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Intelligent Monitoring of Stress Induced by Water Deficiency in Plants using Deep Learning
Our model has achieved ceiling level classification performance of 98. 52% on JG-62 and 97. 78% on Pusa-372 chickpea plant data and has outperformed the best reported time-invariant technique by at least 14% for both JG-62 and Pusa-372 species, to the best of our knowledge.
Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds
Plant Phenomics can be used to monitor the health and the growth of plants.
Performance Evalution of 3D Keypoint Detectors and Descriptors for Plants Health Classification
Plant Phenomics based on imaging based techniques can be used to monitor the health and the diseases of plants and crops.
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