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One of the most economical and precious spices ever known is saffron. It has innumerable pharmaceutical applications, and its cultivation is limited to certain topographic regions throughout the world. Although efforts are continuously being made to increase productivity and increase the area under cultivation, there are multiple factors leading to its low production. Corm size is one of the major factors affecting the quality and production of saffron. The more the corm size for a plantation, the better the yield of the crop. This can pose a serious threat to saffron cultivators who are not able to get good quality corms and sort quality corms from infected ones. Additional factors affecting saffron cultivation include attack by fungus leading to corm rots and sclerotineal disease. In this paper, IoT (Internet of Things) has been used to provide an energy-efficient, ready-to-use, reliable, and user-friendly solution for corm sorting and treatment before sowing to promote quality, better yield and minimized wastage due to corm rots. After sorting the corms, they are treated with 0.2% of tiabendazole fungicide solution and dried, before sowing and for long-term storage. Firstly, sorting and treatment system for the saffron corms processing as per weight is proposed. First two-layer cleaning mechanism to remove foreign substances, then three-step treatment has been applied to remove microorganisms. An automated conveyor system equipped with IoT devices was used for sorting and treating saffron corms. Different energy efficient LPWA (Low Power Wide Area) and LTE (Long Term Evolution) technologies used in the framework, for communication are NB-IoT (Narrow Band IoT) and LoRa (Long Range Radio).

The impact of deep learning (DL) is substantial across numerous domains, particularly in agriculture. Within this context, our study focuses on the classification of problematic soybean seeds. The dataset employed encompasses five distinct classes, totaling 5513 images. Our model, based on the InceptionV3 architecture, undergoes modification with the addition of five supplementary layers to enhance efficiency and performance. Techniques such as transfer learning, adaptive learning rate adjustment (to 0.001), and model checkpointing are integrated to optimize accuracy. During initial evaluation, the InceptionV3 model achieved 88.07% accuracy in training and 86.67% in validation. Subsequent implementation of model tuning strategies significantly improves performance. Augmenting the architecture with additional layers, including Average Pooling, Flatten, Dense, Dropout, and Softmax, plays a pivotal role in enhancing accuracy. Evaluation metrics, including precision, recall, and F1-score, underscore the model’s effectiveness. Precision ranges from 0.9706 to 1.0000, while recall values demonstrate a high capture rate across all classes. The F1-score, reflecting a balance between precision and recall, exhibits remarkable performance across all classes, with values ranging from 0.9851 to 1.0000. Comparative analysis with existing studies reveals competitive accuracy of 98.73% achieved by our proposed model. While variations exist in specific purposes and datasets among studies, our model showcases promising performance in soybean seed classification, contributing to advancements in agricultural technology for crop health assessment and management.