The rapid, stable, and undamaged picking of small-sized spherical fruits are one of the key technologies to improve the level of intelligent picking robots and reduce grading operations. Cherry tomatoes were selected as the research object in this work. Picking strategies of two-stage “Holding-Rotating” and finger-end grasping were determined. The end-effector was designed to separate the fruit from the stalk based on the linear motion of the constraint part and the rotating gripper. This work first studied the human hand-grasping of cherry tomatoes and designed the fingers with sinusoidal characteristics. The mathematical model of a single finger of the gripper was established. The structural parameters of the gripper were determined to meet the requirements of the grabbing range from 0 to 61.6 mm. Based on the simulation model, the constraint part was set to 6 speeds, and the fruit sizes were set to 20 mm, 30 mm, and 40 mm, respectively. When the speed was 0.08m/s, the results showed that the grabbing time was 0.5381 s, 0.387 s, and 0.2761 s, respectively, and the maximum grabbing force was 0.9717 N, 3.5077 N, and 4.0003 N now of clamping, respectively. It met the picking requirements of high speed and low loss. The criterions of two-index stability and undamaged were proposed, including the grasping index of the fixed value and the slip detection of variance to mean ratio. Therefore, the control strategy and algorithm based on two-stage and two-index for rapid, stable, and non-destructive harvesting of small fruit were proposed. The results of the picking experiment for seventy-two cherry tomatoes showed that the picking success rate was 95.82%, the average picking time was 4.86 s, the picking damage rate was 2.90%, the browning rate was 2.90% in 72 h, and the wrinkling rate was 1.49% in 72 h, which can meet the actual small spherical fruit picking requirements. The research will provide an idea for the flexible end-effectors with humanoid grasp function and provides a theoretical reference for small spherical fruit picking.

Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through combining internal and external leaf features. First, multi-source information obtained from tomato leaves of different disease grades was extracted by near-infrared hyperspectral imaging and THz time-domain spectroscopy, while the influence of low-frequency noise was removed by the Savitzky Golay (SG) smoothing algorithm. A genetic algorithm (GA) was used to optimize the selection of the characteristic near-infrared hyperspectral band. Principal component analysis (PCA) was employed to optimize the THz characteristic absorption spectra and power spectrum dimensions. Recognition models were developed for different grades of tomato leaf mildew infestation by incorporating near-infrared hyperspectral imaging, THz absorbance, and power spectra using the backpropagation neural network (BPNN), and the models had recognition rates of 95%, 96.67%, and 95%, respectively. Based on the near-infrared hyperspectral features, THz time-domain spectrum features, and classification model, the probability density of the posterior distribution of tomato leaf health parameter variables was recalculated by a Bayesian network model. Finally, a fusion diagnosis and health evaluation model of tomato leaf mildew with hyperspectral fusion THz was established, and the recognition rate of tomato leaf mildew samples reached 97.12%, which improved the recognition accuracy by 0.45% when compared with the single detection method, thereby achieving the accurate detection of facility diseases.

Substituting the toxic lead (Pb) in Pb halide perovskites by less harmful Sn is an attractive approach towards obtaining environment-friendly perovskite solar cells. However, complete substitution of Pb by Sn undermines the stability of perovskite materials and devices. Therefore, partial substitution of Pb by Sn has attracted much interest to balance environmental considerations and device performance. Here, we exploit the Sn ratio to optimize the band alignment in Pb–Sn mixed perovskite solar cells with an inverted structure. The band edge properties of Pb–Sn mixed triple cation perovskites (Cs/MA/FAPb_1−xSn_xX₃, X = Cl, Br, I) for efficient inverted solar cells are studied via simulation and experiment. The optimized solar cell with x = 0.336 shows the highest power conversion efficiency of 16.10% along with negligible hysteresis. We demonstrate that the devices are stable and maintain 93% of the initial PCE under ambient conditions for 30 days. These results suggest that bandgap engineering is a feasible approach to balance the efficiency and stability of Pb–Sn mixed PSCs.

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.

Recognition of weeds at asparagus fields using multi-feature fusion and backpropagation neural network

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.