Agricultural Processes Research Articles

Revolutionizing Precision Agriculture with Drone-Based Imaging and Fuzzy Intelligent Algorithms

This paper proposes a clear understanding of drone based agricultural image processing techniques using fuzzy intelligent algorithm. This procedure is used for drone-based agricultural monitoring through intelligent calculations by various images acquired and trained from the field. The objective is to upgrade the exactness and proficiency of crop yield and the accuracy in agriculture. The proposed methods use high quality images caught by drones to remove crop-related infections and evaluate their wellbeing status. The fuzzy based calculation is applied to characterize the harvest wellbeing status and produce a choice to guide and assist the farmers of various regions to concentrate the area of infection based on the images captured from drones. The projected approach is assessed utilizing the information and the consideration and appropriate recommendation can be made after careful evaluation of infected region in the field. Graphical User Interface (GUI) is developed using fuzzy based algorithm which can segment the infectious leaves and classify the type of diseases and check the healthiness of the leaves. Results show that the proposed approach accomplishes higher exactness and improved effectiveness in distinguishing crop wellbeing status in comparable with existing the procedures. This study shows the capability of integrating drone-based agriculture with fuzzy intelligent image processing tool which makes efficient for farmers to improve the quality of crops, attain greater yield and to develop the nation with drone-based agribusiness.

Impact of cropping systems on soil quality

The agricultural intensification process implemented during the last 20 years consisted of replacing a crop-pasture rotation system with a continuous no-till cropping system that affected the functional properties of the soil in the medium term. The hypothesis of this study was that the annual cropping intensification process could be characterized, at the commercial level, by soil property indicators that capture changes in the pathway of cropping systems (CSs) in the medium term. The objectives were: i) to identify CSs based on land use; and ii) to characterize soil properties associated with different CSs. Based on this, a database was built with the land use description from 2014 to 2020 of 45 farms belonging to farmers associated with FUCREA and AUSID. A Principal Component Analysis (PCA) was performed with 14 land use indicators to select those that best summarized all the information. The selected indicators were pasture-crop ratio (PCR), soybean frequency during the summer annual cropping phase (SSbCF), intensification of annual cropping phase index (IAI), and intensity of soil use index (ISI). By performing a Cluster Analysis with these indicators, the following CSs were identified: pasture-crop rotation (ROT-PC), continuous cropping with high frequency of C4 photosynthesis species (CC_Corn), and continuous cropping with high frequency of soybean (CC_Soybean). Based on the identified CS, 64 representative sites were selected within a 60 km radius from the coordinates −33.17 S and −57.80 W, Uruguay's most influential annual cropping area, assessing 46 soil properties sensitive to changes in their management in the medium term and capable of affecting productivity. A PCA was used to select the properties that best summarized the information. A Linear Discriminant Analysis (LDA) with the selected variables and the identified CS groups determined a loss in soil fertility (organic carbon and electrical conductivity) and nutrient availability (potassium), which is related to CS diversification (CC_Soybean> CC_Corn>ROT-PC).

Enhancing agriculture production through smart assessment of soil nutrients

ABSTRACT The growth of the world population is leading to an increased demand for food production. Consequently, there is a need to update agricultural processes to enhance production. Smart farming has evolved as a farm management concept that leverages real-time, dependable, and site-specific agricultural information to ensure consistent and sustainable yields. This study focuses on soil nutrient analysis, specifically nitrogen (N), phosphorus (P), and potassium (K) for enhancing crop production. Maintaining an appropriate balance of NPK is crucial for the well-rounded nutrition of crops. Various environmental factors such as temperature, rainfall, humidity, and soil pH affect the NPK composition of agricultural land. The optimal usage of NPK levels can help to improve plant growth, increase crop yields, and enhance overall farm productivity. Hence, we have proposed a stack regressor learning algorithm comprising of two layers for accurate, reliable, and context-sensitive predictions of NPK levels. The proposed approach considers the combined impact of these environmental factors and predicts the optimal NPK requirements tailored to specific crops and agricultural land. The analysis of graphs demonstrates that the predictive accuracy of the proposed stacked regressor model far surpasses that of preexisting built-in machine learning models. This study helps farmers make informed decisions, leading to productivity and sustainability.

Reaction mechanisms for methyl isocyanate (CH3NCO) gas-phase degradation

Methyl isocyanate (MIC) is a toxic chemical found in many commercial, industrial, and agricultural processes, and was the primary chemical involved in the Bhopal, India disaster of 1984. The atmospheric environmental chemical reactivity of MIC is relatively unknown with only proposed reaction channels, mainly involving OH-initiated reactions. The gas-phase degradation reaction pathways of MIC and its primary product, formyl isocyanate (FIC), were investigated with quantum mechanical (QM) calculations to assess the fate of the toxic chemical and its primary transformation products. Transition state energy barriers and reaction energetics were evaluated for thermolysis/pyrolysis-like reactions and bimolecular reactions initiated by relevant radicals (•OH and Cl•) to evaluate the potential energy surfaces and identify the primary reaction pathways and products. Thermolysis/pyrolysis of MIC requires high energy to initiate N-CH3 and C-H bond dissociation and is unlikely to dissociate except under extreme conditions. Bimolecular radical addition and H-abstraction reaction pathways are deemed the most kinetically and thermodynamically favorable mechanisms. The primary transformation products of MIC were identified as FIC, methylcarbamic acid, isocyanic acid (isocyanate radical), and carbon dioxide. The results of this work inform the gas-phase reaction channels of MIC and FIC reactivity and identify transformation products under various reaction conditions.

The Development of Agrotechnopark Model Based on Modern Villages

Agrotechnopark is an area for implementing various types of technology in agriculture, animal husbandry, fisheries, and product processing (postharvest), which has been reviewed by multiple governments and private research institutions and functions as a training ground and center for technology transfer to the broader community. Implementing the Agricultural Technology Park (TTP) uses socio-cultural, ecological, and economic approaches. A comprehensive understanding of community needs will be obtained through a socio-cultural system, which will become input for the company. At the same time, the environmental system will identify the potential basis of local resource excellence as a reference for community development activities. Furthermore, the economic approach is developing productive business units to increase people's income while paying attention to market demand. South Konawe Regency is one of the autonomous regions in Southeast Sulawesi, which is supported by its potential resources. The position of the South Konawe Regency area is very strategic, a southern cross-section of the Southeast Sulawesi peninsula that connects several Regencies/Cities in Southeast Sulawesi with prototypes of coastal and inland villages. One is Atari Jaya Village because it has a heterogeneous population composition, and most of the Javanese Transmigration community is reasonably competent in agricultural development. Because of this, socio-culturally and economically, this village grows and develops and is dynamic by carrying out the diffusion of technology in various fields supporting Agrotechnopark, such as the development of Integrated Farming (Multiple cropping) and integrated farming (Mixed culture) of plantation crops and plantations. Atari Jaya Village is also a central area for the development of Agropolitan both food crops, as well as plantation and horticultural crops on a national scale and a People's Plantation Centre worthy of being an Agricultural Technology Park Area (Agro et al.) in South Konawe Regency, Southeast Sulawesi Province. Development of the Agricultural Technology Park Area (TTP) to become a center for Agribusiness and Agro-fruit tourism as a basic framework for community and regional economic growth towards Konsel Hebat Regency. Atari Jaya Village was realized as an independent village, a pilot Agrotechnopark village in South Konawe Regency, and a service center for upstream and downstream Agribusiness trade in Agrofruit and plantations.

Landscape-Scale Biodiversity Impacts Analysis of Côte d'Ivoire's Cocoa Cultivation along Export Supply Chains.

Agricultural land use for export commodities leads to significant biodiversity impacts. A spatially detailed assessment of these impacts is crucial for implementing effective mitigation policies. Using cocoa cultivation and exports in Côte d'Ivoire as an example, we present a novel framework that combines earth observations, enhanced landscape-scale biodiversity models, and subnational export supply chain data sets to track the tele-connected potential biodiversity impacts of export groups and importing countries. We found that cocoa cultivation accounts for ∼44% of the biodiversity impacts in Côte d'Ivoire's cocoa cultivation areas, with >90% attributable to cocoa exports. The top 10 importing countries account for ∼84% of these impacts. Our method offers improved spatial detail compared to the existing approaches, facilitating the identification of biodiversity impact hotspots. Additionally, the biodiversity impacts of agroforestry cocoa are not always lower compared to full-sun cocoa, especially when agroforestry systems are established in regions of high biodiversity importance. Our transferable framework provides a comprehensive understanding of biodiversity footprint and promotes informed decision-making for sustainable agricultural production, processing, and trade. Our framework's application is currently constrained by the scarcity of detailed supply chain data sets; we underscore the urgent need for improved supply chain transparency to fully unlock the framework's potential.

A Systemic Approach to the Product Life Cycle for the Product Development Process in Agriculture

For a long time, a company’s Product Development Process (PDP) was seen as supporting the operations department, although PDP decisions and mistakes have a considerable impact on market performance. This is critical even in agriculture where bad habits and practices in the PDP can lead rural producers to great losses. Therefore, this research investigates the effect of the PDP on the market performance of rural products (bananas) in the southern region of Brazil, based on two analyses: (i) how sustainability practices support the PDP phases and (ii) how the phases of the Product Life Cycle Assessment (LCA) mediate sustainability practices and PDP phases. This study presents a quantitative analysis using Confirmatory Factor Analysis (CFA) and hierarchical ordinary least squares (OLS) regression of data obtained from a survey of 110 rural producers who directly participate in the banana production and planning process in southern Brazil. Our results show that sustainability practices support the PDP, and we confirm that the product development and post-development phase has an effect on market performance. In addition, we identify that in the pre-development phase of the PDP, dealing with rural products (bananas), the maturity stage of the LCA mediates sustainability. In the PDP development phase, we conclude that rural families who develop economic and environmental practices with their products, which are in the market growth phase may have reduced results. As for the post-development phase of the PDP, we conclude that when companies invest in environmental and social practices, there is a complete mediation of the effect, where these practices lose strength if the product is in the introductory and maturity phases in the market. In an original matter, our study contributes to demonstrating the value of the product life cycle for the Product Development Process in agriculture using sustainability practices through a systemic approach, filling the gap in the literature due to a lack of integrated research on these areas seen.

Advancement of sustainable trypan blue degradation through rice husk ash based-heterogenous Fenton catalyst and study with gaussian process regression

Dyes and their derivatives have been found to be harmful to aquatic organisms, and discharging dye effluent without proper treatment into the environment can lead to affect the usability of water for distinct purposes, including drinking water, agriculture, and commercial processes. To overcome this environmental crisis a cost-effective rice husk ash (RHA) derived catalyst was synthesized as Fenton catalyst and a new, environmentally, and friendly treatment was used to treat trypan blue (TB) from synthetic wastewater. SEM, EDX, XRD, and FTIR were used to describe the prepared catalyst's comprehensive characterization. The optimization of degradation was observed with the batch study, while the reaction time was 1 h at 313 K with 180 ppm catalyst and 200 ppm of H2O2 under pH of 3. DNA study of treated and untreated wastewater demonstrated the non-toxic nature of treated trypan blue containing wastewater. For the betterment of water quality monitoring system Gaussian process regression (GPR) model was introduced for dye degradation determination and prediction in aqueous medium. By managing and controlling dye levels, treatment plants can mitigate the negative effects on receiving water bodies and ecosystems. The production cost of catalyst per kg of rice husk ash was estimated to $ 15.4803 which can be reused up to four cycles with minimum efficiency of 87 %. The outcomes unequivocally demonstrated that the suggested approach offered a completely sustainable resolution for reducing various forms of waste.

Smart IoT Irrigation System Based on Fuzzy Logic, LoRa, and Cloud Integration

Natural resources must be administered efficiently to reduce the human footprint and ensure the sustainability of the planet. Water is one of the most essential resources in agriculture. Modern information technologies are being introduced in agriculture to improve the performance of agricultural processes while optimizing water usage. In this scenario, artificial intelligence techniques may become a very powerful tool to improve efficiency. The introduction of the edge/fog/cloud paradigms, already adopted in other domains, may help to organize the services involved in complex agricultural applications. This article proposes the combination of several modern technologies to improve the management of hydrological resources and reduce water waste. The selected technologies are (1) fuzzy logic, used for control tasks since it adapts very well to the nonlinear nature of the agricultural processes, and (2) long range (LoRa) technology, suitable for establishing large distance links among the field devices (sensors and actuators) and the process controllers, executed in a centralized way. The presented approach has been validated in the laboratory by means of a control scheme aimed at achieving an adequate moisture level in the soil. The control algorithm, based on fuzzy logic, can use the weather forecast, obtained as a cloud service, to reduce water consumption. For testing purposes, the dynamics of the water balance model of the soil were implemented as hardware in the loop, executed in a dSPACE DS1104. Experiments proved the viability of the presented approach since the continuous space state output controller achieved a water loss reduction of 23.1% over a 4-day experiment length compared to a traditional on/off controller. The introduction of cloud services for weather forecasting improved the water reduction by achieving an additional reduction of 4.07% in water usage.

Vegetation and Evapotranspiration Analyses on Climate Maps

This study focuses on the investigation of evapotranspiration (ET) processes under the climatic and geographical characteristics of Turkey. Evapotranspiration refers to the process by which plants transfer water vapour to the atmosphere and is an important part of the water cycle. Vegetation cover, climatic conditions and topographic structures in different regions of Turkey are among the main factors affecting evapotranspiration. In this study, the seasonal and annual variations of evapotranspiration in different regions of Turkey are discussed and the importance of this process in water resources management, agriculture, ecosystems and climate change is emphasised. This research focuses on conducting an evapotranspiration analysis in Turkey by employing imagery data from NASA Global Land Data Assimilation System Version 2 (GLDAS-2), MODIS, TerraClimate, SMAP Level-4, and Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) sources. Evapotranspiration, a pivotal process in the hydrological cycle, significantly impacts water resource management and ecosystem dynamics. This study aims to provide a comprehensive assessment of evapotranspiration dynamics across diverse regions within Turkey. For Turkey, SSM data between 2016-2022 and LST data between 2000-2022 were obtained from MODIS images. In the study, regression analyses were performed with evapotranspiration values and SSM and LST data. The best result was a medium level relationship (R 0.56) between evapotranspiration generated from SMAP Level-4 data and LST. A high correlation (R 0.87) was observed with SSM. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation data between 1981 and 2023 and Surface pressure (PS) data between 1981 and 2023 were obtained from MERRA image. Regression analyses were performed between evapotranspiration data and PS and Precipitation values. A moderate relationship (R 0.37) was observed between evapotranspiration and PS generated from MOD16A2 V105 data. A moderate relationship (R 0.55) was observed between evapotranspiration and precipitation generated from TerraClimate data. This study aims to contribute to the development of strategies to effectively manage water resources and improve agricultural sustainability by analysing evapotranspiration in various regions of Turkey. Understanding evapotranspiration processes under the climatic diversity and geographical structure of Turkey will be an important step in planning and managing the future use of water resources.

Characterization of holocellulose extracted from agricultural and food processing byproducts via ecofriendly delignification pulping process

AbstractThis study investigated an ecofriendly peracetic acid delignification process to extract holocellulose from various fiber rich plant‐based byproducts (coconut palm spathe/leaf, apple pulp, wine grape pomace, hazelnut shell, and hemp fiber/hurd), and evaluated their physicochemical, morphological, and thermophysical properties in comparison with commercial cellulose nanofibers produced from wood. Holocellulose from coconut palm spathe has long fiber (~846.7 μm), whereas that from wine grape pomace and hazelnut shell exhibits a cylinder shape of particles with short fiber length (~123.8 and ~ 71.1 μm, respectively). Biofilms cast from apple pulp and hemp fiber holocellulose have higher degradation temperature (361 and 358°C, respectively) than biofilms from other materials, thus more thermally stable. All biofilms demonstrate high UV absorbance, and the biofilm cast from apple pulp holocellulose is transparent with smooth surface. Fourier‐transform infrared spectroscopy analysis confirms the presence of cellulose and hemicellulose as the main components in the biofilms, along with traces of residual lignin that remained after the extraction process. This study demonstrates that holocellulose with varied cellulose morphology and polymeric characteristics could be extracted depending on the type of byproducts and be employed to produce sustainable packaging materials.

Exploring The Use Of Tiktok Educational Video To Increase Students’ Vocabulary At SMKN 02 Batu

Vocabulary is essential for English learners to understand the material. To increase students' interest in learning new words, medias can be used. This study aims to evaluate the practicability of TikTok as an EFL educational platform based on students' perceptions and assist educators in this regard. This study employs a qualitative method using a questionnaire and interviews to determine the number of TikTok users and to gather information from interviewees to provide a comprehensive description and elucidate the perceptions of students. Purposive sampling was used to select 27 students majoring in Agricultural Product Processing Agribusiness from SMKN 02 BATU, of which 5 were chosen for interviews. The interview session identified five themes related to students' perception of TikTok educational videos and their impact on vocabulary development: 1) content type, 2) benefits, 3) advantages, 4) weaknesses, and 5) impacts. The results revealed that students mostly learn new vocabulary effectively through TikTok since it provides a practical way to understand the meaning through real-life examples from TikTok. The study found that using media to teach English to vocational students is more effective than using textbooks, despite some students finding it distracting. This suggests that implementing TikTok educational videos in the future could be more effective than previous methods.

Vertical Farming System Based on IoT

The comprehensive analysis of an Internet of Things (IoT)-integrated vertical farming system, focusing on the monitoring and optimization of environmental parameters critical for plant growth. Real-time data on variables like flow rates, water quality, temperature, humidity, and moisture levels are provided by the system. It uses a variety of sensors, including as flow, pH, turbidity, ultrasonic, and DHT11 (Digital Humidity and Temperature Sensor) sensors, to achieve this. The system can maintain optimal growing conditions, according to sensor data insights, but they also highlight the necessity of precise irrigation control to account for differences in moisture content. The study demonstrates how IoT-driven vertical farming systems have the potential to transform conventional agricultural processes, promoting sustainability and productivity.

Estimating soil moisture content in citrus orchards using multi-temporal sentinel-1A data-based LSTM and PSO-LSTM models

Soil moisture content is a vital variable in agricultural, hydrological, ecological and climatological processes. However, susceptible to soil type, soil structure, topography, vegetation and human activities, soil moisture content exhibits strong spatial heterogeneity in spatial distribution, which makes it difficult to accurately estimate the soil moisture content distribution information at a large scale using conventional methods. To solve the problem, this study proposed a novel hybrid model (PSO-LSTM) based on the particle swarm optimization (PSO) and long short-term memory (LSTM) network model to accurately predict soil moisture content at a large scale. Five different input combinations were constructed based on the vertical polarization (VV) and cross-polarization (VH) of multi-phase Sentinel-1A data, and the soil moisture content at depths of 5 cm, 10 cm, 20 cm and 40 cm in citrus orchards were estimated using the standalone LSTM and hybrid PSO-LSTM models. The results showed that the estimation accuracy of the hybrid PSO-LSTM model was greater than that of the standalone LSTM model at different depths, with the normalized root mean square error (NRMSE) of 4.568–11.023 % and 18.056–30.156 %, respectively. With the VV polarization as the only inputs, the PSO-LSTM model obtained high prediction accuracy, with the normalized root mean square error (NRMSE) of 5.458–10.125 %, respectively. Therefore, the PSO-LSTM model with VV polarization input was recommended to estimate the soil moisture content at different depths in citrus orchards, which provides important data for decision-making on distributed precision irrigation at a large scale.

Novel intrusion detection system based on a downsized kernel method for cybersecurity in smart agriculture

Smart farming aims to ameliorate the quantity and quality of farms products using modern information and communication technologies. Agriculture has always used cutting-edge technology to improve profitability, efficiency, and safety as well as creating the opportunity for more environmentally friendly operations. In smart agriculture network communication between smart devices is necessary to managing the agriculture process. Consequently, the possibility of information being exposed to hacking is very likely, which makes the information security system extremely important and cannot be neglected. Even though many solutions have been developed to overcome cyberattacks in smart networks, some limitations are observed, such as solving multi-class problems, high feature dimensions, and overhead computation. To address the aforementioned issues, an intelligent Intrusion Detection System (IDS) for identifying cyberattacks in the Internet of Agriculture Things (IoAT) is proposed. The developed model uses a proposed reduced kernel method, the Downsized Kernel Partial Least Square (DKPLS) to extract and reduce data feature dimension to improve detection performance. The DRKPLS approach is used to reduce the dimension of a kernel matrix generated using the Kernel Partial Least Square (KPLS) technique by selecting the most important features. In classification phase, the Kernel Extreme Learning Machine (KELM) is used to classify data for binary and multi-class classification. The proposed IDS is evaluated on a new Industrial IoT Dataset, X-IIoTID. The developed approach achieved higher performances for binary classification and for multi–class classification compared to others machine learning and deep learning approaches. The proposed approach achieved an accuracy rate of 99.92% for binary classification and 99.99% for multi–class classification. Furthermore, the higher percentage in multi-class classification is obtained for the two types of attacks Ransom Denial of Service (RDoS) and Command_Control respectively.

Discovering the relationship between Agri funding and Agri contribution to GDP

One of the main goals for Bangladesh is to achieve economic growth by increasing agricultural productivity. I will be conducting research to examine how agricultural financing and contributions impact Bangladesh. The researchers collected yearly time series data from the Bangladesh Bank (BB), Ministry of Finance (MoF), and Bangladesh Bureau of Statistics (BBS) for the period of 2012 to 2022. This article employed the GMM estimation technique using SPSS to examine the short- and long-term relationships between agricultural financing and agricultural contribution, along with other control variables. The findings of the study showed that there are connections between agricultural funding and agricultural contribution in both the short-term and long-term. Additionally, agricultural productivity is affected by various dynamic factors including GDP, inflation, interest rates, and government agricultural spending. The dependent variable in this study was GDP, while the independent variable was agricultural production. Most agricultural processing sectors, along with some agricultural producing sectors, have a greater potential for making money. This research indicates that it would be beneficial to increase agricultural financing in order to support the economic development of Bangladesh.

Effectiveness of Trained Community Lay Workers on Glycemic Control, Knowledge, and Self-Efficacy Among Agricultural Workers with Diabetes in the Texas Panhandle

Systemic health barriers, economic challenges, and lack of follow-up care exacerbate self-management of chronic diseases like diabetes among Hispanic agricultural workers. The primary objective of this pilot project was to determine the benefit of using community coaches to decrease A1C levels and increase diabetes knowledge among agricultural workers with diagnosed diabetes in the Texas Panhandle. A longitudinal study design with two phases was used to create, deliver, and evaluate a diabetes coaching program. Phase 1 was the development of the program and community coaches training (n = 4). In Phase 2, the coaches then delivered the program over 12 weeks to thirteen clients. Phase 1: All coaches were Hispanic females, 28.3 (SD 3.8) years of age, half had at most a high school education level and the other half had a vocational certification (n = 4). Mean DKQ-24 score was 54.2% (SD = 29.7) at baseline and 75.0% (SD = 31.4) after training (t (4) = 4.6, P < 0.05). We observed a very large difference between mean baseline and exit DKQ-24 scores relative to the pooled standard deviation, resulting in an effect size estimate of 0.59 indicative of a medium to large learning effect. Phase 2: Clients were Hispanic Spanish-speaking, predominantly female (55%), 44.4 (SD 6.8) years of age with at most a high school level of education (88.9%) and occupations varied from dairy farm worker (33.3%), meat processing worker (33.3%), and other agriculture or manufacturing position (33.3%). The mean SKILLD score was 40.0% (SD = 28.7) at baseline, increasing to 72.2% (SD = 25.4) at 12 weeks upon completion of the coaching program (t (9) = 2.956, P < 0.05). We observed a very large difference between mean baseline and exit SKILLD scores relative to the pooled standard deviation, resulting in an effect size estimate of 1.13 indicative of a large learning effect. The mean A1C levels at baseline screening was 8.3% (SD = 3.0) and 7.6% (SD = 3.0) at exit screening, representing a 0.7% decrease (p = 0.4730). No statistically significant differences were observed between depression (p = 0.786) or anxiety (p = 1.000) measures at baseline compared to exit. Training and coaching programs for hard-to-reach agricultural and meat processing workers must be culturally, linguistically, and literacy appropriate for both coaches and clients. The program must be feasible and sustainable, focus on empowering community members, capitalize on technological advances and persisting new-normals from the COVID-19 pandemic as well as dismantle common systemic barriers to health and understanding lived-experiences of agricultural working populations in rural regions.

Special Issue “Pretreatment and Bioconversion of Crop Residues II”—Introduction to the Collection

Bioconversion in biorefineries is a way to valorize residues from agriculture and food processing. Pretreatment is an important step in the bioconversion of lignocellulosic materials, including crop residues. This Special Issue includes nine articles on several pretreatment and bioconversion approaches applied to different agricultural residues and food-processing by-products. The materials addressed in this collection cover straw from wheat, rye, and miscanthus, olive tree pruning residue, almond shells and husks, avocado waste, sweet sorghum bagasse, soybean meal, and residues of non-edible oilseeds.

Jaringan Sosial dalam Membangun Usaha Pengolahan Hasil Tani

Rowosari Village is an agrarian community with advanced agricultural and agricultural processing sectors. These sectors have been well-developed for a long time and receive support from various parties. One of the significant agricultural processing enterprises is owned by Mr. Rudi. The agricultural products processed include rice and corn. The growth of this enterprise involves many parties providing support in the form of funds and processing machines. Additionally, there is a cooperative and farmer groups established by Mr. Rudi himself, which have significantly assisted his business. The purpose of this research is to dissect the networks built within and outside of production relations and how the owner has managed to expand the agricultural processing business to its current size using Social Network theory by focusing on the concept of Embeddedness. This research uses qualitative methods with an ethnographic approach, as the focus of the study lies on the community, specifically the agricultural processing business situated in a rural society.

Development of A Robotic Maize Seed Planter

The automatic maize seed planter design to address the limitations of traditional planting techniques and reduce the lead time in agricultural processes and enhance the output. The research encompasses a comprehensive study of maize sowing techniques, challenges, and advancements in agricultural robotics. The literature review explores the limitations of traditional maize sowing techniques, such as inaccurate seed placement, labor intensiveness, and weather dependence. The construction process involves the integration of a chassis, wheels, sensors, microcontroller, and seed dispensing system. The software component utilizes a custom algorithm to control the movement and seed placement of the robot. Experimental testing and evaluation were conducted to assess the performance of the robot. The accuracy of seed placement was determined by analyzing the deviation from the desired spacing. The efficiency of the robot was measured by comparing the time taken for sowing with traditional methods. The results demonstrated that the automatic maize seed planter achieved high accuracy in seed placement with minimal deviation from the desired spacing. The results validate the effectiveness of the robotic system in addressing the limitations of traditional sowing techniques and demonstrate its potential to revolutionize the agriculture industry. The objectives are to develop a cost-effective, efficient, and accurate maize seed planter and implement the design in the fabrication. Recommendations for further work is real-time mapping and monitoring. Real-time mapping and monitoring of the field can help the robot navigate the field more efficiently and accurately.