Cluster-based segmentation for tobacco plant detection and classification

Abstract

Tobacco is one of the major economical crops in the agriculture sector. It is essential to detect tobacco plants using unmanned aerial vehicle (UAV) images for improved crop yield and plays an important role in the early treatment of tobacco plants. The proposed research work is carried out in three phases: In the first phase, we collect images from UAV’s and apply the French Commision Internationale de l'eclairage (CIE) L*a*b colour space model as pre-processing operations and segmentation. And then two prominent motion descriptors namely histogram of flow (HOF) and motion boundary histogram (MBH) are combined with the optimal histogram of oriented gradients (HOG) descriptor for exploring optimal motion trajectory and spatial measurements. And finally, the spatial variations with respect to the scale and illumination changes are incorporated using the optimal HOG descriptor. Here both dense motion patterns and HOG are refined using hierarchical feature selection using principal component analysis (PCA). The proposed model is trained and evaluated on different tobacco UAV image datasets and done a comparative analysis of different machine learning (ML) algorithms. The proposed model achieves good performance with 95% accuracy and 92% of sensitivity.

Using transfer learning-based plant disease classification and detection for sustainable agriculture

“…They are critical in medical, defense, agriculture, remote sensing, and business analysis applications. Digital image processing methods simulate human visual capabilities, providing automatic monitoring, disease management, and water management [ 17 ].…”

“…While a considerable number of studies availed some plant disease classification and detection models, there are notable deficiencies in these studies [ 4 , 15 , 17 , 20 ], including training on limited dataset size leading to model overfitting and generalization complexity to diverse environments. Training models under controlled backgrounds and environmental conditions, in contrast to the natural setting that makes these models impractical in the natural environment, the accuracy and robustness of models.…”

Smart irrigation with crop recommendation using machine learning approach

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