitable machine learning technique is selected. supervised learning, unsupervised learning, and reinforcement learning are some examples of this.
5. Training the Model: Using the prepared data, the selected machine learning method is then practiced. To reduce the error between the projected output and the actual output, the algorithm makes adjustments to its parameters as it learns from the data during training.
6. Model Evaluation: After the model has been trained, its generalizability to fresh, unexplored data is assessed using a variety of performance criteria.
7. Model tuning: Depending on the results of the evaluation, the performance of the model may need to be improved by modifying its hyper parameters or by employing a new algorithm.
8. Model deployment: The model can be deployed in a production environment to make predictions on new data after it has been trained and tweaked.
9. Monitoring and Maintenance: To ensure that machine learning models continue to perform well over time, it is necessary to monitor and maintain them. This entails keeping an eye out for data changes, frequently retraining the model, and making adjustments as necessary.
Machine learning is an iterative process, thus when the model is created and applied, some steps might need to be repeated or improved. Depending on the type of problem being handled, numerous metrics can be used to assess how well machine learning models perform. The main objective of assessing a machine learning model's performance is to find out how effectively it generalizes to new, untried data. Random Forest is a machine learning algorithm that can be used in agriculture to predict crop yield. It works by creating an ensemble of decision trees, where each tree is trained on a random subset of the data and a random subset of the features [24]. In the context of agriculture crop yielding, the algorithm can be used to predict the yield of a particular crop based on a variety of input variables such as weather patterns, soil quality, seed varieties, and management practices. To use the algorithm for crop yield prediction, historical data on crop yields and the corresponding input variables are used to train the model. Once the model is trained, it can be used to predict the yield of a new crop based on the input variables provided. The random forest algorithm is particularly useful for agriculture because it can handle a large number of input variables and can also capture complex interactions between the variables that may be difficult to identify using traditional statistical methods [25]. Additionally, the algorithm is robust to noisy data and missing values, making it suitable for real-world agricultural data. Overall, the random forest algorithm can be a powerful
tool for predicting crop yields in agriculture and can help farmers make informed decisions about crop management and resource allocation. Here are a few typical metrics for assessing machine learning models:
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Accuracy: This gauges the percentage of instances that were correctly classified out of all the instances. For classification issues, it is frequently employed.
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Precision is the percentage of all accurately identified positive instances (also known as true positives) among all positive predictions. It is beneficial in situations when the expense of false positives is large, like in medical diagnostics.
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Recall: In case you forgot, this quantifies the percentage of real positives among all positive results. It helps identify fraudulent transactions when the cost of false negatives is significant.
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F1 Score: When false positives and false negatives are equally essential, this score—which is the harmonic mean of precision and recall—is helpful.
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Mean Squared Error (MSE): For regression issues, this statistic is frequently utilized. The average squared difference between the expected and actual values is what is measured.
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Since it is expressed in the same units as the target variable, the Root Mean Squared Error (RMSE), which is the square root of the MSE, is a more understandable statistic.
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R-squared (R2): This indicator shows how much of the target variable's variance the model can account for. A better fit is indicated by higher values, which range from 0 to 1.
Depending on the problem being solved, it is crucial to utilize the right evaluation criteria when assessing a machine learning model's performance. To make sure the model can generalize to new data, it's also critical to assess the model's performance on a different test set. The trade-off between model complexity and performance is also crucial to take into account because more complex models may overfit training data while underperforming on fresh data [26].
In general, machine learning models can be divided into three groups based on the kind of learning that they employ:
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Supervised Learning: In supervised learning, the target variable is known and the computer is trained on a labelled dataset. To make precise predictions on fresh, unforeseen data, the model must learn the link between the input features and the target variable.
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Unsupervised Learning: In unsupervised learning, the goal variable is unknown and the machine is trained on an unlabelled dataset. The model's objective is to identify structures, correlations, and patterns in the data without receiving any direct instruction or feedback.
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Reinforcement Learning: In reinforcement learning, a machine learns by doing things incorrectly, interacting with its environment, and receiving feedback in the form of rewards or punishments. The ideal policy, which maximizes the cumulative reward over time, is what the model seeks to learn. In robots, video games, and control systems, this kind of learning is frequently employed.
The following categories are used to enhance productivity in agricultural sectors evolving machine learning techniques:
Crop prediction: Choosing which crops to grow, when to plant them, and how to maximize their production all depend on crop prediction, which is a crucial component of agriculture. To find trends and create precise predictions, machine learning (ML) approaches have shown considerable promise in the field of crop forecasting. The gathering and pre-processing of data is the first stage in crop prediction using ML. This entails compiling information on many elements, such as weather patterns, soil quality, and the type of crop planted, that may have an impact on crop development [27]. The data is then pre-processed to clean out any errors or noise and to make sure that it is in a format that ML algorithms can use. The pre-processed data is then subjected to ML algorithms to find patterns and generate predictions. Several ML methods, such as decision trees, random forests, support vector machines (SVM), and artificial neural networks, can be applied to crop prediction. (ANN). Each algorithm has its advantages and disadvantages, and the best one to use depends on the particulars of the problem at hand. The ML algorithm can be used to generate predictions on new data after being trained on existing data. For instance, a farmer can input information on the weather, soil quality, and other parameters to predict the yield of a specific crop in a given season, and the ML algorithm will then do so [28]. The ability to maximize crop yield and minimize waste is one of the main advantages of utilizing ML for crop prediction. ML algorithms can detect the ideal circumstances for crop growth and suggest adjustments to planting plans, irrigation schedules, and other practices to maximize yield by analysing data on the elements that affect crop growth. The ability to lower the risk of crop failure for farmers is another advantage of utilizing ML for crop prediction. Farmers can take proactive steps to minimize or lessen the impact of crop failure by utilizing pest-resistant crops or modifying planting dates by forecasting the possibility of crop failure based on factors such as weather conditions, soil quality, and disease outbreaks. In conclusion, crop prediction using machine learning has the potential to completely transform the agricultural industry by empowering farmers to make data-driven decisions that maximize crop output and minimize risk [29]. To accurately anticipate crop production and assist farmers in choosing planting techniques, irrigation schedules, and other practices that influence crop growth, machine learning (ML) systems analyse vast volumes of data and look for trends. It is expected that ML technology will become more and more significant as it develops in agriculture and other sectors of the economy.
Plant disease detection: Farmers and the agriculture sector are very concerned about plant diseases. They can lower crop quality and result in considerable yield losses, which might result in financial losses. To reduce these losses, early plant disease detection and prevention are essential. To forecast plant diseases and provide prompt intervention to stop their spread, machine learning approaches can be helpful. Data collection is the first step in the machine learning process used to predict plant diseases. This entails gathering information on the plant species, its growth stage, the type of disease, and climatic factors including temperature, humidity, and rainfall. Sensors, drones, satellite photography, and human observation can all be used to gather data [30]. Once the data is collected, it is pre-processed to remove noise, handle missing values, and normalize the data. Feature engineering is then performed to extract relevant features from the data that can be used to train the machine learning model. The extracted features may include plant morphology, texture, color, and shape, as well as environmental factors such as temperature and humidity. Various machine learning algorithms can be used for plant disease prediction, including decision trees, random forests, support vector machines, and deep learning models such as convolutional neural networks. The choice of algorithm depends on the size and complexity of the dataset and the accuracy required for prediction. The trained model is then tested on a separate dataset to evaluate its accuracy and performance. This is done by splitting the data into training and testing sets, with the majority of the data used for training the model and a smaller subset used for testing. A map displaying the location and severity of the disease or an alert system notifying farmers when the sickness is found are just two examples of how the model's results can be displayed to the user. Using this knowledge, preventive steps can then be taken, such as applying fungicides or removing affected plants [31]. Machine learning- based plant disease prediction has several advantages over conventional disease detection techniques. By enabling early illness identification, it can lessen the need for expensive and time-consuming manual checks. Additionally, it can assist farmers in deciding when and where to apply fungicides and other
treatments, minimizing the amount of chemicals needed and the negative effects on the environment. In conclusion, predicting plant diseases using machine learning is a potential strategy that can help to lower crop losses and improve agricultural practices. The right machine learning algorithms must be chosen, together with careful data gathering, pre-processing, and feature engineering. Machine learning can deliver precise and timely predictions of plant illnesses with the correct methods and equipment, allowing farmers to take preventative measures to save their crops [32]. In conclusion, predicting plant diseases using machine learning is a potential strategy that can help to lower crop losses and improve agricultural practices. The right machine learning algorithms must be chosen, together with careful data gathering, pre- processing, and feature engineering. Utilizing the appropriate methods and resources, machine learning.
Weed detection: Weed detection using machine learning is a rapidly developing field that has the potential to revolutionize the way we manage agriculture. In traditional agriculture, weed management is time- consuming and labour-intensive, often requiring the use of herbicides, which can have negative environmental impacts. Machine learning-based weed detection offers an alternative solution, using computer vision and machine learning algorithms to identify and map weeds in real time. One of the key advantages of machine learning-based weed detection is its ability to process large amounts of data quickly and accurately. This is achieved by training machine learning algorithms on large datasets of images of crops and weeds [33]. These datasets are typically labelled to identify which pixels correspond to crops and which correspond to weeds, allowing the machine learning algorithm to learn to distinguish between the two. The training data is fed into a machine learning model, which then uses this data to create a model that can recognize patterns in new images. When new images of crops are input into the model, the machine learning algorithm can quickly identify which pixels correspond to weeds and which correspond to crops. This information can then be used to create a map of weed distribution across a field. Several challenges must be addressed when developing a machine learning-based weed detection system. One of the key challenges is ensuring that the system is robust to changes in lighting conditions and other environmental factors. This can be achieved by using a combination of image processing techniques and machine learning algorithms to pre-process images before they are input into the weed detection model [34]. Another challenge is ensuring that the system can generalize to new images that it has not seen before. This can be achieved by using a combination of data augmentation techniques and transfer learning, which involves using a pre-trained model as a starting point for training a new model on a smaller dataset. Several different types of machine learning algorithms can be used for weed detection, including convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees. Each of these algorithms has its strengths and weaknesses, and the choice of algorithm will depend on the specific requirements of the application [35]. In conclusion, weed detection using machine learning has the potential to revolutionize agriculture by providing a fast, accurate, and environmentally friendly alternative to traditional weed management techniques. While there are still several challenges that must be addressed, advances in computer vision and machine learning are making this technology increasingly accessible to farmers and researchers alike.
Crop yield prediction: Crop yield prediction is a crucial task in agriculture as it helps farmers and agricultural experts to make informed decisions about crop production, resource management, and market supply. Traditionally, crop yield prediction has been done using various statistical models and methods, but with the recent advancements in machine learning, it has become possible to predict crop yields more accurately using data-driven approaches. Machine learning has proven to be a valuable tool in agriculture and has the potential to revolutionize the industry by making it more efficient and sustainable. The main objective of crop yield prediction using machine learning is to develop models that can accurately estimate the yield of a particular crop in a specific area based on various factors such as weather conditions, soil properties, and agricultural practices. Machine learning models for crop yield prediction typically use large datasets of historical weather and crop data to train the model and make predictions for the future. One of the most common approaches for crop yield prediction is supervised learning, where the model is trained using labelled data, which includes both input features (such as temperature, rainfall, and soil type) and
the corresponding crop yield. Supervised learning algorithms such as regression, decision trees, and neural networks are commonly used for crop yield prediction [36]. These algorithms can learn the relationship between the input features and the output variable (crop yield) and make accurate predictions for new data. Another approach for crop yield prediction is unsupervised learning, where the model is trained on unlabelled data to identify patterns and relationships in the data. Clustering algorithms such as k-means clustering and hierarchical clustering are commonly used in unsupervised learning for crop yield prediction. These algorithms can group similar data points based on their characteristics and help identify areas with similar crop yield patterns. In recent years, deep learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to crop yield prediction. CNNs are particularly useful for analysing spatial data such as satellite images to identify crop types, growth stages, and yield potential [37]. RNNs are well-suited for time series data such as historical weather data, which can be used to predict future weather patterns and their impact on crop yields.
One of the key challenges in crop yield prediction using machine learning is the availability and quality of data. Historical weather and crop data may not be readily available or may be incomplete, making it difficult to train accurate models. Moreover, the data may be biased or outdated, leading to inaccurate predictions. Another challenge is the interpretation of the machine learning models, which can be complex and difficult to understand for non-experts. Therefore, it is important to have a clear understanding of the data and the machine learning models used for crop yield prediction. In conclusion, crop yield prediction using machine learning is a promising approach to improving the efficiency and sustainability of agriculture. With the right data and machine learning models, it is possible to accurately predict crop yields and make informed decisions about crop production and resource management. However, there are challenges in data availability, model interpretation, and practical implementation that need to be addressed to fully realize the potential of machine learning in agriculture.
Farm management: Farm management is a complex process that involves numerous tasks, such as planting, harvesting, pest control, and irrigation, among others. With the advent of machine learning and artificial intelligence, farmers can use technology to optimize their operations and improve their yield. Machine learning is a subset of artificial intelligence that enables computer systems to learn from data and make predictions or decisions based on that data. In the context of farm management, machine learning can be used to analyze various data sources, including weather patterns, soil conditions, and crop growth, among others. One way in which machine learning can be used in farm management is by analysing satellite imagery to monitor crop health and growth. By analyzing changes in vegetation density and color, machine learning algorithms can predict crop yields, identify areas of the farm that require attention, and provide recommendations for irrigation and fertilization [38]. Another application of machine learning in farm management is in predicting weather patterns and their impact on crop growth. By analyzing historical weather data and current weather patterns, machine learning algorithms can predict future weather patterns and provide farmers with recommendations on how to adjust their operations to minimize the impact of adverse weather conditions.
In addition, machine learning can be used to optimize irrigation and fertilization schedules by analyzing soil data and plant growth patterns. By analyzing the nutrient content of the soil and the growth rate of the plants, machine learning algorithms can provide farmers with recommendations on the optimal amount and timing of irrigation and fertilization. Machine learning can also be used to predict pest and disease outbreaks and provide farmers with recommendations on how to prevent or mitigate them [39]. By analyzing historical data on pest and disease outbreaks, as well as current weather and soil conditions, machine learning algorithms can predict the likelihood of an outbreak and provide farmers with recommendations on how to prevent or mitigate it. In conclusion, machine learning has the potential to revolutionize farm management by providing farmers with valuable insights and recommendations on how to optimize their operations and improve their yield. By analyzing various data sources, including weather patterns, soil conditions, and crop growth, machine learning algorithms can provide farmers with
recommendations on irrigation, fertilization, pest control, and other key aspects of farm management, enabling them to make more informed decisions and improve their bottom line [40]. There are several advantages of using machine learning in agriculture, some of which include:
Increased Efficiency: Machine learning algorithms can help farmers and other agriculture stakeholders optimize their operations by reducing the time and effort required to perform tasks such as crop monitoring, disease detection, and yield forecasting. This can help to increase overall efficiency and reduce costs.
Improved Crop Yields: Machine learning algorithms can help farmers predict crop yields by analyzing a wide range of data, including weather patterns, soil moisture, and historical crop yields. By predicting yields more accurately, farmers can make better decisions about when to plant and harvest crops, leading to increased yields.
Enhanced Resource Management: Machine learning algorithms can help farmers manage resources such as water, fertilizer, and pesticides more efficiently. By analyzing data on crop growth and soil conditions, these algorithms can optimize resource usage, reducing waste and maximizing productivity.
Early Disease Detection: Machine learning algorithms can analyze data from sensors and other sources to detect signs of plant diseases early. This allows farmers to take proactive measures to prevent the spread of disease, minimizing the impact on crop yields.
Precision Agriculture: Machine learning can help farmers to target specific areas of their fields that require more attention, allowing for more precise application of resources such as fertilizer and pesticides. This can reduce waste and improve overall crop health.
Overall, the use of machine learning in agriculture has the potential to revolutionize the way that crops are grown and managed, leading to more efficient, sustainable, and productive farming practices.