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Introduction: Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions. Methods: Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiTB5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation. Results: The results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general. Discussion: The findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration. [ABSTRACT FROM AUTHOR]

The current study aimed to design and test the accuracy of a variable-rate control metering mechanism (VRCMM) and a remote smart monitoring system (RSMS) for a precision sugarcane transplanter based on IoT technology. The VRCMM is used to operate the seedling metering device at different speeds using a stepper motor based on the travel speed, and the RSMS was employed to evaluate of the three basic parameters of seedling amount, optimum rate, and missed rate. Two types of sensors were used for detecting the sugarcane seedling (SS) and travel speed, including one ultrasonic sensor and one infrared RPM sensor. The study was performed at five travel speeds and four transplant spacings. The findings of laboratory tests showed that the mean record of the relative error between the desired stepper motor speed of the VRCMM and the real value was 3.39%, and it increased with increasing the travel speed. as Additionally, the speed regulation performance was in agreement with the transplanting index. The change in RSMS accuracy is obvious when the travel speed is high and the transplant spacing is small. The RSMS accuracy drops sharply, revealing a leaping change. In conclusion, the smart and intelligent designed sugarcane transplanter would be very useful in sugarcane production. [ABSTRACT FROM AUTHOR]

Accurately estimating crop performance under various fertilizer levels in a rapid and non-destructive manner has become a vital aspect of precision agriculture technology for both economic and environmental benefits. This study aimed to estimate different sugar beet parameters, such as total chlorophyll (Chlt), chlorophyll a (Chla), chlorophyll b (Chlb), root yield (RY), sugar yield (SY), and sugar content (SC) under five nitrogen (N) levels (0, 30, 60, 90, and 120 kg N ha−1). This was achieved by using a combination of the gradient boosting regression (GBR) model with published and newly developed two- and three-band spectral indices (2D- and 3D-SRIs). The results showed that the N levels had the highest proportion of variations (80.4–92.9%) for all parameters, except for SC, which had more variation (59.9%) according to year than the N levels (37.2%). All parameters, except SC, showed a significant increase with gradually increasing N levels. Additionally, the N levels displayed linear and strong positive relationships with the chlorophyll parameters, and linear and strong negative relationships with SC, while these relationships were quadratic and strong with RY and SY. Several published and novel 3D-SRIs exhibited moderate to strong relationships (R2 = 0.65–0.89) with all parameters. The newly developed 3D-SRIs, which involve wavelengths from the visible, near-infrared, and red-edge regions, such as NDI536, 538, 534, NDI738, 750, 542, and NDI448, 734, 398, were effective in accurately estimating all parameters. Combining 2D-SRIs, 3D-SRIs, and the aggregate of all spectral indices (ASRIs) with GBR models could be a robust strategy for estimating the six observed parameters with reasonable precision. The GBR-ASF-6 SRIs and the GBR-ASF-7 SRIs models performed better in predicting Chl content and SC with R2 values of 0.99 and 0.99 (RMSE = 0.073 and 1.568) for the training dataset and R2 values of 0.65 and 0.78 (RMSE = 0.354 and 6.294) for the testing datasets, respectively. The obtained results concluded that published and newly developed 3D-SRIs, GBR based on 2D-SRIs or 3D-SRIs, and the aggregate of all ASRIs can be used in practice to accurately estimate the Chl content, production, and quality of sugar beet across a wide range of N levels under semiarid conditions. [ABSTRACT FROM AUTHOR]