The efficiency of photosynthesis in strawberry plants is measured to maintain the quality and quantity of strawberries produced. The latest method used to measure the photosynthetic status of plants is chlorophyll fluorescence imaging (CFI), which has the advantage of obtaining plant spatiotemporal data non-destructively. This study developed a CFI system to measure the maximum quantum efficiency of photochemistry (Fv/Fm). The main components of this system include a chamber for plants to adapt to dark environments, blue LED light sources to excite the chlorophyll in plants, and a monochrome camera with a lens filter attached to capture the emission spectra. In this study, 120 pots of strawberry plants were cultivated for 15 days and divided into four treatment groups: control, drought stress, heat stress, and a combination of drought and heat stress, resulting in Fv/Fm values of 0.802 ± 0.0036, 0.780 ± 0.0026, 0.768 ± 0.0023, and 0.749 ± 0.0099, respectively. A strong correlation was found between the developed system and a chlorophyll meter (r = 0.75). These results prove that the developed CFI system can accurately capture the spatial and temporal dynamics resulting from the response of strawberry plants to abiotic stresses.

Increasing food demands, global climatic variations, and population growth have spurred the growth of crop yield driven by plant phenotyping in the age of big data. High-throughput phenotyping of sorghum at each plant and organ level is vital in molecular plant breeding to increase crop yield. LiDAR (light detection and ranging) sensor provides 3D point clouds of plants with the advantages of high precision, high resolution, and rapid measurement. However, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3D point cloud. This study utilized four 3D point cloud-based deep learning models named PointNet, PointNet++, PointCNN, and dynamic graph CNN (DGCNN) for the specific objective of the segmentation of sorghum plants. Subsequently, phenotypic traits were extracted using the segmentation results. Study plants sample were grown under controlled conditions at various developmental stages. The extracted phenotypic traits outcome has been validated through the manually measured phenotypic traits of the sorghum plant. PointNet++ outperformed the other three deep learning models and provided the best segmentation result with a mean accuracy of 91.5%. The correlations of the six phenotypic traits, such as plant height, plant crown diameter, plant compactness, stem diameter, panicle length, and panicle width were calculated from the segmentation results of the PointNet++ model and the measured coefficient of determination (R ² ) were 0.97, 0.96, 0.94, 0.90, 0.95, and 0.88, respectively. The obtained results showed that LiDAR 3D point cloud have good potential to measure the sorghum plant phenotype traits rapidly and accurately using deep learning techniques.

Potatoes are a staple food crop consumed worldwide, with their significance extending from household kitchens to large-scale food processing industries. Their market value and quality are often compromised by various internal defects such as pythium, bruising, internal browning, hollow heart, gangrene, blackheart, internal sprouting, and dry rot. This study aimed to classify internal-based defects and investigate the quantification of internal defective areas in potatoes using visible and short-wavelength near-infrared spectroscopy. The acquisition of the spectral data of potato tubers was performed using a spectrometer with a wavelength range of 400–1100 nm. The classification of internal-based defects was performed using partial least squares discriminant analysis (PLS-DA), while the quantification of the internal defective area was based on partial least squares regression (PLSR). The PLS-DA double cross-validation accuracy for the distinction between non-defective and all internally defective potatoes was 90.78%. The double cross-validation classification accuracy achieved for pythium, bruising, and non-defective categories was 91.03%. The internal defective area model based on PLSR achieved a correlation coefficient of double cross-validation of 0.91 and a root mean square error of double cross-validation of 0.85 cm². This study makes a valuable contribution to advancing nondestructive techniques for evaluating internal defects in potatoes.

Fresh-cut vegetables are a food product susceptible to contamination by foreign materials (FMs). To detect a range of potential FMs in fresh-cut vegetables, a dual imaging technique (fluorescence and color imaging) with a simple and effective image processing algorithm in a user-friendly software interface was developed for a real-time inspection system. The inspection system consisted of feeding and sensing units, including two cameras positioned in parallel, illuminations (white LED and UV light), and a conveyor unit. A camera equipped with a long-pass filter was used to collect fluorescence images. Another camera collected color images of fresh-cut vegetables and FMs. The feeding unit fed FMs mixed with fresh-cut vegetables onto a conveyor belt. Two cameras synchronized programmatically in the software interface simultaneously collected fluorescence and color image samples based on the region of interest as they moved through the conveyor belt. Using simple image processing algorithms, FMs could be detected and depicted in two different image windows. The results demonstrated that the dual imaging technique can effectively detect potential FMs in two types of fresh-cut vegetables (cabbage and green onion), as indicated by the combined fluorescence and color imaging accuracy. The test results showed that the real-time inspection system could detect FMs measuring 0.5 mm in fresh-cut vegetables. The results showed that the combined detection accuracy of FMs in the cabbage (95.77%) sample was superior to that of green onion samples (87.89%). Therefore, the inspection system was more effective at detecting FMs in cabbage samples than in green onion samples.