Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural network (CNN) requires high computational resources, limiting its portability. In this study, a lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models. The models were trained, validated, and tested using tomato leaf disease datasets split into an 8:1:1 ratio. The efficacy of the various attention modules in plant disease classification was compared in terms of the performance and computational complexity of the models. The performance of the models was evaluated using the standard classification accuracy metrics (precision, recall, and F1 score). The results showed that CNN with attention mechanism improved the interclass precision and recall, thus increasing the overall accuracy (>1.1%). Moreover, the lightweight model significantly reduced network parameters (~16 times) and complexity (~23 times) compared to the standard ResNet50 model. However, amongst the proposed lightweight models, the model with attention mechanism nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy. Although all the attention modules enhanced the performance of CNN, the convolutional block attention module (CBAM) was the best (average accuracy 99.69%), followed by the self-attention (SA) mechanism (average accuracy 99.34%).

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Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve production. In recent years, image-based studies have become an efficient solution in various fields such as navigation for unmanned vehicles, human–machine-based systems, agricultural surveying, livestock, etc. So far, several studies have been conducted to identify, track, and classify the behaviors of pigs and achieve early detection of disease, using 2D/3D cameras. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors and presents automated approaches for the monitoring and investigation of pigs’ feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors.

Solar renewable energy (SRE) applications are substantial in eradicating the rising global energy shortages and reversing the approaching environmental apocalypse. Hence, effective solar irradiance forecasting models are crucial in utilizing SRE efficiently. This paper introduces a partially amended hybrid model (PAHM) by the implementation of a new algorithm. The algorithm innovatively utilizes bi-directional gated unit (Bi-GRU), autoregressive integrated moving average (ARIMA) and naive decomposition models to predict solar irradiance in 5-min and 60-min intervals. Meanwhile, the models’ generalizability strengths would be tested under an 11-fold cross-validation and are further classified according to their computational costs. The dataset consists of 32 months’ solar irradiance and weather conditions records. A fundamental result of this study was that the single models (Bi-GRU and ARIMA) outperformed the hybrid models (PAHM, classical hybrid model) in the 5-min predictions, negating the assumptions that hybrid models oust single models in every time interval. PAHM provided the highest accuracy level in the 60-min predictions and improved the accuracy levels of the classical hybrid model by 5%, on average. The single models were rigorous under the 11-fold cross-validation, performing well with different datasets; although the computational efficiency of the Bi-GRU model was, by far, the best among the models.

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The optimal production of strawberries requires the essential nutrients and favourable media for vegetative and reproductive growth. The present study sought to determine the effectiveness of growth parameters and fruit yield of strawberries in different media growing under a greenhouse. To analyze the significant effect for the growth and fruit yield among the growing media, four treatments such as control soil (CS), bio plus compost (T 1 ), the combination of bio plus compost, and synthetic nutrient applied media/integrated media (T 2 ) and synthetic nutrient applied soil media (T 3 ) were assayed. Morphology parameters like plant height, canopy area, fresh weight, dry weight of roots were measured in each stage after eight weeks and sixteen weeks and yield attributing parameter as the number of fruits set per plant and number of fruits per plant were measured at the beginning and end of the reproductive stage eight and sixteen weeks respectively. The effects of growing media for the strawberry plant growth and productivity were analyzed using completely randomized block designs through analyzing the variance with a significance level of p < 0.05. The canopy area of the strawberry plants was calculated using the image processing technique applied in HSV colour space. Correspondingly, the vegetative stage and reproductive stage of T 2 plants attained the maximum plant height of 16.93 AE 0.31 cm and 19.34 AE 0.21 cm, canopy area with 23.02 AE 1.94 cm 2 and 28.78 AE 0.93 cm 2 , fresh weight of 18.00 AE 3.06 g, and 20.15 AE 3.49 g, dry weight of 5.15 AE 1.26 g and 6.66 AE 2.34 g and the number of fruits set per plant 18.83 AE 2.64 and number of fruits per plant 24.17 AE 2.14 followed by T 1 , T 3, and CS respectively. A comparison of the relative growth and fruit yield at the vegetative and reproductive phases of plants T 2 implied better performance. This study demonstrated that bio plus compost with synthetic nutrients act as a better source for the growth and production of strawberries under the greenhouse.

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