The crop recommendation system helps farmers for deciding which crop is suitable to their cultivation. It mainly depends on weather conditions, soil characteristics and other common factors.
Agriculture is the major scorching topics going under research so crop recommendation are more effectful to framers, this is an emerging topic that are going to hot now a days. As per scholars’ interest we have developed many crop recommendation thesis by using machine learning and we have explained below have a look at it. World class certified PhD professionals are working here so all your desired results will be achieved by us. Online guidance will be given for research proposal, thesis writing and all research activities.
This article acts a guide to create a crop recommendation system using machine learning. Let’s go through this!
- Objective Definition
The suitable crop is predicted by us that must depend on the parameters like temperature, soil content, humidity, pH, etc.
- Data Collection
We require the datasets which contain records of various crops that grown under different conditions. This datasets must include perfect features like pH level, rainfall, temperature, humidity, soil type and the resulting crop type. Datasets are extracted from agricultural research institutions or by exploring online platforms like kaggle.
- Data Pre-processing
- Handle Missing Values: The missing values are assigned by us or separate records with missing data.
- Feature Scaling: It contains similar scale, so the feature must be standardized.
- Feature Encoding: The conversion of categorical features into the numerical format by using one-hot encoding or label encoding. For example, Soil type.
- Feature Engineering
Integrating or converting the existed features to make new ones which provide us the better clarity in context. For example, calculation of “water requirement” attribute based on rainfall, humidity and temperature.
- Model selection and Training
- Decision Trees: This is simple and explainable that is appropriate for such applications.
- Random Forest: A group method is used by us to achieve higher accuracy.
- Gradient Boosting Machines (like XGBoost): These machines catch the difficult relationships in the data.
- Neural Networks: Neural networks are deployed, but it requires multiple data and fine-tuning.
- Evaluation
- Accuracy: It calculates the absolute predictions in percentage.
- Precision, Recall, F1-score: This is more efficient when the crops are minimalized in the dataset.
- Confusion Matrix: The confusion matrix offers an elaborated view of our model performance.
- Deployment
A web or mobile application is developed by us to help farmers to store the current conditions of their soil and environment. For cultivation, the system should retreat the suggested crops. With the help of some tools like Flask or FastAPI result in to create a web based interface. The tools such as Tensorflow Lite or ONNX make use of deploying models on mobile applications.
- Post-Deployment Monitoring :
Based on the user feedback, update the system frequently and modify the changes in agricultural research. We must retrain the model usually with fresh data and it adjusts and suits to the changing environmental conditions and farming practices.
Challenges
- Data Collection: The quality of data is more important and then depending on the training dataset, the system recommendation makes progress as better.
- Diverse Conditions: Chances of diversity in crops and various conditions, which is about the growing process of crops as make a challenging problem to us.
Extensions/Advanced Approaches:
- Integrate with IoT: We combine with IoT (Internet of Things) that offers the real time data from the fields.
- Incorporate External Factors: The factors include market demands, pest/disease outbreaks or market demand.
- Personalized Recommendations: Preferences, farmer’s historical data and economic conditions are consisted in this area.
Finally, maintaining collaboration with farmers and agriculture experts is more essential for the current trends and latest advancements. The experts and farmers field knowledge integrated with machine learning which results in highly effective recommendation systems.
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Crop Recommendation System Using Machine Learning Project Thesis Topics
Some of the best thesis topics that we have worked under Crop Recommendation System Using Machine Learning Project are as follows. Customised projects are also done by us or else we propose a topic based upon your interest and after your confirmation we create it and give a brief explanation.
- An Improved Machine Learning based Crop Recommendation System
Keywords
Agriculture, crop yield, soil, Crop, Machine Learning
Our study utilizes various ML approaches to recommend fertilizers and forecast the suitable crops that provides enormous number of productions. Several factors related to soil and crop leaves are offered by the farmers to predict the crops. A main objective of our study is to forecast the production rate by collecting, storing and examining of data. Our approach will assist the farmers to choose best suitable crops based on different criteria for their lands.
- Crop recommendation using machine learning algorithms
Keywords
Crop recommendation, Naive bayes, Decision tree, Random forest
A crop suggestion procedure is recommended in our paper by considering previously stored data of soil parameters through the utilization of ML techniques. From our research, we help the farmers to handle the efficiency of crops and minimize the issue of soil deterioration. We employed various ML techniques including random forest, NB, KNN, decision tree, Logistic regression to examine several factors like rainfall, pH, etc. for appropriate crop recommendation.
- Crop Recommendation System Using Fusion Model in Machine Learning
Keywords
Fusion model, weighted average, voting classifier, neural network, support vector machine
By utilizing fusion framework, an effective, more precise and minimum error rate crop suggestion model is constructed in our study. We proposed a suggestion model to forecast the appropriate crops by the farmers by considering weather and soil textures. Here, we employed weighted average classifier and voting classifier. We compared the classifiers to find the optimal one. From the analysis, weighted average classifier outperformed the other.
- IoT Based Smart Soil Fertilizer Monitoring and ML Based Crop Recommendation System
Keywords
Internet of Things (IoT), Wireless Sensor Networks (WSN), Potential of Hydrogen (pH)
An innovative IoT related soil nutrient tracking and crop suggestion model is proposed in our article to assist the farmers by recommending crop based information based on various factors related to soil and climatic conditions. We utilized sensors to gather the farm related data and send it through the WSN to cloud database. We tracked and examined various factors such as soil condition, temperature etc. to suggest the suitable crop through the use of ML techniques.
- An Innovative Method to Increase Agricultural Productivity using Machine Learning-based Crop Recommendation Systems
Keyword
Crop suggestion
A major goal of our project is to help the farmers by suggesting appropriate crops by considering various circumstances including rainfall, temperature etc. We carried out comparative analysis for several ML methods to find out the optimal approach for efficient crop suggestion. As a consequence, XGBoost approach offers greater outcomes. Then, our project established as a website that will demonstrate the capacity of ML techniques to assist farmers.
- A Crop Recommendation System Based on Nutrients and Environmental Factors Using Machine Learning Models and IoT
Keywords
Recommendation system, Sensors, Soil nutrient based
The ML related crop suggestion framework is recommended in our study for the farming fields and to help the farmers to enhance their productivity. Here we deployed sensor devices to gather the data and it also utilized to monitor the soil conditions and various nutrient contents. To discover the best method for crop suggestion, we utilized and compared various methods such as Artificial Neural Networks, Random Forest, Logistic Regression, and K-Nearest Neighbor.
- Smart Crop Recommendation System: A Machine Learning Approach for Precision Agriculture
Keywords
Classification, Precision Agriculture, Smart Crop Recommendation System
By employing ML methods, we developed a smart crop recommendation approach in our paper. To forecast the appropriate crops, we employed several ML techniques such as DT, SVM, KNN, LGBM, and RF. However, by considering different factors such as Nitrogen, Potassium, temperature etc, our suggested approach recommend the appropriate crops that is suitable for the farmer’s land. Results show that, RF achieved highest performance than others.
- AGROFERDURE: Intelligent Crop Recommendation System for Agriculture Crop Productivity Using Machine Learning Algorithm.
Keywords
Fertilizer Recommendation, XGBoost, Accuracy, Precision, Recall, F1 Score
Our work enhances the production rate with particular crops through the utilization of ML and AI methodologies. We suggested an efficient fertilizer and precise crop recommendations by gathering data from the farmers that comprises of soil conditions and its textures. We employed ML, Regression and Recommendation techniques for our proposed approach. We stored the gathered data for future utilization.
- Crop Recommendation using Machine Learning and Plant Disease Identification using CNN and Transfer-Learning Approach
Keywords
Plant disease, deep learning, Efficientnetv2
A main goal of our study is categorized into two. The first one is crop suggestion model and the other one is plant disease detection model and both are integrated into a single website. We extracted features to train several methods like LR, DT, SVM, MLP and RF for initial task. After that, we trained several CNN methods like VGG16, ResNet50 and EfficientNetV for the next task. From both the tasks, RF and EfficientNetV provide better end results.
- A crop recommendation system for better sustainability using machine learning techniques
Keywords
To predict the optimal crop for the farmer’s land by considering various soil conditions, we recommended ML approaches in our article. We employed various methods like LGBM and LR. We performed comparative analysis in terms of various metrics to find out the best suitable method for crop suggestion. We trained and examined the ML methods by utilizing a specific data. Our approach is integrated into Gradio interface that offers best crops to the farmers.
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