Harvesting Of Products Research Articles

One-Step Strategy Streamlining Microalgae Biofuel Production: Integration of Microalgae Harvesting and Methane Production by Dual-Functional Magnetic Nanoparticles

One-Step Strategy Streamlining Microalgae Biofuel Production: Integration of Microalgae Harvesting and Methane Production by Dual-Functional Magnetic Nanoparticles

Portable dual-functional hygroscopic device for efficient atmospheric water harvesting and hydrogen production in diverse environments

Portable dual-functional hygroscopic device for efficient atmospheric water harvesting and hydrogen production in diverse environments

IMPLEMENTASI FUZZY TSUKAMOTO DALAM SISTEM PREDIKSI PANEN PADI DI KABUPATEN INDRAMAYU

Strategic efforts to maintain rice production, which is the staple food of the Indonesian people, must continue to be carried out, one of which is maintaining production in rice-producing areas such as Indramayu Regency is a step that must be prioritized. Knowing the prediction of the upcoming rice harvest is the right effort to make. Fuzzy Tsukamoto logic is a method that can be used as a calculation to make predictions, the selection of fuzzy Tsukamoto is due to its ability to predict with factors that have uncertainty such as rainfall, in addition, land area and harvest yields can also be used as variables to obtain predictions through fuzzy Tsukamoto. This study was conducted to implement fuzzy Tsukamoto for predicting rice harvest yields, where the results of the fuzzy Tsukamoto logic process that have been carried out produce six rules at the inference stage in the category of increasing or decreasing harvests, which are used as the basis for the defuzzification process to find the output value of the harvest. The calculation results are then applied in a website-based system using the Laravel framework and MySql DBMS so that they can be used to predict rice harvest yields. Based on the prediction process carried out through a system based on the fuzzy Tsukamoto method, out of 31 sub-districts, 16 sub-districts have the potential to experience a decline, and 15 sub-districts have experienced an increase in rice harvest production in Indramayu Regency.

A Procedure to Characterize Wood Pile Inventories at Roadside

Tracking roadside wood inventories is necessary for wood procurement. However, this operation is increasingly problematic due to the costs associated with reaching remote sites, labour shortage, and the methods providing limited information on the characteristics of the logs required for transport planning. To overcome these problems, an automated procedure has been developed in Arcmap to characterize individual wood pile inventories at roadside by using GPS points of forwarders, harvester production files, and the road network shape files. An inventory of the logs in the harvest area followed by a wood pile inventory at the roadside were made to evaluate how the procedure could trace logs from machine operating trail to predicted wood pile locations. The study was done at six harvest blocks in the Saguenay-Lac-Saint-Jean region in the province of Quebec, Canada. The procedure was not able to differentiate individual wood piles and aggregated several piles into predicted unloading areas. Results indicated a similarity index of 72% to manually inventoried wood piles. The similarity index could be explained by the low percentage of inventoried unpredicted wood pile lengths (3%) and a high percentage of overpredicted wood pile lengths (27%). The positive allocation rate could not be assessed at the level of the individual piles. On the other hand, the procedure properly allocated 96% of the logs to unloading areas. With the level of precision obtained, the developed procedure could be beneficial for managing the transportation of wood at the level of the road segment since it provides all the dendrometric data of the logs available for transport without requiring human intervention in the forest.

A 3D multiobjective multi-item eco-routing problem for refrigerated fresh products delivery using NSGA-II with hybrid chromosome

In a developing countries, say India, about 40 percent fresh foods are wasted during transportation and $14 billion of post harvest products are lost due to inefficiency of cold supply chain such as using non-refrigerated vehicle during last mile transport. Hence, temperature control through refrigerated transportation is very essential. But, the by-product of this process i.e., carbon emission (CE) due to transportation and refrigeration is detrimental for the world. Only logistic transportation contributes about 25 percent of global CE. Due to the world wide infrastructural development, there are several available routes for the road transportation among different cities. For the maintenance of these routes, there are some toll plazas for collection of money. Some routes also cross the rail lines. For maximum profit, a supplier distributes the products at the earliest as the retailers fix the product’s price with respect to its freshness. Again, this process generates more CE. With these realities, a 3D multiobjective multi-item eco-routing problems for refrigerated fresh products (3DMOMIERPsfRFPs) are developed, where a refrigerated vehicle starts from a depot (refrigerated) and comes back after distributing the fresh products to retailers as per their demands. The optimal routing plan, vehicle’s velocity and preservation rate are determined so that total profit and CE are respectively maximized and minimized simultaneously. For solution, an NSGA-II with different modified operators (NSGA-IIwDOs) is developed with hybrid chromosomes having discrete and continuous variables together, probabilistic selection, problem-specific crossover and generation-dependent mutation. Some performance metrics are presented through NSGA-IIwDOs on the standard TSPLIB problems. Results of 3DMOMIERPsfRFPs without and with specific delivery time slots, road-dependent vehicle velocity and time constraints are evaluated. Pareto front for multiobjective are depicted. As a particular case, results of a previous investigation are obtained. Some of the managerial decisions are observed.

Implementation of hybrid tomatoes as an effort to increase tomato harvest production

Tomatoes are a horticultural commodity that is widely cultivated, especially in Bandung Regency. Tomato plants are widely cultivated because they have a fairly high economic value and are in demand by the community because of their many health benefits, among them are vitamin and antioxidant content. Currently, tomato production is declining, so education and assistance are needed to increase the potential for results. The target of this activity is tomato farmers in four sub-districts in Bandung Regency. The purpose of this activity is to increase farmers' knowledge in cultivating hybrid tomatoes at various altitudes. The activity started with counseling, field practice, and evaluation of the results of the activity. As a result of this activity, farmers have additional knowledge in appropriate technology for tomato plants in their respective locations. After the evaluation process, tomato production increased significantly, both in terms of quantity and quality, so it had a positive impact on the economic welfare of farmers.

In-field performance evaluation of robotic arm developed for harvesting cotton bolls

Robotic harvesting of cotton bolls combines the advantages of manual picking and mechanical harvesting, including selective picking and reduced labor dependency. Thus, we designed, developed, and tested a cotton-picking robotic arm equipped with a depth camera, a 4-DoF manipulator, and a vacuum-type end-effector. Convolutional neural networks were implemented for cotton boll recognition, while an artificial neural network-assisted particle swarm optimization-based model was utilized for solving inverse kinematics. Linear actuators, controlled by a microcontroller and relays, positioned the end-effector for cotton boll harvesting. Laboratory testing resulted in picking 44–49 cotton bolls per hour, taking approximately 73.0 s per boll. During field testing, the robotic arm picked 40–42 bolls per hour with an average time of 86.0 s per boll. Picking efficiency ranged from 76.19% to 85.71% in the lab and 66.32% to 72.04% in the field. The complex structure of cotton plants sometimes hindered the end-effector’s path, resulting in lower picking efficiency during field testing. As observed in present study, manual pickers achieved a significantly higher picking rate, harvesting 1.381 kg of cotton per hour, nearly 4.7 times more than the robotic arm. Manual pickers achieved 98.04% picking efficiency, significantly higher than the robotic arm’s 68.25%. This significant difference in efficiency is due to the agility, adaptability, and decision-making abilities of human pickers. The developed robotic arm demonstrates competitive efficiency in the domain of harvesting robots, contributing to advancements in robotic agriculture. Future research and development efforts in this field hold the potential to enhance the efficiency and productivity of robotic cotton harvesters.

Agronomic and Starch Content Studies of Local Adlay Accessions (Coix lacrima jobi - L.) from West Sumatra, Indonesia

Indonesia has a high potential for diverse local food resources, one of which is the plant of adlay. Exploration activities aimed at collecting genetic resources of adlay have been successfully carried out in the province of West Sumatra, Indonesia, resulting in the collection of six local genotypes. The purpose of this research is to obtain information about the agronomic potential and starch content of several local adlay accessions. The study was conducted from April to September 2023 in Limau Manis, Padang City, West Sumatra. The six adlay accessions used in the study are PTA-1, KKD-3, PH-4, BTA-2, GT-2, and TJR-2. The experimental design employed was a completely randomized design with three replications. Based on the research findings, significant differences were observed in both agronomic aspects and starch content among the tested accessions. Adlay with the accession code PH-4 exhibited advantages in terms of harvest maturity and production. The starch content of adlay ranged from 56.64% to 61.26%, with the PH-4 accession having the highest amylopectin content (96.60%), while the highest amylose content was found in the GT-2, TJR-2, and KKD-3 accession (19.90%, 20.03%, and 20.48%). Accession PH-4 can be proposed as a candidate for Adlay variety assembly. Keywords: agronomic, starch content, adlay accessions, Coix lacrima jobi

Fresh water production via atmospheric water harvesting using solar energy for green hydrogen production

Abstract Solar energy is used to drive an atmospheric water harvesting unit to produce fresh water needed for the electrolysis process to produce hydrogen, as well as to drive the electrolyzer. So, the system can be considered as a solar powered hybrid system for fresh water harvesting and green hydrogen production implementing the electrolysis concept. Implementation of the system specially in arid regions with high levels of water scarcity and high relative humidity levels helps in achieving SDGs 3, 7, 9, and 13 progressively, improving health and wellbeing, reducing water scarcity, using clean renewable energy, and limiting climate change. The system is proposed and investigated for the production of both fresh and clean water and green hydrogen gas. To improve the productivity of the overall system, performance enhancement of the atmospheric water harvesting subsystem is experimentally investigated to assure the reliability and continuous operation of the overall green hydrogen hybrid system. It was investigated in summer and winter using different number of porous sheet metals with the silica gel bed. The results show that the productivity of the atmospheric water harvesting system can be increased by a minimum of 6% with one mesh metal sheet and 30% with two meshes, and increases by a maxinmum of 60% with one mech and 260% with two meches.

Maximization of Micractinium sp., biomass and use of Dual-Purpose reduced graphene supported vanadium oxide nanoparticles for harvesting and subsequent biodiesel production

A sustainable media composition comprising of nanourea (NU), groundnut de-oiled cake (GDOC), and seaweed Extract (SE) was formulated using a mixture design for the cultivation of Micractinium sp. Maximum biomass yield and productivity of 5.52 ± 0.09 g/L and 0.72 ± 0.03 g/L d−1 were observed at 1.67 mM NU, 0.134 g/L GDOC and 0.250 mL L−1 SE, respectively. The highest lipid yield of 2.73 ± 0.24 g/L was also observed, respectively. The reduced graphene-supported vanadium oxide nanoparticles (RGO-VNPs) with a net surface charge of + 34.10 mV were developed, which acted as a % as well as a catalyst for transesterification. A maximum flocculation efficiency of 98 % was observed with 200 ppm of RGO-VNPs. The Fatty Acid Methyl Ester (FAME) yield of 96.81 ± 1.61 % was observed. Thus, the present study could provide a plausible solution for one-pot harvesting and synthesis of biodiesel utilizing microalgal lipids.

Harvesters’ productivity prediction in Brazilian Eucalyptus plantations: development of a model from machine learning

ABSTRACT Productivity analysis in mechanized harvesting has traditionally relied on statistical expertise and mathematical modeling. However, machine learning tools have emerged as a viable alternative, as they serve the same purpose, utilizing a combination of varied attributes (quantitative and qualitative) and handling large datasets. This study aimed to determine whether the inherent attributes of mechanized timber harvesting of Eucalyptus spp. plantations enable the creation of a high-performance model that can accurately predict productivity from machine learning. For the modeling, we considered five attributes concerning forest inventory, in addition to working hours and the operator experience level. We considered the productivity, timber harvested per working hour, as the target attribute of the modeling. We subjected the database to 17 common algorithms in default mode and compared them according to error metrics and accuracy. We also determined the relative importance of each attribute in the predictive model. The inherent attributes concerning mechanized timber harvesting of Eucalyptus spp. plantations evaluated in this study enable the creation of a high-performance model that can accurately predict productivity from machine learning. The Gradient boosting model in ensemble mode can predict the productivity of harvesters in Eucalyptus spp. plantations with an R2 of 0.81. The attributes that have greater relative importance are operator experience level, average individual tree volume, and stand density with 100%, 76.3%, and 65.8%, respectively.

A Comprehensive Study On The Business Feasibility, And Optimization Model For Paddy Cultivation On Marginal Agricultural Land In Pekalongan District

Paddy cultivation on marginal land is the agribusiness activity. The aim of this research is to assess the feasibility status of paddy cultivation on marginal land and make efforts to optimize based on dynamic modeling system analysis. The research method used is descriptive quantitative with the dynamic modeling system analysis. The results show that the business profitability analysis shows a profit value of IDR 30,486,500, R/C Ratio 2.30, BEP unit 6,316 kg, BEP Sales IDR 15,477,220,-, and returnability 1.30%. This means this business is very profitable. The results of business financial analysis show an NPV IDR 2,536,754,052,-, profitability index 21.36%, IRR 14.21%, and payback period 14.4 years. This means this business is very worthy for development. The results of the dynamic modeling system analysis show that the paddy growth rate is influenced by using technology. Technology will influence rice growth of 3.5-7.0 cm. Nutrients influence the paddy biomass on marginal land. Using 10-20 kg/Ha of fertilizer will affect paddy biomass growth of 35-70 kg/Ha. The results of the dynamic model analysis show that the maximum paddy harvest production on marginal land is 25-50 kg/Ha/month with fertilizer use of 45-90 kg/Ha/month. This research concludes that the level of paddy cultivation feasibility on critical land is still quite feasible and profitable to develop. model analysis results show that of using agricultural technology and administering appropriate fertilizer doses will greatly determine the level of rice productivity on marginal agricultural land.

Mosaic chromosomal alterations in hematopoietic cells and clinical outcomes in patients with multiple myeloma.

Mosaic chromosomal alterations (mCAs) in hematopoietic cells increase mortality and risk of hematological cancers and infections. We investigated the landscape of mCAs and their clinical consequences in 976 patients with multiple myeloma undergoing high-dose chemotherapy and autologous stem cell support (ASCT) with median 6.4 years of follow-up. mCAs were detected in the stem cell harvest product of 158 patients (16.2%). Autosomal aberrations were found in 60 patients (6.1%) and affected all chromosomes. Loss of chromosome X was found in 51 females (12.7%) and loss of chromosome Y in 55 males (9.6%). Overall survival and progression were similar between carriers of autosomal mCAs and non-carriers. In contrast, female patients with loss of the X chromosome had longer overall survival (age-adjusted[a.a.] HR 0.54, 95% CI 0.32-0.93, p = 0.02), lower risk of progression (a.a. HR 0.55, 95% CI 0.35-0.87; p = 0.01), and better post-transplant response (higher degree of complete response (CR) or very good partial response (VGPR)). The reason for this substantial effect is unknown. Additionally, myeloma clones in the stem cell product was confirmed by mCA analysis in the few patients with multiple mCAs (n = 12 patients). Multiple mCAs conferred inferior overall survival (a.a. HR 2.0, 95% CI 1.02-3.84; p = 0.04) and higher risk of myeloma progression (a.a. HR 3.36, 95% CI 1.67-6.81; p < 0.001), which is presumed to be driven by suspected myeloma contaminants.

Glycine betaine material affects the growth and yield of asparagus in the long-term harvest production system in a semi-forcing culture

Glycine betaine material affects the growth and yield of asparagus in the long-term harvest production system in a semi-forcing culture

How stem size variations in forest stands influence harvester productivity and the use of productivity models

ABSTRACT Stem size has the greatest effect on harvester productivity, and stem sizes vary in a forest stand. How these within-stand variations influence harvester productivity is normally not considered in studies or predictions of productivity. This study suggests reasons as to why the current production and/or application of productivity models are prone to bias from stem size variations in a stand, irrespective of whether models were developed from tree-based or stand-based studies. Moreover, it also provides empirical data on the stand stem size variation’s influence on stand-based modeling of harvester productivity. Data from 11 harvesters in 347 final fellings and four harvesters in 80 thinnings were used. The mean productivity was 26.7 and 11.0 m3/PMh5 in final felling and thinning, respectively, and the mean stem size explained most of the observed variation. The productivity in final felling decreased with increased levels of stand stem size variation, as well as with increases in the proportion of broadleaf trees in the stand. For thinnings, productivity increased with increases in the proportion of pine trees in the stand, but there was no significant effect of stand stem size variation or other tested factors. The results show that stand stem size variation is a relevant factor to consider when modeling and predicting harvester productivity.

Evaluation of Standard Times for Some Local Blacksmith’s Products

The practice of menial farming in the southwestern part of Nigeria have necessitated the need for the blacksmith industry to produce farm implements for the need of their immediate environment (farmers). With increased cost and time of production, coupled with demand size, reduced productivity and in order to meet the customer’s schedule, the study was conceived to evaluate the standard time to start and finish a product to manage production time and to estimate the optimum production capacity of the blacksmith workstation. After a careful survey of the local blacksmiths and with the aid of a stopwatch and study time chart, standard time was evaluated for harvester, digger and axe. These implements are the most commonly used items by the locals. The standard time for the local blacksmith’s axe, digger and harvester production was estimated to be 1458.35, 1931.44 and 936.66 Seconds respectively. It can therefore be concluded that the blacksmiths working for 8 hours a day will produce either (i) about 20 units of axe per day, (ii) 15 units of digger per day, or (iii) 31 units of harvester per day, or (iv) 7 units each, of the products per day

Cut-to-Length Harvesting Prediction Tool: Machine Learning Model Based on Harvest and Weather Features

Weather is a significant factor influencing forest health, productivity, and the carbon cycle. However, our understanding of these effects is limited for many regions and ecosystems. Assessing the impact of weather variability on harvester productivity from plantation forests may assist in forest planning through the use of data modeling. We investigated whether weather data combined with timber harvesting attributes could be used to create a high-performance model that could accurately predict harvester productivity in Eucalyptus plantations using machine learning. Furthermore, we aimed to provide an online application to assist forest managers in applying the model. For the modeling, we considered 15 weather and timber harvesting attributes. We considered productivity as the target attribute. We subjected the database to 24 common algorithms in default mode and compared them according to error metrics and accuracy. From the timber harvesting features combined with weather features, the Catboost model can predict the productivity of harvesters in a tuned mode, with a coefficient of determination of 0.70. The use of weather data combined with timber harvesting attributes in the model is an accurate approach for predicting harvester productivity in Eucalyptus plantations, allowing for the creation of an online, free application to assist forest managers.

Impact of mechanical weed control on soil N dynamics, soil moisture, and crop yield in an organic cropping sequence

Mechanical weed control is a major element of weed suppression in organic farming systems. In addition to the direct effect on weed growth, mechanical weeding, such as harrowing or hoeing, is known to induce side effects on several soil- and crop-related properties. In this study, we investigated the impact of mechanical weeding on soil mineral nitrogen (SMN), soil moisture, and crop yield in an organic crop rotation of grass-clover (Lolium multiflorumLam., Trifolium pratense L.), silage maize (Zea mays L.) and winter barley (Hordeum vulgare L.). The experiment was conducted in two consecutive years (2021, 2022), where each crop was grown in each year on a Plaggic Anthrosol with sandy loam in North-West Germany. Two weed control treatments (mechanical: harrowing, hoeing; chemical: herbicide application) were implemented in a randomized block design with four replications. Greater net nitrogen (N) mineralization in maize compared to winter barley were attributed to the incorporation of grass-clover residues before sowing of maize and greater mineralization potential during the maize growing season. Higher weed growth in maize after mechanical weeding resulted in a reduction of up to 47% in SMN content in the topsoil. In barley, no differences in weed suppression were observed between the treatments and only small effects on SMN were determined after mechanical weeding. The soil water content in the mechanically weeded plots was significantly higher at several events in both years and for both crops, which was attributed to increased water infiltration by disrupting the soil crust. Neither crop yield nor N uptake in harvest products was affected by the different treatments.

PENERAPAN K-MEANS CLUSTERING UNTUK PEMETAAN PRODUKTIVITAS PADI DAN PREDIKSI PANEN DI KABUPATEN INDRAMAYU

Indonesia is a country where the majority of people consume rice, making the issue of rice harvest productivity a concern for many parties. Efforts to maintain food security, especially the availability of rice, are a shared responsibility, where efforts that can be made are to maintain and predict rice production results in areas that are National Rice Granaries. Indramayu is one of the national rice producing districts, which has 31 sub-districts with varying amounts of rice production. The large difference in rice production between sub-districts is an important problem that affects rice harvests in Indramayu, so improvements need to be made, including clustering, namely grouping sub-districts based on harvest potential using K-Means clustering by looking at historical harvest data for the past 5 years between 2019–2023. Then compared using the orange data mining application. K-Means clustering either manually or using orange3 produces three clusters with the same sub-district cluster results. This data is also used to predict future harvest results using the random forest algorithm in Orange3. Analysis of K-Means clustering results and prediction results shows that sub-districts in the lower cluster have the potential for a greater percentage reduction in harvest than clusters with a higher harvest category.

Growth and productivity of stevia variety Cibodas Manis 3 (Stevia rebaudiana) in 40 days planting with agroforestry

Abstract. Choeriah MN, Wijayanto N, Batubara I. 2024. Growth and productivity of stevia variety Cibodas Manis 3 (Stevia rebaudiana) in 40 days planting with agroforestry. Biodiversitas 25: 3277-3283. Stevia (Stevia rebaudiana Bertoni.) is a shrub plant that can be used as a substitute for sugar cane and has the potential to be cultivated in agroforestry ways. This study aims to analyze the growth and productivity of stevia in agroforestry systems compared to monocultures. Stevia was planted in 2 types of land, namely agroforestry and monoculture. The research location's soil and area's climatic conditions were observed stevia agroforestry was grown under pine, eucalyptus, and coffee trees. During stevia planting, the height was measured. Stevia was harvested 40 days after planting, and leaf area, number of leaves, leaf weight, and harvest productivity were measured. In addition, chlorophyll and stevioside yields were analyzed using UV VIS and LC-MS/MS spectrometry. Stevia in agroforestry land had higher chlorophyll and stevioside content than monoculture, although not as good as stevia in monoculture, which had higher height, number of leaves, leaf area, wet weight, dry weight, and productivity compared to agroforestry. The total stevioside produced from agroforestry land was 1.63 kg/ha, while it was 2.19 kg/ha from monoculture land. Stevia in agroforestry land also has a higher secondary metabolite content than in monoculture. This is related to the growing conditions that stress stevia. Pearson correlation test results also show a relationship between the amount of chlorophyll and the stevioside content in stevia plants.