Harvest Cycle Research Articles

Evaluating the agronomic and economic benefit of including spinosad with and without pyrethroid insecticides in bermudagrass stem maggot treatments

AbstractThe bermudagrass stem maggot (BSM; Atherigona reversura Villeneuve) is known to have a detrimental effect on bermudagrass (Cynodon spp.). Currently, two strategically timed pyrethroid applications are recommended for BSM suppression in each harvest cycle. However, producers are interested in applying spinosad because of its residual effects for other insects or reducing the number of pyrethroid applications to cut input costs. Therefore, the objective of this study was to determine the agronomic and economic implications of one or multiple pyrethroid (zeta‐cypermethrin) and/or spinosad applications on ‘Alicia’ and ‘Tifton 85’ bermudagrasses. Generally, zeta‐cypermethrin treatments resulted in a greater herbage accumulation compared to the untreated control in both cultivars. Regardless of cultivar, spinosad only treatments did not improve upon the herbage accumulation observed in the untreated control. Crude protein and total digestible nutrients were not affected by insecticide treatments in either cultivar. Finally, two zeta‐cypermethrin applications resulted in greater net profit compared to other insecticide treatments. These data illustrate that there is not yet an alternative for two pyrethroid applications for BSM suppression. Ongoing research and breeding efforts are focused on BSM tolerance and reduced pyrethroid usage to prevent resistance to the insecticide.

Electrochemical kinetic energy harvesting mediated by ion solvation switching in two-immiscible liquid electrolyte

Kinetic energy harvesting has significant potential, but current methods, such as friction and deformation-based systems, require high-frequency inputs and highly durable materials. We report an electrochemical system using a two-phase immiscible liquid electrolyte and Prussian blue analogue electrodes for harvesting low-frequency kinetic energy. This system converts translational kinetic energy from the displacement of electrodes between electrolyte phases into electrical energy, achieving a peak power of 6.4 ± 0.08 μW cm−2, with a peak voltage of 96 mV and peak current density of 183 μA cm−2 using a 300 Ω load. This load is several thousand times smaller than those typically employed in conventional methods. The charge density reaches 2.73 mC cm−2, while the energy density is 116 μJ cm−2 during a harvesting cycle. Also, the system provides a continuous current flow of approximately 5 μA cm−2 at 0.005 Hz for 23 cycles without performance decay. The driving force behind voltage generation is the difference in solvation Gibbs free energy between the two electrolyte phases. Additionally, we demonstrate the system’s functionality in a microfluidic harvester, generating a maximum power density of 200 nW cm−2 by converting the kinetic energy to propel the electrolyte through the microfluidic channel into electricity.

Regulating the Hydrophilicity of Hyper-Cross-Linked Polymers via Thermal Oxidation for Atmospheric Water Harvesting.

We explore the thermal oxidation of hyper-cross-linked polymers to enhance their hydrophilicity and efficacy in atmospheric water harvesting. Comprehensive chemical and physical characterizations are used to confirm the successful incorporation of polar oxygen moieties and the preservation of porosity upon thermal treatment. Newly introduced oxygen-based functional groups significantly improve water sorption properties, increasing total water uptake capacities by up to 400% and shifting water uptake onsets to significantly lower relative humidity. We also investigate the regeneration of oxidized hyper-cross-linked polymers after water sorption to probe their potential for multiple water harvesting cycles and reuse. Our findings outline a simple and cost-effective postsynthetic modification route for optimizing porous organic polymers for more sustainable and efficient atmospheric water harvesting.

A low-impact nature-based solution for reducing aquatic microplastics from freshwater ecosystems

Effective nature-based solutions (NBS) and strategies for freshwater microplastic (MP) pollution are beneficial for reducing ecological and human health risks. This study proposed an innovative NBS for the in-situ retention of aquatic MPs. By evaluating the tolerance and MP retention efficiency of different submerged macrophytes, Myriophyllum aquaticum was identified as a well-suited system for optimization as NBS for operational MP retainment practice. The response surface method and artificial neural network modeling were applied to determine the optimal operational strategy of this solution, which was determined to be at a flow rate of 60 L/h, aeration intensity of 5 m3/(m2·h), and plant density of 190 plants/m2. Under this strategy, an average MP retention of 93.38% was achieved for the actual tested lake. The retention of MPs was particularly effective for particle sizes larger than 100 μm (especially films and fragments) and for the 4 polymer types. At the same time, also total nitrogen and phosphorus levels in the treated waters were reduced by 80.0% and 78.4% respectively, reflecting the added environmental value for water purification. This NBS provides a feasible strategy for mitigating MP pollution, but further research is needed on its long-term applicability and potential ecological effects in a wider range of specific environments, and effective development of plant harvesting cycle strategies is also essential to achieve long-lasting MP pollution removal.

Green mould contamination of Pleurotus pulmonarius cultivation in Malaysia: Unravelling causal agents and water source as critical factors

Green mould contamination causes a significant challenge to mushroom growers in Malaysia leading to reduced yields and economic losses in the widely cultivated and marketed edible grey oyster mushroom, Pleurotus pulmanorius. This study aimed to identify the causal agents of green mould contaminants and determine the critical points in the cultivation process in the farm that contribute to green mould contamination. Samples of mushroom substrate (sawdust), spawn substrate (corn), environmental sources and tools were collected at different stages of mushroom cultivation. As results, the causal agents of green mould contamination were identified as Trichoderma pleuroti, T. harzianum and T. ghanese. Prior to steam pasteurisation and after steam pasteurisation, the spawn substrate and mushroom substrate were found to be free of Trichoderma. However, Trichoderma was detected in water, air within the production house and on cleaning tools. This findings suggests that water could serve as the source of green mould introduction in mushroom farms, while cultivation practices such as watering and scratching during the harvesting cycle may contribute to adverse green mould. Understanding these critical points and causal agents provides information to mitigate the green mould contamination throughout the grey oyster mushroom cultivation process.

Abiotic Stress Effect on Agastache mexicana subsp. mexicana Yield: Cultivated in Two Contrasting Environments with Organic Nutrition and Artificial Shading.

Research on medicinal plants is essential for their conservation, propagation, resistance to environmental stress, and domestication. The use of organic nutrition has been demonstrated to improve soil fertility and plant quality. It is also important to study the effects of the Basic Cation Saturation Ratio (BCSR) approach, which is a topic where there is currently controversy and limited scientific information. Evaluating the growth and yields of Agastache mexicana subsp. mexicana (Amm) in different environments is crucial for developing effective propagation and domestication strategies. This includes examining warm and subhumid environments with rain in summer in comparison to mild environments with summer rain. Significant differences were observed in the effects of cold, waterlogging, and heat stresses on the plant's biomass yield and the morphometric-quantitative modeling by means of isolines. The biomass yield was 56% higher in environment one compared to environment two, 19% higher in environment one with organic nutrition, and 48% higher in environment two with organic nutrition compared to using only BCSR nutrition. In the second harvesting cycle, the plants in environment one did not survive, while the plants in environment two managed to survive without needing additional nutrition. Statistical and mathematical analyses provided information about the population or sample. Additionally, further analysis using isolines as a new approach revealed new insights into understanding phenology and growth issues.

The Potential of Building Information Modeling in Agricultural Operations

Building Information Modeling (BIM) has been widely adopted in the construction industry, providing a virtual representation of physical objects and systems. In the context of agriculture, BIM can be applied to monitor and manage the physical and operational characteristics of farms and agricultural facilities using BIM digital twins. A BIM digital twin is a virtual representation of a physical object or system that is based on data from BIM models and other sources. The integration of sensors is essential in creating a BIM digital twin as they provide real-time data that can be used to update and maintain the virtual representation. The use of BIM digital twins in agriculture has the potential to improve the management of agricultural facilities, reduce environmental pollution, and promote the sustainability of agricultural land. Sensors can be used in agriculture to monitor various parameters such as soil moisture, temperature, and nutrient levels. BIM digital twins can also be used to monitor and manage sources of pollution, such as the use of pesticides and fertilizers, and to develop strategies to minimize their impact on the environment. BIM digital twins provide a comprehensive view of the condition of agricultural land and the factors that influence it, such as weather patterns, industry activities, and human activities. 4D BIM is an extension of traditional 3D BIM that adds a time dimension to the model. In the context of agriculture, 4D BIM can be used to visualize and simulate various processes related to time and soil management, such as planting and harvesting cycles, and to evaluate the impact of different planting strategies on crop yields. 4D BIM can also be used to model the impact of weather patterns on crop growth and to develop contingency plans for weather- related events. The use of BIM in agriculture has the potential to transform the industry by providing a new level of insight and control over agricultural operations. BIM models can be used to simulate and visualize different scenarios for agricultural facilities, such as the use of water, fertilizer, and energy, leading to improved efficiency and reduced waste. By using BIM to optimize operations, agricultural companies can increase their productivity and profitability. The integration of BIM digital twins and 4D BIM in agriculture has the potential to revolutionize the industry by providing new tools for monitoring and managing agricultural operations. The use of BIM digital twins and 4D BIM can help reduce environmental pollution, promote sustainability, and improve the efficiency and productivity of agricultural operations. Testing is needed to fully understand the potential benefits of BIM and BIM digital twins in agriculture and to develop best practices for their implementation.

A novel approach to improve double-tube thermal energy storage: Impacts of inner tube twisting and its location

Thermal energy storage (TES) systems are a crucial component of solar energy harvesting cycles. Our objective in this study is to enhance the efficiency of a double-tube TES that utilizes lauric acid, a phase change material (PCM), and heated water as a heat transfer fluid (HTF). For the first time, this study examined the effects of utilizing a twisted inner tube inside a double-tube TES containing HTF. The finite volume method based on the enthalpy porosity model was used to investigate the effects of three different cross-sectional aspect ratios (AR = 0.5, 0.75, and 1) of the inner tube, at three different normalized locations along the vertical axis (Y = 0, −0.2, and − 0.4). The results were evaluated based on liquid fraction, temperature, velocity, total melting time, and average Nusselt number. The obtained results revealed that the AR effect of the inner tube varies depending on its location along the Y-axis. It is established that placing the tube at lower Y values in cases where a helical tube is present will yield significantly better results than higher Y values. Finally, the best case with the lowest total melting time occurred when the inner tube was located at Y = -0.4 and AR = 0.5. This case resulted in a total melting time decrease of up to 63 % compared to the reference case (AR = 1 at Y = 0).

Mule deer selection of fuel reductions is restricted by site fidelity and structured by circadian and seasonal patterns

Forests in the western US have undergone a profound transformation over the last 100 years due to chronic fire suppression and a cycle of extensive timber harvest followed by little silvicultural activity. Forested landscapes in this region are now dominated by intermediate age, denser stands of coniferous trees that reduce transmission of light and precipitation to the understory, with potential consequences for ungulate nutrition. We used a 20-year dataset of mule deer locations (560 animal-years of data) to evaluate long-term patterns of selection of forest stands that had been thinned and burned compared to untreated reference stands. We evaluated within home-range (3rd order) selection at 3 temporal scales (hourly, daily, annually) and landscape-scale (2nd order) use annually. We found that annual selection of treated stands during summer generally increased with two peaks, once immediately after treatments were applied and again 15 years later. Mule deer also showed strong seasonal and temporal patterns in selection of treated forest stands. Deer selected treated stands at night in July and August immediately after treatments. After 4–6 years, mule deer selected treated stands both during the day and at night in May, July, and August, but with stronger selection at night. We hypothesize that preference for treated forest stands were driven by differences in forage quantity and quality among forest stands. Vegetation surveys during years of peak selection showed that treated stands had significantly higher biomass and digestible energy of neutrally selected forage and lower biomass of avoided forage species, although there was no difference in biomass of preferred forage species. A subset of female mule deer with multiple years of data did not shift home ranges to overlap with treated forest stands over time (2nd order use) but did show increasing selection over time for treated forest stands within their home ranges (3rd order). Our results show that thinning and burning of forest stands can benefit mule deer both immediately and for up to 15 years after treatment and that researchers should evaluate temporal and seasonal patterns in studies of habitat selection. Given the diverse habitat needs of mule deer, treatments would be most beneficial if they were scattered across large landscapes and staggered in time to provide a diversity of forage and cover types.

Phosphate fertilization to improve yield and chemical composition of banana ‘BRS SCS Belluna’ fruit

Phosphate fertilization improves banana yields, but due to the physiological effects of phosphorus on plants, it can also be used to improve the physical and chemical characteristics of the fruits, which can contribute to increasing the processing of green bananas and reducing losses in the production chain. Herein, the productivity of bunches and fruit, the biometric analysis of the fruit, dry matter content, and the chemical composition of immature fruit were evaluated when the cultivar BRS SCS Belluna (AAA) was grown under different levels of phosphate fertilization for two harvest cycles. The results showed that phosphate fertilizer levels interfered with bunch yield and fruit mass in both cycles, leading to yield gains of 44.3% (1st cycle) and 84.0% (2nd cycle) in bunches and 51.8% (1st cycle) and 81.9% (2nd cycle) in fruits. Fertilization promoted gains of up to 33.5% in fruit mass and enhanced the resistant starch content in the pulp, leading to an increase of 15.1%. The phosphorus, potassium, and calcium contents in green fruits increased with fertilizer levels in the first cycle, improving nutritional quality. These results have useful applications because they indicate that adequate management of phosphate fertilization can increase the potential of the cultivar for industrial processing of green fruits and the nutritional quality of the derived products.

Metabolomic analysis of umami taste variation in Pyropia haitanensis throughout the harvest cycle

Porphyra sensu lato, a highly valuable edible seaweed renowned for its distinctive umami taste, undergoes significant taste variations during the harvest cycle, affecting product quality and pricing. In this study, umami-related metabolites in Pyropia haitanensis were investigated at different harvesting times using GC–MS metabolomic, targeted LC-MS analysis, and an electronic tongue taste evaluation. High concentrations of compounds, including glutamic acid, aspartic acid, and inosine 5′-monophosphate, were identified as the main contributors to the overall umami profile. The concentrations of the compounds and umami-enhancing substances, such as sugars, were negatively correlated as the harvesting period extended. The early harvested P. haitanensis exhibited a superior umami taste, which gradually decreased with subsequent harvest time. Proline, a known cold-resistance metabolite, accumulated as the seawater temperature decreased and the harvest period progressed. These findings provide insights into the optimal cultivation and harvesting practices for maintaining umami quality in P. haitanensis products.

3D Camera and Single-Point Laser Sensor Integration for Apple Localization in Spindle-Type Orchard Systems.

Accurate localization of apples is the key factor that determines a successful harvesting cycle in the automation of apple harvesting for unmanned operations. In this regard, accurate depth sensing or positional information of apples is required for harvesting apples based on robotic systems, which is challenging in outdoor environments because of uneven light variations when using 3D cameras for the localization of apples. Therefore, this research attempted to overcome the effect of light variations for the 3D cameras during outdoor apple harvesting operations. Thus, integrated single-point laser sensors for the localization of apples using a state-of-the-art model, the EfficientDet object detection algorithm with an mAP@0.5 of 0.775 were used in this study. In the experiments, a RealSense D455f RGB-D camera was integrated with a single-point laser ranging sensor utilized to obtain precise apple localization coordinates for implementation in a harvesting robot. The single-point laser range sensor was attached to two servo motors capable of moving the center position of the detected apples based on the detection ID generated by the DeepSORT (online real-time tracking) algorithm. The experiments were conducted under indoor and outdoor conditions in a spindle-type apple orchard artificial architecture by mounting the combined sensor system behind a four-wheel tractor. The localization coordinates were compared between the RGB-D camera depth values and the combined sensor system under different light conditions. The results show that the root-mean-square error (RMSE) values of the RGB-D camera depth and integrated sensor mechanism varied from 3.91 to 8.36 cm and from 1.62 to 2.13 cm under 476~600 lx to 1023~1100 × 100 lx light conditions, respectively. The integrated sensor system can be used for an apple harvesting robotic manipulator with a positional accuracy of ±2 cm, except for some apples that were occluded due to leaves and branches. Further research will be carried out using changes in the position of the integrated system for recognition of the affected apples for harvesting operations.

Morpho-Physiological Traits and Oil Quality in Drought-Tolerant Raphanus sativus L. Used for Biofuel Production.

Raphanus sativus L. is a potential source of raw material for biodiesel fuel due to the high oil content in its grains. In Brazil, this species is cultivated in the low rainfall off-season, which limits the productivity of the crop. The present study investigated the effects of water restriction on the physiological and biochemical responses, production components, and oil quality of R. sativus at different development stages. The treatments consisted of 100% water replacement (control), 66%, and 33% of field capacity during the phenological stages of vegetative growth, flowering, and grain filling. We evaluated characteristics of water relations, gas exchange, chlorophyll a fluorescence, chloroplast pigment, proline, and sugar content. The production components and chemical properties of the oil were also determined at the end of the harvest cycle. Drought tolerance of R. sativus was found to be mediated primarily during the vegetative growth stage by changes in photosynthetic metabolism, stability of photochemical efficiency, increased proline concentrations, and maintenance of tissue hydration. Grain filling was most sensitive to water limitation and showed a reduction in yield and oil content. However, the chemical composition of the oil was not altered by the water deficit. Our data suggest that R. sativus is a drought-tolerant species.

Aplikasi Pestisida Nabati pada Budidaya Tanaman Cabai Merah (Capsium Annum L) di Desa Ujung Bandar, Kabupaten Langkat

Chili has become a necessity for Indonesian people as an ingredient used as a cooking spice, thus making the demand for chili on the market very high. However, high market demand is often not matched by the supply of chilies on the market which is not sufficient to meet market needs. The lack of availability on the market is caused by several factors, one of which is crop failure caused by diseases and pests of chili plants. The first method of land processing is to loosen the land until it is fertile, after that it is sown with compost, usually with chicken, cow, and goat pui. After that, it is sprinkled and watered using a composter. The fertilizers used are synthetic and organic chemicals such as humic acid, trikoderma, PSE, and micro barter. Usually, the harvest produces an average of 300-500 grams per tree per cycle, not reaching 1 kg per tree in the harvest cycle. And some of the harvest is consumed and some is sold, and is usually sold through middlemen and to the market.

Impact of Climate Change on the Farming Community of Bhaktapur District of Nepal

This study was carried out to evaluate the impact of climate change on the farming community in Bhaktapur, Nepal. The study considered rainfall, temperature, flood/drought, harvest and sowing time, and pest and disease incidence over 20 years as primary factors to be considered. A household survey of 120 houses was conducted following the set of questionnaires prepared. As a result, significant changes were observed in climatic aspects as well as biological aspects. The obtained result demonstrated that the precipitation observed drastic change as there was an incidence of erratic and heavy rainfall on summer days while the intensity and frequency of rainfall decreased during winter. These abnormalities certainly do not favor agriculture and farming. Similarly, the temperature of the study area has increased annually over the last two decades. The data suggests that the average annual temperature is increasing at the rate of 0.038°C per year. Moreover, the survey showed that the time of harvest and time of sowing seeds have been delayed respectively to the time a few decades back while the climatic aberrations have increased the incidence of pests and diseases. In a nutshell, the changes in climatic conditions have a drastic change in the farming system and thus the agricultural produce and productivity. The study highlights the significant impact of climate change on traditional farming practices in Bhaktapur, Nepal. It underscores how changes in rainfall patterns, temperature, and the incidence of extreme weather events like floods and droughts have disrupted agricultural activities such as planting and harvesting cycles. The research not only identifies challenges but also emphasizes the need for adaptation strategies to mitigate the impacts of climate change on farming communities. By linking research findings to policy recommendations and practical interventions, the study offers actionable insights for policymakers, practitioners, and local stakeholders striving to build resilience in agricultural systems.

Nighttime Harvesting of OrBot (Orchard RoBot)

The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 s. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted in images with the lowest noise. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94% compared to 88% during the daytime operations.

Smart Agriculture Automation System using ML

Abstract: The "Smart Agriculture Automation System using Machine Learning" project envisages a future where farming transcends manual labor and embraces data-driven automation. In response to the escalating demand for food amidst a burgeoning global population, traditional farming methods are rendered insufficient. However, Smart Agriculture Automation Systems offer a transformative solution by enhancing crop production efficiency and maintaining yield quality. Climate changeinduced challenges such as erratic weather patterns and heightened pest attacks further underscore the necessity for innovation in agriculture. At the heart of this system lies real-time sensor data, enabling precision farming practices that optimize resource utilization while fostering environmental sustainability. Leveraging Machine Learning algorithms, the system not only predicts and mitigates agricultural challenges but also detects early signs of crop diseases and nutrient deficiencies. This proactive approach ensures higher yields minimizes manual tasks and minimizes wastage. The integration of IoT technology enables remote monitoring and seamless data exchange, unlocking capabilities such as precise crop prediction, weather forecasting, and fertilizer recommendations. By analyzing historical data alongside current environmental factors, these systems accurately forecast crop yields and offer insights for informed decision-making in planting and harvesting cycles. Smart Agriculture Automation Systems embody adaptability, sustainability, and accessibility, heralding a new era of intelligent and efficient modern farming. This project integrates the Internet of Things (IoT) sensors and an innovative marketplace to revolutionize the agricultural landscape. Through this system, we aim to empower farmers with data-driven insights,

Biodegradable mulching materials for agriculture: technology and environmental safety

Soil mulching is a common agricultural technique that reduces moisture loss, suppresses weeds, regulates soil temperature, and generally increases yield. Traditionally, low-density polyethylene is used for these purposes due to its mechanical and barrier properties, resistance to all forms of degradation, simple processing of cover soil, and low cost. However, using polyethylene has several artificial and environmental risks associated with difficult biodegradability, the difficulty of its removal after the harvest cycle, and final disposal. Therefore, there is great interest in the use of biodegradable mulch films. Two groups of materials most suitable for producing biodegradable coatings are polysaccharides of natural origin and biopolymers of synthetic origin. Technological methods for producing biopolymers from raw natural material by extraction from biomass and chemical or biotechnological methods are analyzed. Data are presented on the potential of biodegradable mulch materials of natural origin, films based on polysaccharides, synthetic polymers, and those applied as a solution for possible replacement of traditional mulch materials. A comparison was made of the compositions of biodegradable coatings, their properties, and their effects when used. The prospects and ways of introducing biodegradable film materials into agricultural practice are summarized.

Willow, Poplar, and Black Locust Debarked Wood as Feedstock for Energy and Other Purposes

Solid biomass can be used for energy generation and the production of various renewable bioproducts. The aim of this study was to determine the yield and characteristics of wood obtained as debarking residue from 14 genotypes of short-rotation woody crops (SRWCs). These included five Populus genotypes, one Robinia genotype, and eight Salix genotypes, harvested in both annual and quadrennial cycles. The results showed that the highest dry wood yield (12.42 Mg ha−1 y−1 DM) and yield energy value (244.34 GJ ha−1 y−1) were obtained from willow (cultivar Żubr) harvested in a quadrennial cycle. The best effect among the poplar genotypes was achieved for the Hybryda275, and it was particularly marked in the quadrennial harvest cycle. The poorest results were determined for black locust. The Robinia characteristics included the significantly lowest moisture content (31.6%), which was a positive attribute from the energy point of view, but, on the other hand, it had some adverse characteristics—the highest levels of sulfur (0.033% DM), nitrogen (0.38% DM), and ash (0.69% DM). More beneficial properties in this respect were determined for willow and poplar wood. Moreover, willow and poplar wood contained more cellulose—51.8 and 50.0% DM, respectively—compared with black locust. Extending the SRWC shoot harvest cycle from annual to quadrennial resulted in an increase in cellulose, lignin, and carbon, higher heating value, and a decrease in nitrogen, sulfur, ash, and moisture content. Therefore, extending the harvest cycle improved the parameters of SRWC wood as an energy feedstock.

Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification

This research paper explores a comprehensive approach to advancing capsicum harvesting by integrating cutting-edge technologies. The study addresses key objectives, including capsicum detection using various YOLO algorithms, peduncle detection through YOLO segmentation models in a proposed robotic harvester, laboratory testing of cutting target point coordinates using the Real Sense D455 RGB-D camera, growth stage determination, and capsicum counting/tracking with a supervision algorithm. The investigation highlights the YOLOv8s model as the most successful for capsicum detection, achieving a remarkable mean Average Precision (mAP) of 0.967 at a 0.5 Intersection over Union (IOU) threshold. As part of the growth stage determination task, YOLOv8s achieved a satisfactory mAP of 0.614 at the same IOU threshold. Additionally, the YOLOv8s-seg model demonstrated superior performance in peduncle detection, attaining a box mAP of 0.790 and a mask mAP of 0.771. The YOLOv8s-seg model excels in peduncle detection with a box mAP of 0.790 and a mask mAP of 0.771. Laboratory experiments using the Real Sense D455 RGB-D camera showcased its capability to localize the target point with a maximum error of 8 mm longitudinally, 9 mm vertically, and 12 mm laterally. The developed tracking and counting algorithm achieve a notable counting accuracy of 94.1 % during the third harvesting cycle in the greenhouse. The Android application developed demonstrated robust performance, achieving high accuracy (Mean IoU: 0.92), precise localization (Mean Euclidean Distance: 5 pixels), responsive user interface (Touch Response Time: 150 ms), and broad compatibility across Android versions and device types, with effective error handling (Success Rate: 95 %). The study not only advances capsicum harvesting techniques but also presents practical insights for the integration of advanced technologies, paving the way for efficient robotic harvesting systems in agriculture.