Optimal transfer learning based nutrient deficiency classification model in ridge gourd ( Luffa acutangula )

The efficient detection of nutrient deficiency and proper fertilizer for that deficiency becomes the critical challenges various farmers face. The family Cucurbitaceae includes members cultivated globally as a source of indigenous medicines, food, and fiber. Luffa acutangula (L.) Roxb, generally called Ridge gourd, belongs to the Cucurbitaceae family and is an annual herb originating in several areas of India, particularly in the coastal regions. Nutrient deficiency detection in ridge gourd is essential to improve crop productivity. In agricultural practises, the early identification and categorization of nutrient deficiencies in crops is essential for sustaining optimal growth and production. Addressing these nutrient deficiencies, we applied the Ring Toss Game Optimization with a Deep Transfer Learning-based Nutrient Deficiency Classification (RTGODTL-NDC) to Ridge Gourd ( Luffa acutangula ). This research proposes a new ring toss game optimization with a deep transfer learning-based nutrient deficiency classification (RTGODTL-NDC) method. The RTGODTL-NDC technique uses pre-processing, segmentation, feature extraction, hyperparameter tuning, and classification. The Gabor filter (GF) is mainly used for image pre-processing, and the Adam optimizer with SqueezeNet model is utilized for feature extraction. Finally, the RTGO algorithm with the deep hybrid learning (HDL) model is applied to classify nutrient deficiencies. The suggested framework has the potential to improve crop management practises by allowing for proactive and targeted interventions, which will result in improved agricultural health, production, and resource utilisation. The outcomes represented by the RTGODTL-NDC method have resulted in improved performance. For example, based on accuracy and specificity, the RTGODTL-NDC methodology rendered maximum accu_y of 97.16