The knowledge of crop water requirements is critical for agricultural water conservation, especially for accurate irrigation decision making in the greenhouse. Investigating the water demand pattern of the tomato in the solar greenhouse environment and constructing an appropriate irrigation decision-making model are urgently needed to improve irrigation water use efficiency. We designed four irrigation-level treatments: 100% ET0 (T1), 85% ET0 (T2), 70% ET0 (T3), and 55% ET0 (T4), and conducted a two-vegetation-season tomato planting trial under drip irrigation conditions in a solar greenhouse. The Pearson’s correlation coefficient method analyzed the intrinsic linkage and influence between soil–crop–environment and tomatoes’ water demand patterns. Indicators suitable for irrigation decision making in greenhouse tomatoes were selected, and regression functions were constructed for environmental and crop physiological parameters by combining path analysis and multiple regression methods. Finally, a fusion irrigation decision-making model was constructed by introducing a distance function in the Dempster–Shafer (D–S) theory primary probability assignment (BPA) synthesis algorithm and combining it with a triangular affiliation function. The results showed that: (1) the soil coefficient of variation was shallow > middle > deep, and tomatoes absorbed water mainly in the 0–60 cm soil layer; (2) the crop stem flow rate, net photosynthetic rate, and transpiration rate were positively correlated with irrigation water and had the highest correlation with net radiation, relative humidity, and relative humidity, with correlation coefficients of 0.9441, 0.9441, and 0.7679, respectively; (3) the constructed decision model had a significantly lower value of uncertainty than other methods, while the highest decision value could reach over 0.99, which achieved the best decision accuracy compared to other algorithms.
Owing to the requirements of a high yield and high-quality tomatoes, tomato grading is important—particularly for fruit morphology. Machine vision provides a fast and nondestructive manner to address this demand, and accuracy has become the focus of attention. In this study, the gamma correction method was used for preprocessing to enhance the surface reflection of tomatoes, and Otsu’s method was used to segment the A scalar diagram under the LAB color model. On this basis, two levels of exploration were conducted. First, new evaluation indices were proposed for different views. For the top view, two shape-evaluation indices were established: the area ratio of the maximum inscribed circle to the maximum circumscribed circle and the dispersion of the contour centroid distance (extreme value and coefficient of variation). For the side view, the difference between the maximum and minimum centroid distances in the contour was established as a shape index. Compared to with commonly used shape features, the shape-detection accuracy of the proposed indicators proposed was > 7% higher. Second, an evaluation method based on multi-view fusion was developed by combining the advantage indices for different views. The detection accuracy was 96%, which was 9.40–9.70% higher than those for the top and side views alone. The proposed evaluation method combining top views (dispersion of centroid distance) with side views (difference between maximum and minimum centroid distances) is effective for classifying tomatoes.
Owing to the requirements of a high yield and high-quality tomatoes, tomato grading is important—particularly for fruit morphology. Machine vision provides a fast and nondestructive manner to address this demand, and accuracy has become the focus of attention. In this study, the gamma correction method was used for preprocessing to enhance the surface reflection of tomatoes, and Otsu’s method was used to segment the A scalar diagram under the LAB color model. On this basis, two levels of exploration were conducted. First, new evaluation indices were proposed for different views. For the top view, two shape-evaluation indices were established: the area ratio of the maximum inscribed circle to the maximum circumscribed circle and the dispersion of the contour centroid distance (extreme value and coefficient of variation). For the side view, the difference between the maximum and minimum centroid distances in the contour was established as a shape index. Compared to with commonly used shape features, the shape-detection accuracy of the proposed indicators proposed was > 7% higher. Second, an evaluation method based on multi-view fusion was developed by combining the advantage indices for different views. The detection accuracy was 96%, which was 9.40–9.70% higher than those for the top and side views alone. The proposed evaluation method combining top views (dispersion of centroid distance) with side views (difference between maximum and minimum centroid distances) is effective for classifying tomatoes.
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