Collection

Implementing machine learning in plant protection

Submission status
Open
Open for submission from
30 April 2024
Submission deadline
Ongoing
Machine learning (ML) represents a valuable tool across various facets of plant protection, offering innovative solutions for monitoring, detection, and decision-making. In this collection of articles, we invite submissions that illustrate how machine learning can be applied to enhance plant protection: Disease Detection and Diagnosis: ML algorithms can analyze extensive datasets of plant images to identify early signs of diseases or infections. Image recognition models can pinpoint patterns and symptoms linked to specific plant diseases, enabling farmers to swiftly respond with targeted treatments or removal of affected plants. Pest Identification and Monitoring: ML techniques can develop models for identifying pests based on images or sensor data. Automated pest monitoring systems can detect and quantify pest populations, facilitating timely interventions. Drones equipped with ML algorithms can survey vast agricultural areas and pinpoint pest infestations. Predictive Modeling for Pest and Disease Outbreaks: ML models can assess historical data on weather patterns, crop health, and pest/disease occurrences to forecast future outbreaks. Predictive analytics can aid farmers in planning preventive measures and optimizing resource allocation. Precision Agriculture: ML algorithms can analyze diverse data sources like satellite imagery, soil moisture levels, and weather conditions to refine farming practices. Precision agriculture techniques guided by machine learning enable targeted resource applications (e.g., water, fertilizers, pesticides), reducing waste and environmental impact. Weed Detection and Management: ML models can differentiate between crops and weeds in images, allowing for precise herbicide application. Autonomous or semi-autonomous robotic systems with ML capabilities can target and eliminate weeds, thereby decreasing reliance on broad-spectrum herbicides. Optimizing Pesticide Application: ML algorithms can assess factors such as weather conditions, crop growth stages, and pest prevalence to optimize pesticide timing and dosage. This approach minimizes pesticide use while maximizing effectiveness. Disease Resistance Prediction and Breeding: ML can analyze genetic data to predict plant variety resistance to specific diseases, guiding efforts in plant breeding to develop crops with enhanced pest and disease resistance. Smart Sensor Networks: ML algorithms can process real-time data from sensor networks monitoring environmental conditions, crop health, and pest activity. Smart sensor networks offer early warnings and enable swift responses to changing conditions. Decision Support Systems: ML-driven decision support systems can integrate data from multiple sources to furnish farmers with actionable insights. We welcome submissions of ready-to-use developments. It is important that the efficiency of new algorithms is tested and compared using publicly available data following the FAIR principles. This ensures that the research outcomes are Findable, Accessible, Interoperable, and Reusable, which promotes transparency and reproducibility in scientific research. Submissions should include detailed descriptions of the methods used for testing and comparing algorithm efficiency, along with references to the specific publicly available datasets utilized.

Articles (7 in this collection)