000			01737nam a22001577a 4500
082			_a629.892
100			_aFasihi, Sajeel _9119577
245			_aDeep Learning Methods for Disease Identification of Cotton Plants / _cSajeel Fasihi
264			_aIslamabad : _bSMME- NUST; _c2023.
300			_a79p. ; _bSoft Copy _c30cm.
520			_aCotton is a vital cash crop, contributing significantly to the global textile industry and the livelihoods of millions of farmers worldwide. However, diseases such as bacterial blight, leaf curl virus, and whitefly infestations pose a severe threat to cotton production and quality. Timely detection and accurate identification of these diseases are crucial for implementing effective control measures and ensuring crop health by exploring multiple state-of-the-art deep learning models, including CNNs and transformers. The research utilizes a diverse dataset of cotton plant images, encompassing healthy and diseased leaves, to train and fine-tune the deep learning models and Vision transformers. Additionally, we will focus on evaluating the models’ capability to detect varying intensities of whitefly infestations, which is critical for assessing disease severity and implementing appropriate control strategies. The models were cross-validated and regularized to improve the models working. This study has the potential to contribute significantly to the field of computer vision, particularly for cotton disease detection.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Karam Dad Kallu _9119537
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/37932
942			_2ddc _cTHE
999			_c607255 _d607255