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In the current study, a modified solar dryer (SD) integrated automatic solar collector tracker (ASCT) was used for drying tomato fruit (TF) at three slice thicknesses of 4, 6, and 8 mm on both drying systems at three air speeds of 1, 1.5, and 2 m/s until reaching the equilibrium moisture content. Where the comparison study was conducted between the ASCT, and another SD integrated with a fixed solar collector (FSC). The obtained results of the current study showed that the maximum solar intensity and ambient air temperature during the test period were 900 W/m2, and 43.6 °C, respectively. As well as the highest efficiency of the PV system was 16.69% at the same time. On the other hand, the height, greatest diameter and smallest diameter of the TF used in the current study ranged between (4.6 and 5.2 cm), (3.5 and 4.2 cm), and (3.4 and 4.1 cm), respectively. As well as both the athematic and geometric diameters ranging between 3.87 and 4.47 cm and 2.26 and 2.38 cm, the sphericity values of tomatoes tend to have a round shape. In addition, the obtained results that there was no significant effect of the hot air velocities on the drying time, but the final moisture content (MC) decreased with increasing the hot air velocities. The lowest final MC was 6%, and it was recorded with a slice thickness of 4.0 mm that dried on SD integrated with ASCT. Additionally, color analysis showed that, the darkest tomato slices were dried on the SC integrated with FSC at a hot air velocity of 1.0 m/s. Meanwhile, the intensity of red and yellow colors significantly increased after drying with the SD integrated with ASDT. Furthermore, the chemical analysis of the dried tomato slices showed that the highest rehydration ratio of 4.43 kg water/kg dry matter was obtained for the dried tomato slices dried on SD integrated with ASCT at a hot air velocity of 1.5 m/s and a slice thickness of 8.0 mm. As well as the highest values of pH were monitored on the dried tomato slices on ASCT in comparison to the other tomato slices dried on FSC. Also, the highest ascorbic acid content recorded was 141 mg/100 g (d.b.) in tomato slices with an 8.0 mm thickness, dried using SD combined with FSC at an air velocity of 2 m/s. After drying, the total phenols content increased in all dried tomato samples but decreased with lower hot air velocities. [ABSTRACT FROM AUTHOR]

Maize leaf area offers valuable insights into physiological processes, playing a critical role in breeding and guiding agricultural practices. The Azure Kinect DK possesses the real-time capability to capture and analyze the spatial structural features of crops. However, its further application in maize leaf area measurement is constrained by RGB–depth misalignment and limited sensitivity to detailed organ-level features. This study proposed a novel approach to address and optimize the limitations of the Azure Kinect DK through the multimodal coupling of RGB-D data for enhanced organ-level crop phenotyping. To correct RGB–depth misalignment, a unified recalibration method was developed to ensure accurate alignment between RGB and depth data. Furthermore, a semantic information-guided depth inpainting method was proposed, designed to repair void and flying pixels commonly observed in Azure Kinect DK outputs. The semantic information was extracted using a joint YOLOv11-SAM2 model, which utilizes supervised object recognition prompts and advanced visual large models to achieve precise RGB image semantic parsing with minimal manual input. An efficient pixel filter-based depth inpainting algorithm was then designed to inpaint void and flying pixels and restore consistent, high-confidence depth values within semantic regions. A validation of this approach through leaf area measurements in practical maize field applications—challenged by a limited workspace, constrained viewpoints, and environmental variability—demonstrated near-laboratory precision, achieving an MAPE of 6.549%, RMSE of 4.114 cm2, MAE of 2.980 cm2, and R2 of 0.976 across 60 maize leaf samples. By focusing processing efforts on the image level rather than directly on 3D point clouds, this approach markedly enhanced both efficiency and accuracy with the sufficient utilization of the Azure Kinect DK, making it a promising solution for high-throughput 3D crop phenotyping. [ABSTRACT FROM AUTHOR]

Developing models to assess the nutrient status of plants at various growth stages is challenging due to the dynamic nature of plant development. Hence, this study encoded spatiotemporal information of plants within a single time-series model to precisely assess the nutrient status of aquaponically cultivated lettuce. In particular, the long short-term memory (LSTM) and deep autoencoder (DAE) approaches were combined to classify aquaponically grown lettuce plants according to their nutrient status. The proposed approach was validated using extensive sequential hyperspectral reflectance measurements acquired from lettuce leaves at different growth stages across the growing season. A DAE was used to extract distinct features from each sequential spectral dataset time step. These features were used as input to an LSTM model to classify lettuce grown across a gradient of nutrient levels. The results demonstrated that the LSTM outperformed the convolutional neural network (CNN) and multi-class support vector machine (MCSVM) approaches. Also, features selected by the DAE showed better performance compared to features extracted using both genetic algorithms (GAs) and sequential forward selection (SFS). The hybridization of deep autoencoder and long short-term memory (DAE-LSTM) obtained the highest overall classification accuracy of 94%. The suggested methodology presents a pathway to automating the process of nutrient status diagnosis throughout the entire plant life cycle, with the LSTM technique poised to assume a pivotal role in forthcoming time-series analyses for precision agriculture. [ABSTRACT FROM AUTHOR]

It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy mildew spores during the experiment was collected by a portable spore catcher, and the proportion of cucumber downy mildew leaf area to all cucumber leaf area was recorded, which was used as the incidence degree of cucumber plants. The environmental data in the greenhouse were monitored and recorded by the weather station in the greenhouse. Environmental data outside the greenhouse were monitored and recorded by a weather station in front of the greenhouse. Then, the influencing factors of cucumber downy mildew were analyzed based on the Pearson correlation coefficient method. The influencing factors of the cucumber downy mildew early warning model in greenhouse were identified. Finally, the CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) algorithm was used to establish the cucumber downy mildew incidence prediction model. The results showed that the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and determination coefficient (R2) of the CNN-LSTM network model were 0.069, 0.0098, 0.0991, and 0.9127, respectively. The maximum error between the predicted value and the true value for all test sets was 16.9398%. The minimum error between the predicted value and the true value for all test sets was 0.3413%. The average error between the predicted and true values for all test sets was 6.6478%. The Bland–Altman method was used to analyze the predicted and true values of the test set, and 95.65% of the test set data numbers were within the 95% consistency interval. This work can serve as a foundation for the creation of early prediction models of greenhouse crop airborne diseases. [ABSTRACT FROM AUTHOR]

Chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R 2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction ( R p 2 ) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest R p 2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems. [ABSTRACT FROM AUTHOR]

This study addresses the challenge posed by the small spore size of tomato gray mold, which hinders its identification and enumeration by conventional techniques. This work presents a novel approach for quantifying spore counts of tomato gray mold using diffraction imaging technology and image processing techniques. To construct a device for acquiring diffraction images of tomato gray mold spores, initially, the hyperspectral data pertaining to the gray mold spores of tomatoes was obtained. The characteristic wavelength of the light source of the diffraction image acquisition device was obtained by smoothing, principal component analysis, and comprehensive coefficient weight calculation. Then, the key parameters of the system were simulated, and the diffraction image acquisition device was built. Finally, tomato gray mold spores were counted based on angular spectrum reconstruction and image processing. The findings indicated that the combined contribution rate of the initial and secondary principal components of the original spectral data obtained from tomato gray mold spore samples amounted to 92.271%. The visible range of 435 nm, 475 nm, and 720 nm can be selected as the light source for tomato gray mold's spore diffraction imaging system. CMOS image sensor was installed 45 mm below the micropore with a diameter of 100/um, and the diffraction image obtained by simulation has a clear diffraction fingerprint. The diffraction imaging system can collect diffraction images of disease spores, and the collected diffraction images have clear diffraction fingerprints. The experimental error range was 5.13%-8.57%, and the average error was 6.42%. The error was within a 95% consistency. Therefore, this study can provide a research basis for the classification and recognition of greenhouse disease spores. [ABSTRACT FROM AUTHOR]

Timely detection and control of airborne disease is important to improve productivity. This study proposed a novel approach that utilizes micro polarization image features and a backpropagation neural network (BPNN) to classify and identify airborne disease spores in a greenhouse setting. Firstly, disease spores were collected in the greenhouse, and their surface morphological parameters were analyzed. Subsequently, the micropolarization imaging system for disease spores was established, and the micropolarization images of airborne disease spores from greenhouse crops were collected. Then the micropolarization images of airborne disease spores were processed, and the image features of polarization degree and polarization angle of disease spores were extracted. Finally, a disease spore classification model based on the BPNN was ultimately developed. The results showed that the texture position of the surface of the three disease spores was inconsistent, and the texture also showed an irregular shape. Texture information was present on the longitudinal and transverse axes, with the longitudinal axis exhibiting more uneven texture information. The polarization-degree images of the three disease spores exhibit variations in their representation within the entirety of the beam information. The disease spore polarization angle image exhibited the maximum levels of contrast and entropy when the Gabor filter's direction was set to n/15. The recognition accuracy of cucumber downy mildew spores, tomato gray mildew spores, and cucumber powdery mildew spores were 75.00%, 83.33%, and 96.67%, respectively. The average recognition accuracy of disease spores was 86.67% based on BPNN and micropolarization image features. This study can provide a novel method for the detection of plant disease spores in the greenhouse. [ABSTRACT FROM AUTHOR]