The ovules inside the ovary of a plant are the precursors of seeds and they are important for the perpetuation of the plants. The genus Passiflora L., produce fruits with numerous seeds and they have economic and medicinal value. The edible portion of the Passiflora are the seeds surrounded by pulp. Being the edible parts of a fruit, it is important to investigate the early development of ovules in Passiflora that lead to the formation of seeds after pollination. Wild relatives of the domesticated crops are increasingly being investigated for possible genetic resources that can be used for crop improvement programs. The present study was designed to investigate the comparative ovule development between a wild (Passiflora foetida L.) and a cultivated (Passiflora edulis Sims) species of Passiflora with an aim that it may provide important information about the common and diverging regulatory mechanisms during ovule development between the wild and the cultivated species. We also investigated the pollen morphology between the wild and cultivated species using light and scanning electron microscopy. Our results show that wild type P. foetida ovule growth is faster when compared with that of cultivated P. edulis. Furthermore, wild species harbour ovules of large size (0.14 mm²) but less in number (6) as compared to cultivated ones which show smaller size (0.05 mm²) of ovules but relatively more in number (21). The differences in ovary wall thickness were also stark between the two species. The ovary wall thickness was 0.10 mm in the wild type whereas it was 0.74 mm in cultivated species. Notable differences were also observed in diameter where the wild type (2.45 mm) reported smaller diameter than cultivated species (3.25 mm). We observed little difference in the pollen morphology between the two species.

Background: Agriculture is very important for human existence since time immemorial. Approximately sixty percent of the world’s population is dependent on agriculture and its allied activities. Due to plant diseases every year farmers bear heavy economic loss which can be reduced by having an early detection system for plant disease. Conventional plant disease detection techniques are laborious and based on chemical and analytical testing. In this work, we suggest a deep learning method to precisely detect plant diseases by applying the VGG19 convolutional neural network. Methods: Modifications specifically designed for the classification of plant diseases were applied to the VGG19 model. By first freezing the bottom layers of the pre-trained VGG19 model-which had been trained on the ImageNet dataset-and then fine-tuning the upper layers to better fit the PlantVillage dataset, transfer learning was used. Resizing, standardization and data augmentation are a few of the image preprocessing approaches that were used to increase the variety of the dataset and boost model performance. The efficacy of the model was assessed through the use of metrics like F1-score, recall, accuracy and precision. Result: The constructed model performed well in the classification of plant diseases, with over 95% accuracy on the test set. The success of the model in generalizing across different plant disease categories was largely attributed to the application of transfer learning and data augmentation. The findings show that deep learning techniques-in particular, the use of VGG19-can significantly enhance agricultural practices and decision-making processes by helping to quickly and accurately identify plant illnesses. VGG9-based image processing offers an accurate and automated solution for plant disease identification, achieving 98% accuracy in classifying healthy and diseased leaves. By integrating mobile applications, drones and smart farming cameras, farmers can detect diseases early and take timely action, improving crop health and yield. Future advancements in dataset expansion and real-time processing will further enhance its effectiveness in precision agriculture.