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Modern agriculture heavily relies on Site-Specific Farm Management practices,necessitating accurate detection, localization, and quantification of crops andweeds in the field, which can be achieved using deep learning techniques. Inthis regard, crop and weed-specific binary segmentation models have shownpromise. However, uncontrolled field conditions limit their performance fromone field to the other. To improve semantic model generalization, existingmethods augment and synthesize agricultural data to account for uncontrolledfield conditions. However, given highly varied field conditions, these methodshave limitations. To overcome the challenges of model deterioration in suchconditions, we propose utilizing data specific to other crops and weeds for ourspecific target problem. To achieve this, we propose a novel ensembleframework. Our approach involves utilizing different crop and weed modelstrained on diverse datasets and employing a teacher-student configuration. Byusing homogeneous stacking of base models and a trainable meta-architecture tocombine their outputs, we achieve significant improvements for Canola crops andKochia weeds on unseen test data, surpassing the performance of single semanticsegmentation models. We identify the UNET meta-architecture as the mosteffective in this context. Finally, through ablation studies, we demonstrateand validate the effectiveness of our proposed model. We observe that includingbase models trained on other target crops and weeds can help generalize themodel to capture varied field conditions. Lastly, we propose two novel datasetswith varied conditions for comparisons.

Improved Crop and Weed Detection with Diverse Data Ensemble Learning in Agriculture