Robotic Crop Disease Monitoring Using Neural Network-Based Prediction and Weighted Path Planning

Disease control is paramount in modern agriculture to ensure optimal yield. Monitoring the spread of crop diseases is crucial for effective control measures. Traditional methods involve uniform pesticide spraying across entire fields, which can be inefficient and environmentally harmful. In this paper, we propose an intelligent solution employing mobile robots equipped with predictive AI techniques for disease monitoring and targeted intervention. These robots strategically visit select locations within the field, guided by a convolutional and recurrent neural network model trained on limited data to predict disease spread. We introduce a novel weighted path planning algorithm to optimize robot movement within the field considering disease risk and battery constraints. Our approach is implemented in the WaterBerry benchmark, an open-source platform for agricultural robotics. Experimental results demonstrate the efficacy of our technique, showcasing improved prediction accuracy and operational efficiency compared to baseline methods.