Deep learning has profoundly impacted agriculture by enhancing the accuracy and efficiency of plant classification tasks. In particular, advanced models have significantly improved the ability to classify various plant species based on their visual features. This study focuses on classifying alfalfa plant varieties using deep learning techniques. We created a custom dataset comprising 1,214 images of three alfalfa varieties (Bilensoy-80, Diana and Nimet) cultivated under controlled conditions. Our comparative study involved several state-of-the-art models, including MobileNetV3, InceptionV3, Xception, VGG19, DenseNet121, ResNet101, and EfficientNetB3, to assess their performance in classifying these alfalfa varieties. We evaluated these models with various configurations: learning rates ranging from 0.1 to 0.000001, batch sizes of 8, 16, 32, and 64, and using dropout with a decay rate of 0.96 and decay steps of 1000. The results revealed that models trained with transfer learning generally achieved higher test accuracies. For instance, DenseNet121 achieved a test accuracy of 0.9945 when trained from scratch and 1.0000 with transfer learning, while EfficientNetB3 achieved a test accuracy of 0.9945 with both methods. The findings underscore the effectiveness of transfer learning in enhancing model performance for plant classification tasks. • The study introduced a unique dataset consisting of 1214 images of three alfalfa varieties (Bilensoy-80, Diana, and Nimet) cultivated under controlled conditions, providing a valuable resource for advancing plant classification research. • The research compared the performance of several state-of-the-art deep learning models (MobileNetV3, InceptionV3, Xception, VGG19, DenseNet121, ResNet101, and EfficientNetB3) with various hyperparameter configurations, demonstrating the effectiveness of different architectures for classifying alfalfa plant varieties. • The study highlighted the superior performance of transfer learning in plant classification tasks, with models like DenseNet121 and EfficientNetB3 achieving near-perfect accuracy, underscoring its potential to significantly enhance model accuracy and efficiency in agricultural applications.

Rising atmospheric CO₂ levels drive greenhouse effects, elevating temperatures, and diminishing water accessibility in semi-arid regions, affecting agriculture. Alfalfa contributes to climate change mitigation by sequestering carbon, enhancing soil fertility and carbon storage, reducing synthetic nitrogen fertilizer use, preventing soil erosion, supplying high-quality livestock feed, and serving as a bioenergy source. This research examined the effects of elevated CO₂ levels in climate change scenarios (600, 800, and 1000 ppm, with control at 400 ppm) on two alfalfa varieties, Medicago sativa cv. Nimet and Bilensoy-80. The experiments were conducted in specialized Climate Change Simulation Greenhouses, allowing control of CO₂, water, and temperature variables. Results revealed a positive relationship between higher CO₂ concentrations and increased photosynthesis (P ≤ 0.001), promoting the plant growth leaf area (P ≤ 0.001), yields and both leaf (P ≤ 0.05) and stem dry biomass (P ≤ 0.001). At 1000 ppm CO₂, a saturation point was reached, halting further photosynthesis. This down-regulation was linked to decreased intercellular CO₂ levels, which expedited chlorophyll and breakdown and potentially induced leaf senescence. High CO₂ levels led to greater biomass, as anticipated. However, total protein levels, a forage quality indicator, initially decreased with high CO₂ concentrations (up to 1000 ppm) due to an inverse relationship with shoot yield. Surprisingly, the 1000 ppm CO₂ concentration mitigated this protein reduction in both alfalfa varieties.