Drought and excess ambient temperature intensify abiotic and biotic stresses on agriculture, threatening food security and economic stability. The development of climate-resilient crops is crucial for sustainable, efficient farming. This review highlights the role of multi-omics encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics in identifying genetic pathways for stress resilience. Advanced phenomics, using drones and hyperspectral imaging, can accelerate breeding programs by enabling high-throughput trait monitoring. Artificial intelligence (AI) and machine learning (ML) enhance these efforts by analyzing large-scale omics and phenotypic data, predicting stress tolerance traits, and optimizing breeding strategies. Additionally, plant-associated microbiomes contribute to stress tolerance and soil health through bioinoculants and synthetic microbial communities. Beyond agriculture, these advancements have broad societal, economic, and educational impacts. Climate-resilient crops can enhance food security, reduce hunger, and support vulnerable regions. AI-driven tools and precision agriculture empower farmers, improving livelihoods and equitable technology access. Educating teachers, students, and future generations fosters awareness and equips them to address climate challenges. Economically, these innovations reduce financial risks, stabilize markets, and promote long-term agricultural sustainability. These cutting-edge approaches can transform agriculture by integrating AI, multi-omics, and advanced phenotyping, ensuring a resilient and sustainable global food system amid climate change.

Precise canopy management is critical in vineyards for premium wine production because maximum crop load does not guarantee the best economic return for wine producers. The growers keep track of the number of grape bunches during the entire growing season for optimizing crop load per vine. Manual counting of grape bunches can be highly labor-intensive and error prone. Thus, an integrated, novel detection model, Swin-transformer-YOLOv5, was proposed for real-time wine grape bunch detection. The research was conducted on two varieties of Chardonnay and Merlot from July to September 2019. The performance of Swin-T-YOLOv5 was compared against commonly used detectors. All models were comprehensively tested under different conditions, including two weather conditions, two berry maturity stages, and three sunlight intensities. The proposed Swin-T-YOLOv5 outperformed others for grape bunch detection, with mean average precision (mAP) of up to 97% and F1-score of 0.89 on cloudy days. This mAP was ~44%, 18%, 14%, and 4% greater than Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5, respectively. Swin-T-YOLOv5 achieved an R² of 0.91 and RMSE of 2.4 (number of grape bunches) compared with the ground truth on Chardonnay. Swin-T-YOLOv5 can serve as a reliable digital tool to help growers perform precision canopy management in vineyards.

The application of remote sensing, which is non-destructive and cost-efficient, has been widely used in crop monitoring and management. This study used a built-in multispectral imager on a small unmanned aerial vehicle (UAV) to capture multispectral images in five different spectral bands (blue, green, red, red edge, and near-infrared), instead of satellite-captured data, to monitor soybean growth in a field. The field experiment was conducted in a soybean field at the Mississippi State University Experiment Station near Pontotoc, MS, USA. The experiment consisted of five cover crops (Cereal Rye, Vetch, Wheat, Mustard plus Cereal Rye, and native vegetation) planted in the winter and three fertilizer treatments (Fertilizer, Poultry Liter, and None) applied before planting the soybean. During the soybean growing season in 2022, eight UAV imaging flyovers were conducted, spread across the growth season. UAV image-derived vegetation indices (VIs) coupled with machine learning (ML) models were computed for characterizing soybean growth at different stages across the season. The aim of this study focuses on monitoring soybean growth to predict yield, using 14 VIs including CC (Canopy Cover), NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index 2), and others. Different machine learning algorithms including Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) are used for this purpose. The stage of the initial pod development was shown as having the best predictability for earliest soybean yield prediction. CC, NDVI, and NAVI (Normalized area vegetation index) were shown as the best VIs for yield prediction. The RMSE was found to be about 134.5 to 511.11 kg ha⁻¹ in the different yield models, whereas it was 605.26 to 685.96 kg ha⁻¹ in the cross-validated models. Due to the limited number of training and testing samples in the K-fold cross-validation, the models’ results changed to some extent. Nevertheless, the results of this study will be useful for the application of UAV remote sensing to provide information for soybean production and management. This study demonstrates that VIs coupled with ML models can be used in multistage soybean yield prediction at a farm scale, even with a limited number of training samples.

Fish and fishery products are among the most important sources of nutritional components for human health, including high-quality proteins, essential vitamins, minerals, and healthy polyunsaturated fatty acids. Fish farming and processing technologies are continuously evolving to improve and enhance the appearance, yield, and quality of fish and fish products from farm to fork throughout the fish supply chain, including growth, postharvest, treatment, storage, transportation, and distribution. Processing of fish involves a period of food withdrawal, collection and transportation, the process of stunning, bleeding, chilling, cutting, packaging, and byproduct recycling. Cutting is a set of crucial operations in fish processing to divide the whole fish into smaller pieces for producing fish products (e.g., fish fillets, steaks, etc.). Various techniques and machinery have been introduced in the field to advance and automate cutting operations. This review aims to provide a comprehensive review of fish cutting techniques, machine vision and artificial intelligence applications, and future directions in fish industries. This paper is expected to stimulate research on enhancing fish cutting yield, product diversity, safety and quality, as well as providing advanced solutions for engineering problems encountered in the fish industry.