Next‐Gen Crop Monitoring: MTEG‐RTU Algorithm and UAV Synergy for Precise Disease Diagnosis

Abstract

The rapidly changing climatic scenarios are highly favorable for the rising diseases that lead to increasing threats to food production and supply. Various scholars and scientists make long steps to hasten the process of making innovations in farming for managing these issues. In this context, UAV is applied for the purpose of managing and monitoring plant health. The abiotic stresses available in plant diagnosis through traditional strategies are highly labor‐intensive and unfit for large‐scale deployment. Conversely, UAVs designed with mobile sensors, multispectral, radar, and so on make them flexible, affordable, and more effective. Thus, this study proposes a novel meta ensemble transfer extreme gradient‐based random tactical unit (MTEG‐RTU) algorithm for diagnosing crop illnesses precisely. The proposed MTEG‐RTU methodology entails three methods such as transfer learning, adaptive boost, and meta‐ensemble, and the hyper parameters are tuned using random tactical unit algorithm. Healthier and disordered crop images gained from the crop disease dataset comprise 8000 images and are preprocessed. The more optimal features from the preprocessed images are learned through the ResNet method, and these features enter into the classification phase. Random tactical unit algorithm enhanced the performance by optimizing the hyperparameters of MTEG classifier. The experimental results conducted based on the various assessment components and validation dataset indicate that the developed method outperformed the other chosen models, achieving precision, recall, and accuracy of 98.5%, 97.9%, and 98.6%, respectively. The other achievements made by the model are offering technical guidance for conducting the precise diagnosis and treatment of plant pathologies with less time of 9 s.