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- 1. SEMINAR PRESENTATION ON CROPMONITORING USING MACHINE LEARNING ALGORITHM
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- 3. Project Motivation For instance,in a crop monitoring project, emphasize the importance of sustainable agriculture, increasing crop yields, and reducing resource wastage. Relevance to Industry and Society: Describe how advancements in crop monitoring impact farmers, food security, and environmental sustainability. Need for Precision Agriculture: Rising food demands, resource limitations, and environmental concerns Importance of Crop Monitoring: Timely information on crop health improves yield, quality, and resource use.
- 4. Introduction to crop monitoring Cropmonitoring is the practice of observing and tracking various parameters related to crop health, growth, and environmental conditions to support effective farming decisions. By collecting real-time data on aspects such as soil moisture, plant health, temperature, and pest activity, farmers can identify issues early, optimize resource use, and improve crop yields.
- 5. Literature Survey Author Year Focus area Techniqu es/ Methods Used FindingsLimitatio ns Yang et al. 2019 Plant disease classification SVM, Random Forest High accuracy in identifying resistance genes and classifying diseases. Limited to specific pathogens; needs broader crop data. Jawade et al. 2020 Real-time disease prediction RF Regression Model Predicted disease outbreak probabilities using weather data. Limited real- time integration with IoT. Mohanty et al. 2016 Image-based disease detection Deep Convolution al Neural Network (DCNN) Achieved 99.35% accuracy in disease classification . Focused on leaf images; lacks real- time IoT integration.
- 6. Literature Survey Author Year Focus area Techniqu es/ Methods Used FindingsLimitation Kumar et al. 2020 Yield prediction and fertilizer use Decision Tree, Random Forest High accuracy in predicting yield and recommending fertilizers. Relies on historical data; lacks IoT- enabled real-time monitoring. Mishra et al. 2021 Automation in disease prediction IoT, Decision Tree Automated growth monitoring and disease prediction using real- time weather data. Limited scalability to diverse crops and larger agricultural areas.
- 7. Problem statement The primarychallenge in smart farming is the lack of a comprehensive, scalable, and cost-effective crop monitoring solution that utilizes both IoT and machine learning. Traditional crop monitoring methods are inadequate for providing real-time insights, which can lead to delayed interventions, increased costs, and suboptimal crop yields. Need for an effective and adaptable ML-based system for real-time crop monitoring.
- 8. Aim and objective AIM VE Developand apply machine learning algorithms to enhance crop monitoring accuracy and efficiency • Implement real-time data collection via IoT and sensors. • Build ML models for disease detection, yield prediction, and irrigation management. • Test and validate the model in field or simulated environment
- 9. Methodology Data collectionsthrough various sensors and stores in a data base During this phase, the heterogeneous data collected from different sensors were filtered and made ready for feature selection The process of decreasing no. of input variable(s) during the development process of a predictive model is said to be as feature selection. Its primary goal is to minimize the number of input variables in order to reduce overall modeling costs, and in some cases, it is used to improve performance Most likely disease will be predicted for the selected crops, and necessary precaution may be taken to avoid major financial loss to the farmer
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- 11. Advantages & Disadvantages Advantages • Precisionand accuracy in crop health assessment. • Reduced resource use and cost savings. • Improved crop yield predictions Disadvantages • High setup costs for IoT and ML infrastructure. • Requires expertise for algorithm training. • Data quality and connectivity issues.
- 12. Applications Applicatio ns Disease Detection: Earlyidentification of pests or diseases from crop images. Yield Prediction: Forecasting crop output based on environmental data. Soil Monitoring: Tracking soil health parameters like pH, moisture, and nutrients. Water Management: Optimizing irrigation by predicting soil moisture needs.
- 13. Conclusion Machine learning isa useful tool in today’s world for analyzing massive amounts of data and producing more accurate results and predictions. Our research demonstrated and provided accurate results for the five crops that were chosen as a sample for yield prediction. Hence, we were able to confirm that machine learning algorithms may also be used to predict various illnesses impacting crops over multiple seasons and across a variety of crops because our training set and testing data are practically identical. To achieve the best level of classification accuracy, careful selection of preprocessing data methodologies and machine learning technologies is required. As a result, more machine learning-based technologies are required to predict various sorts of illnesses impacting diverse. SVM beats the other two methods, with Gram’s training accuracy of 96.29% and testing accuracy of 95.67% as prediction is concerned.
- 14. References • Sharma, B.,& Yadav, S. (2020). Predict Crop Production in India Using Machine Learning Techniques: A Survey. 8th International Conference on Reliability, Infocom Technologies, and Optimization. IEEE, 101–106. • Aggarwal, N., & Singh, D. (2021). Technology-Assisted Farming: Implications of IoT and AI. IOP Conference Series: Materials Science and Engineering, 1022 (1), 1-10. Ramcharan, A., et al. (2017). Deep Learning for Image-Based Cassava Disease Detection. Frontiers in Plant Science, 8(1852), 1-12. • Mishra, D., & Deepa, D. (2021). Automation and Integration of Growth Monitoring in Plants (with Disease Prediction) and Crop Prediction. Materials Today: Proceedings, 43, 3922–3927. • This list includes key studies and articles referenced for the report, ensuring credibility and acknowledgment of previous research.
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