Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a ...non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.
Removing seedlings from plug-trays to transplant in the field poses transplanting shocks to the seedlings and may reduce the survival rate. Therefore, this study designed biodegradable plug-tray ...cutting mechanism (SPCM) that separates seedlings with plug-cells from plug-trays and eliminates a complex clamping mechanism. SPCM consists of three sub-mechanisms that align the plug-cell at the seedling discharge point to cut and separate the plug-cell from the plug-tray, allowing the seedling to fall into the transplanting hopper. The SPCM separated around 82% of the plug-cell and delivered it to the planting unit. Furthermore, the SPCM-equipped transplanter achieved a transplanting performance of 74% with pepper and cabbage seedlings, with an average field efficiency of 68%, field capacity of 0.032-0.035 ha h-1 and required 73% less labour than manual seedling transplanting. The transplanting performance was satisfactory, with most pepper seedlings (85%) transplanted with a planting angle less than 10°, and 7% of cabbage seedlings were inclined and had sufficient planting depth of 48 mm for cabbage and 53 mm for pepper. In conclusion, the SPCM is a step towards sustainable and efficient vegetable seedling transplanting. Increasing efficiency, planting accuracy, and sustainability present exciting opportunities for further research and development in the field.
Intensively grown strawberries in a greenhouse require frequent and precise soil physicochemical constituents for optimal production. Strawberry leaf color analyses are the most effective way to ...evaluate soil status and protect against excess environmental nutrients and financial setbacks. Meanwhile, precision agriculture (PA) endorsements have been utilized to mimic solutions to these problems. This research aimed to create machine learning models such as multiple linear regression (MLR) and gradient boost regression (GBR) for simulating strawberry leaf color changes related to soil physicochemical components and plant age using RGB (red, green, and blue) mean values. The soil physicochemical properties of the largest varied colored leaves of strawberry were precisely measured by a multifunctional soil sensor from the rooting zones. Simultaneously, 400 strawberry leaflets were detached in each vegetative and reproductive stage, and individual leaves were captured using a digital imaging system. The RGB mean values of colored images were extracted using the image segmentation algorithms of image processing technique. Consequently, MLR and GBR models were developed to predict leaf RGB mean values based on soil physicochemical measurements and plant age. The GBR model vigorously fitted with RGB mean values throughout the growth stage, with R2 and RMSE values of (R = 0.77, 7.16, G = 0.72, 7.37, and B = 0.70, 5.68), respectively. Furthermore, the MLR model performed moderately with R2 and RMSE values of (R = 0.67, 8.59, G = 0.57, 9.12, and B = 0.56, 6.81) when consecutively predicting RGB mean values in strawberry leaves. Eventually, the GBR model performed more effectively than the MLR model with high-performance metrics. In addition, the leaf color model uses visualization technology to measure growth progress, and it performs well in predicting dynamic changes in strawberry leaf color.
The purpose of this study was to determine the optimal operating speeds for a modified linkage cum hopper type planting unit that was used in low-speed automated vegetable transplanters. The ...transplanter utilizes a biodegradable seedling plug-tray feeding mechanism. The movement of the planter unit was simulated at different operating conditions using kinematic simulation software, and the resulting trajectories were compared based on factors such as plant spacing, soil intrusion area, soil intrusion perimeter, and horizontal displacement of the hopper in soil and found optimal result at 200, 250 and 300 mm/s and 40, 50 and 60 rpm combinations. The optimal operating speeds were then tested in a soil bin facility and found to perform well when transplanting pepper seedlings, with measured plant spacing that was close to the theoretical spacing. The planting depth in each case was not significantly different and the planting angle in different speed combinations was found to be significantly different, but within permissible limits. The mulch film damage was low for the selected optimised speed combinations. This study resulted in the determination of the optimal speeds for the transplanter, which can be used as a basis for optimising the other mechanisms within the transplanter.
Accurate classification of strawberry ripeness is a crucial aspect of ensuring high-quality food products, optimizing harvesting and storage processes, and promoting consumer health. Although several ...non-destructive computer vision-based systems have been developed for this purpose, the influence of different colour spaces on machine-learning model performance during the ripeness stage classification of strawberries remains underexplored. In this context, three machine-learning models, namely Gaussian Naïve Bayes (GNB), support vector machine (SVM) and feed-forward artificial neural networks (FANN), were combined with four colour spaces (RGB, HLS, CIELab and YCbCr) and biometrical characteristics to evaluate the effectiveness of colour spaces on the performance of machine-learning models for classifying strawberry ripeness. For this purpose, 1210 samples were collected and manually classified into four ripeness stages. A dataset was created by combining each colour space value, biometrical properties, and corresponding ripeness stage, which was used as inputs to the models. The results indicated that FANN with CIELab colour space achieved the highest accuracy of 96.7%, followed by GNB and SVM, both having equal accuracy of 95.46% in CIELab colour space. The least accuracy of 92.15% was observed in RGB colour space with the GNB classifier. In this study, the unripe and over-ripe stages were more accurately classified, while intermediate ripening stages proved to be more challenging for the models. Furthermore, the accuracy of models was observed to be influenced by both the colour space and classification model selected. Additionally, further research is needed to investigate other features that could improve the performance of models for strawberry ripeness classification.
Timely monitoring of fruit weight is a paramount concern for the improvement of productivity and quality in strawberry cultivation. Therefore, the present study was conducted to introduce a simple ...non-destructive technique with machine learning models in measuring fruit weight of strawberries. Nine hundred samples from three strawberry cultivars, i.e., Seolhyang, Maehyang, and Santa (300 samples in each cultivar), in six different ripening stages were randomly collected for determining length, diameter, and weight of each fruit. Pixel numbers of each captured fruit’s image were calculated using image processing techniques. A simple linear-based regression (LR) and a nonlinear regression, i.e., support vector regression (SVR) models were developed by using pixel numbers as input parameter in modeling fruit weight. Findings of the study showed that the LR model performed slightly better than the SVR model in estimating fruit weight. The LR model could explain the relationship between the pixel numbers and fruit weight with a maximum of 96.3% and 89.6% in the training and the testing stages, respectively. This new method is promising non-destructive, time-saving, and cost-effective for regularly monitoring fruit weight. Hereafter, more strawberry samples from various cultivars might need to be examined for the improvement of model performance in estimating fruit weight.
The disadvantageous properties of plastic and plastic wastes have resulted in biodegradable products and seedling pots gaining popularity. Agents of different strengths and sizes agents are usually ...mixed in the paper pulp to enhance the strength of paper-based seedlings pots. In this study, three types of paper-based seedling pots, with 0%, 3% and 5% of additives, named N0, N3, and N5, respectively, were tested to determine their physical, mechanical and biodegradation properties. Water absorption test results showed that the absorption rate was higher in N0, followed by N3 and N5; a similar pattern was observed in the maximum water absorption, thickness and solubility tests. The tensile test showed the highest strength in N3 (3.9 MPa), followed by N0 (3.8 MPa) and N5 (3.1 MPa) at 0% moisture absorption. However, at 100% moisture absorption, tensile strength dropped the most for N0 (82%), followed by N3 (67%) and N5 (65%). Hybrid broccoli seeds germinated inside the plant factory showed that 95% germinated within 13 days. Temporal data showed that germination time was most delayed in N5. No significant difference was found in seedling height; however, a significant difference was found in the root to shoot height ratio. N0 showed maximum weight and tensile strength loss on the biodegradation test, followed by N3 and N5. At the end of the fourth week, the tensile strength of N0, N3 and N5 was found to be 0.25 MPa, 0.69 MPa and 0.79 MPa, respectively, which was reduced by 94%, 81%, and 79%, respectively, compared to their initial strength. In conclusion, pots containing water repellent additives showed different properties than those without additives, except for germination and seedling growth. This experiment confirms that using additives will increase the strength of paper-based seedling pots in wet conditions without affecting the germination and growth of seedlings.
Monitoring the energy inputs and outputs in pig production systems is crucial for identifying potential imbalances and promoting energy efficiency. Therefore, the objective of this study was to ...measure the energy input, output, and losses during the growing–finishing phase of pigs from 1 September to 1 December 2023. A Livestock Environment Management System (LEMS) was used to measure the temperature, humidity, airflow, and water consumption levels inside the barn, and a load cell was used to measure the body weight of pigs. Furthermore, a bomb calorimetric test was conducted to measure the energy content of pigs’ manure. While calculating energy balance in the experimental barn, it was found that energy from feed and water contributed approximately 81% of the total input energy, while the remaining 19% of energy came from electrical energy. Regarding output energy, manure, and body weight accounted for about 69%, while around 31% was lost due to pig activities, maintaining barn temperature and airflow, and illuminating the barn. In conclusion, this study suggested methods to calculate energy balance in pig barns, offering valuable insights for pig farmers to enhance their understanding of input and output energy in pig production.
•Factors influencing drinking water intake in swine buildings are not fully understood.•Monitoring and evaluating drinking water are crucial for farm profitability.•Statistical and machine learning ...models were developed for drinking water estimation.•The accuracy of the random forest model was higher than other tested models.•Body mass and feed intake mostly influence the drinking water intake in pigs.
Effective monitoring and management of drinking water in swine buildings is a crucial aspect for promoting pigs' health and productivity. Therefore, this study aimed to quantify and model drinking water intake (DWI) in growing-finishing pigs by providing them with three concentrated diets in experimental pig barns. Two independent experiments were conducted in three experimental barns between 2021 and 2022. One statistical (multiple linear regression) and four machine learning algorithms (elastic net, random forest regression, support vector regression, and multilayer perceptron) were employed, with feed intake (FI), mass of pigs (MP), pigs' body temperature (PBT), room temperature (RT), CO2 concentration (RCO2), and temperature-humidity index (RTHI) as input parameters. The results revealed that pigs with a body mass of 30 to 60 kg consumed approximately 3.58 L of drinking water and 2.10 kg of concentrated diet per day. Additionally, strong positive correlations were observed between MP, FI, and DWI (correlation coefficient (r) > 90) during both experimental periods. The findings indicated that the random forest regression algorithm performed the best, explaining over 90% and 80% of the observed and predicted data during the training and testing phases, respectively. However, during the testing phase, the multiple linear regression methods performed the worst (R2 < 0.79 and RMSE > 0.89 L pig−1 day−1) when compared to the other models. Sensitivity analysis indicated that among all the variables, MP had the greatest impact on predicting DWI, followed by FI, RCO2, RTHI, and RT. The study concluded that random forest regression could predict DWI precisely, which can assist pig farmers in enhancing their water monitoring capabilities and promptly assessing the availability of drinking water.
•Monitoring body composition is a crucial aspect for improving livestock management.•Conventional methods to determine animals' body composition have certain limitations.•Three machine learning ...models for predicting pigs' body composition were developed.•SVR outperformed MLR and RFR models in predicting fat mass and fat-free mass in pigs.•Combination of MP, FI and STP as parameters can predict body composition accurately.
Timely monitoring and precise estimation of body composition parameters, such as fat mass (FM) and fat-free mass (FFM), are crucial for pig production. Therefore, this study aimed to utilize three machine learning models, namely multiple linear regression (MLR), random forest regression (RFR), and support vector regression (SVR), to predict FM and FFM in growing-finishing pigs using four input combinations of three variables, i.e., mass of pigs, feed intake, and surface temperature of pigs. An ultrasound-based back-fat depth measurement approach was used to determine FM and FFM, and these measurements were compared with reference measurements obtained from slaughtered pigs. Data from two experimental periods in 2021 and 2022 were used for training and testing these models. Performance metrics, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the models' performance and stability. The results showed that the SVR model had the highest accuracy in predicting FM and FFM, with the ability to explain the relationship between input and target variables up to 94.4% in FM and 94.6% in FFM prediction. Additionally, the SVR model consistently outperformed the RFR and MLR models in predicting FM, with an increase in R2 of up to 6.72% and 27.96%, respectively, and a reduction in RMSE of up to 24.06% and 36.82%, respectively, across different input combinations. Similar results were obtained in FFM prediction, where the SVR model showed an increase in R2 of up to 6.47% and 22.45%, and a reduction in RMSE of up to 23.96% and 36.57% compared to RFR and MLR models, respectively. Moreover, the SVR model demonstrated the highest stability, with only 2.9% to 3.3% decrease in R2 during the testing phase compared to the training phase, while the RFR model exhibited the worst stability. Findings of the present study suggested that the SVR model was the most stable and reliable, along with the ultrasound-based back-fat depth approach for measuring FM and FFM in growing-finishing pigs. This approach could aid in monitoring meat quality and providing a rapid overview of body composition for pig farmers.
