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Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase. [ABSTRACT FROM AUTHOR]

With advancements in unmanned aerial vehicle (UAV) remote sensing technology, remote sensing images have emerged as a critical source of research data across various domains, including agriculture, forestry, and environmental research. UAVs fitted with diverse spectral sensors are capable of capturing diverse image modalities, presenting both challenges and opportunities for image semantic segmentation technology. Most existing semantic segmentation networks excel in processing images captured by visible light cameras and often fail to segment images captured by unmanned aerial vehicles under low-light conditions due to insufficient lighting, reduced visual clarity, high noise levels, and uneven illumination. Thermal infrared imaging sensors can capture thermal radiation information, which has the potential to improve segmentation accuracy when integrated with visible images. In this study, we introduce a novel semantic segmentation processing framework, which evaluates different fusing methods and fuses visible and thermal infrared images. The framework employs a lightweight deep learning model and is designed for accurate semantic segmentation on the fused images. Experiments are conducted on images collected in our unmanned aerial vehicle flight experiments and a public night-time dataset to assess the performance of our proposed approach. Experimental results show that our proposed framework achieves state-of-the-art performance in semantic segmentation tasks in low-light conditions. [ABSTRACT FROM AUTHOR]

Considering precision agriculture, recent technological developments have sparked the emergence of several new tools that can help to automate the agricultural process. For instance, accurately detecting and counting apples in orchards is essential for maximizing harvests and ensuring effective resource management. However, there are several intrinsic difficulties with traditional techniques for identifying and counting apples in orchards. To identify, recognize, and detect apples, apple target detection algorithms, such as YOLOv7, have shown a great deal of reflection and accuracy. But occlusions, electrical wiring, branches, and overlapping pose severe issues for precisely detecting apples. Thus, to overcome these issues and accurately recognize apples and find the depth of apples from drone-based videos in complicated backdrops, our proposed model combines a multi-head attention system with the YOLOv7 object identification framework. Furthermore, we provide the ByteTrack method for apple counting in real time, which guarantees effective monitoring of apples. To verify the efficacy of our suggested model, a thorough comparison assessment is performed with several current apple detection and counting techniques. The outcomes adequately proved the effectiveness of our strategy, which continuously surpassed competing methods to achieve exceptional accuracies of 0.92, 0.96, and 0.95 with respect to precision, recall, and F1 score, and a low MAPE of 0.027, respectively. [ABSTRACT FROM AUTHOR]