Manual fruit picking is labor-intensive and can damage fruit. Fully mechanized picking is efficient, but it also risks fruit damage. Therefore, semi-automated tools are needed to improve bitter orange picking. This paper presents a smart manual picker designed to facilitate picking while predicting fruit maturity based on picking force as well as various chemical and physical parameters using machine learning (ML). The study methodology consists of five stages: (1) manufacturing the smart picker, (2) picking 50 bitter orange samples, (3) measuring the characteristics of the bitter oranges in the laboratory, (4) training different ML models, and (5) identifying the most accurate model for predicting fruit maturity. The results indicate that as fruits mature, their weight, CIE-L*a*b* values, and pH levels increase, while picking force and hardness decrease. Notably, picking force exhibited a strong correlation (93.5

The objective of this study was to determine the energy balance, and greenhouse gas (GHG) emissions associated with the growing of blackberry fruit. The study was carried out during the 2024–2025 agricultural season. Within this scope, energy use efficiency, specific energy, energy productivity, net energy, and GHG emissions were assessed. The total energy input for blackberry fruit growing was calculated as 18,568.18 MJ ha−1. Among the input components, diesel fuel accounted for the highest share at 6847.30 MJ ha−1 (36.88

Improving energy efficiency and reducing the carbon footprint of crop production are critical for sustainable agriculture, particularly in semi-arid regions where resource use efficiency is essential. This study evaluated the effects of different fertilization strategies on energy use efficiency and carbon footprint in maize production. A field experiment was conducted during the 2023 growing season in Konya Province, T & uuml;rkiye, using a randomized block design with three treatments and three replications. The treatments included an unfertilized control (U1), inorganic fertilizer application (U2), and liquid animal manure application (U3). The results showed that the highest grain yield was obtained in the liquid manure treatment, which was 2.08 times higher than the unfertilized treatment and 1.18 times higher than the inorganic fertilizer treatment. The highest total energy input was recorded in the inorganic fertilizer treatment (26,235.12 MJ ha-1), while the highest total energy output was observed in the liquid manure treatment (203,154 MJ ha-1). The liquid manure treatment also showed higher net energy efficiency, output-input ratio, carbon efficiency, and carbon sustainability index, while producing the lowest carbon footprint per unit of product. These findings indicate that liquid animal manure can improve maize productivity while enhancing energy efficiency and reducing carbon emissions in semi-arid agroecosystems.

The objective of this study was to determine the energy balance and greenhouse gas (GHG) emissions associated with the production of industrial hemp (Cannabis sativa L.) under semi-arid Central Anatolian conditions. A field experiment was conducted using the registered industrial hemp cultivar ‘Vezir’ under semi-arid conditions, and energy use indicators and greenhouse gas emissions were quantified through an input–output analysis based on field-level agricultural inputs and biomass yield. The total energy input was calculated as 13,688.50 MJ ha–1, of which chemical fertilizers (54.339%) and diesel fuel (32.09%) jointly accounted for more than 90%, followed by irrigation water (4.66%), machinery 2.87%), and human labor (0.21%). The corresponding energy output reached 388,073.80 MJ ha–1, yielding an energy use efficiency of 28.35, a specific energy of 0.62 MJ kg–1, an energy productivity of 1.61 kg MJ–1 and a net energy of 374,385.28 MJ ha–1. Of the total energy input, 36.95% was direct and 63.05% indirect, while 89.35% originated from non-renewable sources and only 10.65% originated from renewable sources. Total GHG emissions amounted to 590.14 kg CO2 eq ha–1, with diesel fuel and nitrogen fertilizer identified as the dominant emission sources. These findings indicate that industrial hemp combines high productivity with low environmental burdens under semi-arid conditions. Beyond its favorable energy balance, hemp offers potential contributions to climate-smart agriculture through efficient resource use, reduced greenhouse gas emissions per unit of output, and diversification of cropping systems in water-limited environments. Therefore, industrial hemp can support the transition toward more sustainable and resilient agricultural systems in semi-arid regions of Türkiye and similar agroecological zones.

The purpose of this study is to determine the energy balance and greenhouse gas emissions of white cherry growing. The study was conducted during the 2023–2024 agricultural period. The study data were collected from the 101 white cherry growers (reachable) determined according to complete count method in Konya province in Türkiye. In this study were done the energy use efficiency, specific energy, energy productivity, net energy and greenhouse gas emissions calculations of white cherry growing. Energy inputs in white cherry growing, including human labor, machinery, chemicals, chemical fertilizers, mineral oil, diesel fuel, irrigation water and electricity were supplied in terms of their usage per hectare. The energy output of white cherry fruit was calculated. The energy inputs in white cherry growing were calculated respectively as chemical fertilizers 4393.11 MJ ha−1 (23.73

The purpose of this study was to determine the energy use and greenhouse gas emission levels in kumquat production. Energy use efficiency indicators and greenhouse gas emission figures for the 2022–2023 production season have been defined. The study was conducted during the 2022–2023 production period in Mersin province, ranking in second place in terms of citrus production in Türkiye. The energy input and output for kumquat production have been calculated as 24,914.75 MJ ha−1 and 8635.50 MJ ha−1, respectively. The energy use efficiency was found to be 0.35, with a specific energy of 5.48 MJ kg−1, an energy productivity of 0.18 kg MJ−1 and a net energy value of −16,279.25 MJ ha−1. The required direct energy percentage is 36.79

This study aims to determine the energy use efficiency and greenhouse gas emissions in Kaman walnut production. It provides the comprehensive of the energy use efficiency and greenhouse gas emissions associated with the production of Kaman walnut varieties produced in Türkiye. Energy use efficiency indications and greenhouse gas emission ratios were computed for the 2023–2024 production season. The energy use efficiency, specific energy, energy productivity and net energy were computed 3.39, 5.19, 0.19 and 105,083.78 MJ ha−1, respectively. Energy inputs in walnut production contain 17,803.62 MJ ha−1 (40.09

Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices using the k-Nearest Neighbors (KNN), Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms. The results showed an inverse relationship between the storage period and the hardness of the apple slices, with the average hardness values gradually decreasing from 4.33 (day 1) to 3.37 (day 5). Treatment with atmospheric plasma at a pressure of 5 atm and an immersion time of 3 min gave the best results for maintaining the hardness of the slices during the storage period, recording values of 4.85 (first day) and 3.68 (fifth day), outperforming other treatments. The average improvement rate was 23.09% over five consecutive days. Regarding the CNN algorithms, the ANN algorithm achieved the highest classification accuracy of 97%, while the Tree algorithm achieved the lowest accuracy of 88.7%. The KNN and SVM algorithms achieved classification accuracies of 94.7% and 95.1%, respectively. The study demonstrated the possibility of using a CNN to classify apple slices based on the degree of hardness. Furthermore, the application of atmospheric plasma at 5 atmospheres with a 3-min immersion improves the firmness of the apple slices by inhibiting degradative enzymes while preserving the cellular structure and tissue quality.

The objective of this study is to investigate the energy efficiency, greenhouse gas emissions and irrigation associated with orange production. The study will provide valuable insights into the environmental impact of orange production. The study was conducted during the 2020–2021 agricultural season in the province of Adana. The study data were collected from the 31 orange producers determined according to stratified random sampling method in Adana province. The aim of this study is to evaluate the energy use efficiency, irrigation activities and carbon emission levels of orange production in Adana province of Türkiye, based on the relevant literature. The study findings reveal that the energy required for orange production includes human labor 5033.73 MJ ha−1 (10.58

The objective of this study was to determine the energy use and greenhouse gas emissions associated with sesame production. Energy use efficiency indicators and greenhouse gas emission rates were calculated for the 2022-2023 production season. The energy input and output for sesame production were found to be 12079.15 MJ ha-1 and 30052.44 MJ ha-1, respectively. The study found an energy use efficiency of 2.49, with a specific energy of 12.20 MJ kg-1, an energy productivity of 0.08 kg MJ-1, and a net energy value of 17973.29 MJ ha-1. The direct and indirect energy inputs were calculated to be 4584.41 MJ ha-1 (37.95%) and 7494.74 MJ ha-1 (62.05%), while the renewable and non-renewable energy inputs were calculated to be 469.12 MJ ha-1 (3.88%) and 11610.03 MJ ha-1 (98.65%), respectively. The calculation shows that the total greenhouse gas emissions are 380.52 kgCO2-eq ha-1 and the greenhouse gas emission rate is 0.38 kgCO2-eq ha-1. Sesame production is highly profitable for the 2022-2023 production season in terms of energy use efficiency.

This research aims to calculate the energy use efficiency, greenhouse gas (GHG) emissions and production costs of organic table grape production. The data of this research belong to the 2021 production season and the study was carried out in 2022. The agricultural production inputs and outputs used in organic table grape production were calculated to determine the energy use efficiency and GHG. Energy use efficiency, specific energy, energy productivity and net energy values were calculated, respectively as 3.84, 3.07 MJ kg −1 , 0.33 kg MJ −1 and 174,690.11 MJ ha −1 . The used total energy inputs in organic table grape production can be classified as 42.50% direct, 57.50% indirect, 28.30% renewable and 71.70% non-renewable. Total GHG emission was calculated as 4411.47 kgCO 2‑eq ha −1 for organic table grape production, with electricity having the greatest share by 3239.85 kgCO 2‑eq ha −1 (73.44%). GHG ratio was calculated as 0.22 kgCO 2‑eq kg −1 . In addition, according to the study, the production cost of table grapes is 7.543 TL kg −1 and the income is 9.470 TL kg −1 . According to the results of the study, it was concluded that organic grape production in the 2021 production season is profitable in terms of energy use efficiency (3.84). In addition, this study is important since there has not been a collective study in the literature on the energy balance, GHG emissions, costs and incomes in organic table grape production in the region.

Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture content in bean and corn seeds based on measuring their dimensions using image analysis using artificial neural networks (ANN). Experimental tests were carried out at three levels of wet basis moisture content of seeds: 9, 13 and 17%. The analysis of the results showed a direct relationship between the wet basis moisture content and the main dimensions of the seeds. Based on the statistical analysis of the seed material, it was shown that the characteristics examined have a normal or close to normal distribution, and the seed material used in the investigation is representative. Furthermore, the use of artificial neural networks to predict the wet basis moisture content of seeds based on changes in their dimensions has an efficiency of 82%. The results obtained from the method used in this work are very promising for predicting the moisture content.

This study aimed to determine the energy use and greenhouse gas emissions in pecan production. Energy use efficiency indicators and greenhouse gas emission rates were calculated for the 2022–2023 production season. The energy input and output for pecan production were calculated to be 21,548.08 MJ ha−1 and 73,812.43 MJ ha−1, respectively. The energy use efficiency was found to be 3.43, with a specific energy of 5.07 MJ kg−1, an energy productivity of 0.20 kg MJ−1, and a net energy value of 52,264.35 MJ ha−1. The direct energy required is 8209.46 MJ ha−1 (38.10

This study analysed the energy balance and greenhouse gas emissions in the production of the ‘Maraş 18’ walnut cultivar. Energy use efficiency indicators and greenhouse gas emission rate calculations were performed for the 2022–2023 production season in ‘Maraş 18’ cultivar production. Here, total energy input was calculated as 19,962.04 MJ ha−1 and total energy output was calculated as 30,145.68 MJ ha−1. Energy use efficiency, specific energy, energy efficiency and net energy were determined as 1.51, 11.86 MJ kg−1, 0.08 kg MJ−1 and 10,183.63 MJ ha−1, respectively. The percentages of direct energy, indirect energy, renewable energy, and non-renewable energy inputs in production were calculated as 47.02

The relationship between the power consumed in the engine and the power take-off (P.T.O.) shaft of a maize silage harvester is critical to understanding the efficiency and performance of the harvester. The power consumed in the engine directly affects the power available for use on the P.T.O. shaft, which is the power source for the suspended silage harvesters. The research aimed to predict the power consumption of the P.T.O. shaft based on the power consumption of the tractor engine at different operating parameters, which are two applications of the P.T.O. shaft (540 and 540E rpm) and two forward speeds (1.8 and 2.5 km/h) using machine learning algorithms. The best results in terms of engine power consumption were achieved in the 540E P.T.O. application, and the forward speed was 1.8 km/h. The results also gave a correlation between the power consumed by the engine and the P.T.O shaft of 87%. Regarding prediction algorithms, the Tree algorithm gave the highest prediction accuracy of 98.8%, while the KNN, SVM, and ANN algorithms gave an accuracy of 98.1, 60, and 60%, respectively.

The aim of this research was to determine the energy balance and greenhouse gas (GHG) emissions of pomegranate cultivation. This research was conducted during the 2019-2020 production period in Ortaca district of Mugla province, Turkey. The agricultural inputs and outputs used in pomegranate cultivation were computed to determine the energy balance and GHG. According to research findings, the energy inputs in pomegranate cultivation were computed respectively as 10,224 MJ ha(-1) (49.22%) chemical fertilizers energy, 3081.60 MJ ha(-1) (14.84%) electrical energy, 3074.53 MJ ha(-1) (14.80%) diesel fuel energy, 1939.14 MJ ha(-1) (9.34%) irrigation water energy, 1033.72 MJ ha(-1) (4.98%) human labour energy, 853.11 MJ ha(-1) (4.11%) chemicals energy and 564.41 MJ ha(-1) (2.72%) machinery energy. Total input energy was computed as 20,770.51 MJ ha(-1). Output energy (pomegranate fruit) was computed as 56,430 MJ ha(-1). Energy use efficiency, specific energy, energy productivity and net energy values were computed respectively as 2.72, 0.70 MJ kg(-1), 1.43 kg MJ(-1) and 35,659.49 MJ ha(-1). The consumed total energy inputs in pomegranate cultivation can be classified as 43.95% direct, 56.05% indirect, 14.31% renewable and 85.69% non-renewable. Total GHG emission was computed as 2446.46 kgCO(2-eq)ha(-1) for pomegranate cultivation with the greatest share for nitrogen (23.54%). GHG ratio value was computed as 0.08 kgCO(2-eq)kg(-1) in pomegranate cultivation. According to the findings of this current research, pomegranate cultivation is a profitable production in terms of energy use efficiency (2.72) for the 2019-2020 production period.

The aim of this research was to determine the energy balance and greenhouse gas (GHG) emissions of pomegranate cultivation. This research was conducted during the 2019–2020 production period in Ortaca district of Muğla province, Turkey. The agricultural inputs and outputs used in pomegranate cultivation were computed to determine the energy balance and GHG. According to research findings, the energy inputs in pomegranate cultivation were computed respectively as 10,224 MJ ha−1 (49.22

The purpose of this study was to determine the energy use efficiency and greenhouse gas emissions of lemon production. It was performed during the 2019–2020 production period in Turkey. The agricultural inputs and outputs used in lemon production were calculated to determine the energy use efficiency and greenhouse gas emissions. According to study findings, the energy inputs in lemon production were calculated respectively as 16,046.98 MJ ha−1 (55.43

This study was conducted in a deep well simulator used for typical irrigation studies. In this work, the changes in the pump flow rate, drawdown, noise level, and pump pressures were analyzed for three different gravel zone thicknesses used in the well. From the study, it was found that a high gravel zone thickness increased the well’s drawdown levels during pumping. For drawdown values of 40, 45, 50 and 55 m3 h–1, an increase in the gravel thickness by 10 cm increased these values by 2.92, 2.41, 2.38 and 2.37 times, respectively. When the gravel thickness was doubled (from 5 cm to 10 cm), the hydraulic conductivity decreased by about half and head loss doubled. As a result, gravel thickness directly affected the drawdown rate of the pump. It was shown that different drawdown values resulting due to different gravel thicknesses should be taken into consideration when placing the pump in a deep well.