Orchards Research Articles

Effects of paraquat dichloride on lungs of pregnant wistar rats

Paraquat dichloride is a toxic bipyridine herbicide widely used for weed control in fruit orchards and forestry. Its fumes primarily affect the lungs due to preferential accumulation in lung alveolar cells. Aim: This study investigates paraquat toxicity in pregnant Wistar rats at varying doses over a three-week gestational period. Methods: A total of seventy-two Wistar rats were used, with 48 females entering the experimental phase after mating with males. The female rats were randomly divided into four groups: Group 1 (Normal female, n=12), Group 2 (1 mg/ml/kg bw of paraquat dichloride, n=12), Group 3 (2.5 mg/ml/kg bw, n=12), and Group 4 (5 mg/ml/kg bw, n=12). Tissues were examined weekly and prepared for histological analysis using a Primo Star ZEISS light microscope. Results: At the end of the third week, lung tissue sections exhibited varying degrees of injury, including mild inflammation, taller and slender respiratory epithelium indicating stress, bronchiolar dilation, and fibrotic changes in lung stroma. Notably, the lungs of the foetuses showed no significant histological differences compared to controls. Conclusion: Maternal exposure to paraquat during pregnancy results in dose-dependent lung inflammation and fibrosis. Recommendations for new policies to prevent perinatal maternal exposure to paraquat are warranted.

Precision heating strategy based dynamic heater path planning for frost protection in apple orchards

Precision heating strategy based dynamic heater path planning for frost protection in apple orchards

Apple tree architectural trait phenotyping with organ-level instance segmentation from point cloud

Three-dimensional (3D) plant phenotyping techniques measure organ-level traits effectively and provide detailed plant growth information to breeders. In apple tree breeding, architectural traits can determine photosynthesis efficiency and characterize the developmental stages of trees. The overall goal of this study was to develop a deep learning-based organ-level instance segmentation method to quantify the 3D architectural traits of apple trees. This study utilized PointNeXt for the semantic segmentation of apple tree point clouds, classifying them into trunks and branches, and benchmarked its performance against several competitive models, including PointNet, PointNet++, and Point Transformer V2 (PTv2). A cylinder-based constraint method was introduced to refine the semantic segmentation results. Next, the branches were identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The type of 3D skeleton vertices determined whether a cluster represented a single branch or multiple branches. If multiple, a graph-based technique further separated them. This study also directly applied the instance segmentation model SoftGroup++ to the apple tree point clouds and analyzed the segmentation results on the apple tree dataset. Finally, seven architectural traits of apple trees were extracted, including height, volume, and crown width of the tree, as well as height and diameter for the trunk, and length and count for the branches. The experimental results showed that the post-processed mIoU values for PointNet, PointNet++, PTv2, and PointNeXt were 0.8495, 0.8535, 0.9500, and 0.9481, respectively. The final instance segmentation results based on SoftGroup++ and PointNeXt achieved mAP_50 of 0.815 and 0.842, respectively. For traits such as tree height, trunk length and diameter, branch length, and branch count, the method based on PointNeXt achieved R2 values of 0.987, 0.788, 0.877, 0.796, and 0.934, with mean absolute percentage errors of 0.86 %, 2.17 %, 5.93 %, 10.24 %, and 13.55 %, respectively. The segmentation results of PTv2 and SoftGroup++ were also used to extract the phenotypic traits of apple trees, achieving results comparable to those of PointNeXt. The proposed method demonstrates a cost-effective and accurate approach for extracting the architectural traits of apple trees, which will benefit apple breeding programs as well as the precision management of apple orchards.

An apple fruit localization system based on accurate and flexible hand-eye pose acquisition for robotic harvesting

The accurate localization of fruits and vegetables is crucial for robotic harvesting systems, depending not only on the localization accuracy within the vision device frame but also on the precision of the hand-eye pose. Despite considerable research focusing on localization accuracy within the vision device frame, limited attention has been directed toward improving the accuracy of the hand-eye pose. Furthermore, current methods for acquiring an accurate hand-eye pose are often inflexible and difficult to apply for the users. Therefore, we propose an accurate and flexible hand-eye pose acquisition method to enhance the performance of an apple fruit localization system. This method begins by attaching a datum block with three marked points to the camera, and the camera pose relative to the datum block is then determined using an iterative least-squares algorithm in the proposed calibration system. Once calibrated, the camera with the attached datum block can be mounted on a harvesting robot. In this manner, users can easily acquire the hand-eye pose by introducing the robotic arm of the harvesting robot along with a probe to contact the marked points on the datum block, making the process more flexible and accessible. To validate the effectiveness of the proposed hand-eye acquisition method, extensive experiments were conducted using a self-designed apple harvesting robot. Comparative experiments demonstrated that our method significantly improved the accuracy of the fruit localization system, reducing the mean positioning error by at least 33.3% compared to the selected state-of-the-art methods. Overall, the system achieved a mean localization accuracy of 14.0 mm under non-occluded conditions, with illuminance levels ranging from 8 to 20 kLux in a simulated orchard environment. Furthermore, field evaluation experiments in an apple orchard revealed that the localization success rate of the system exceeded 80.53%.

Understanding the role of moths (Insecta: Lepidoptera) as pollinators of apple (Malus domestica)

ABSTRACT Given their economic and nutritional value, apples are among the most valuable crops. However, apple production requires cross-pollination. Climate change and various human activities have decreased pollinator diversity in apple orchards, leading many farmers to use honeybee colonies to increase productivity. Studying the pollinators of apple crops is crucial for future pollinator management. While diurnal pollination of apple flowers has been explored, the role of nocturnal pollinators in general and moths in particular has not been thoroughly studied. To assess the role of moths in apple pollination, we studied the effectiveness of moths and other nocturnal flower visitors in depositing pollen grains on the stigma of apple flowers (single-visit pollen deposition) at Shergaon Apple Garden, Arunachal Pradesh (East Himalaya, India). The mean single-visit pollen deposition on apple flowers by nocturnal flower visitors is calculated to be 112.92 ± 10.98. Among all nocturnal flower visitors, moths are the most effective pollen depositors with a mean single-visit pollen deposition of 152 ± 19.9. Thirty moth species are found to carry apple pollen on their proboscides. Of them, three species Nyctemera arctata Walker, 1856 (Erebidae), Pyrrhia umbra (Hufnagel, 1766) (Noctuidae) and Chiasmia azataria (Swinhoe, 1893) (Geometridae) are found to be the highest carriers of apple pollen load. This study indicates that moths are the most significant for nocturnal pollination of apple.

Analysis of Trend, Growth, and Instability Index in Output, Harvested Area, and Yield of Oil Palm Fruit in Nigeria

The research examined the trend, growth rate, and instability index related to the output, harvested area, and yield of oil palm fruit in Nigeria. Utilizing secondary data sourced from the Food and Agricultural Organization (FAO) covering the period from 1961 to 2022, the study revealed inconsistent patterns in the trend of output, harvested area, and yield of oil palm fruit across the specified timeframes. The compound growth rates identified were 1.11% for output, 1.03% for harvested area, and 0.08% for yield. The Cuddy-Della Valle instability index (CDI) and the Coppock Instability Index (COI) were calculated at 13.87 and 46.57 for output, 13.87 and 46.02 for harvested area, and 2.52 and 37.88 for yield, respectively. These instability indices were relatively low, indicating limited activity within the oil palm fruit sub-sector in Nigeria. A decomposition analysis of the total effect of the output showed that the area effect accounted for 94.20%, the yield effect for 3.19%, and the interactive effect for 2.61%. The findings indicate that the area effect is the predominant factor driving the growth of oil palm fruit production in Nigeria. Consequently, it is recommended that various programs such as; small grower schemes, oil palm farmers’ cooperatives, and off-takers scheme be implemented within the sub-sector to stimulate increased activities, production, and yield. Also, the oil palm fruit farmers should be provided with quality seeds and financial resources in addition to broadening research efforts aimed at developing high-yield varieties.

Computer simulation of the soil water regime under an apple orchard in a mountainous area (using the example of the leached chernozem of the experimental agricultural station Gorno-Altaiskoe)

Relevance. Progressive degradation of farmland soils – erosion, salinization, desertification. Aim. To establish features of water-physical properties of leached chernozem under an apple orchard and associated patterns of soil profile moisture distribution in summer period when precipitation deficiency observed. Methods. Moisture content in soil horizons was determined by the gravimetric method. To determine the granulometric composition of soils, the pipette method was used. The soil bulk density was determined by the cutting ring method. For solid phase density determination the pycnometric method was used. The method of graphic interpolation was used for transition the soil texture classification from Russian to international one. Water infiltration modeling into the soil profile was carried out using the HYDRUS-1D program. The parameters of approximating of the water retention curve of soils by the Van Genuchten equation were obtained by the calculation method of pedotransfer functions «Rosetta Lite» of the RETC program, using data on soil density and soil texture. Results. It has been established that the upper well-structured horizons of leached chernozem under an apple orchard have a greater water-holding capacity; their water-retention curves are more flattened. The water retention curve of the more structureless B2k horizon, the soil texture of which is dominated by sandy fractions, is shifted towards lower humidity. During hot and dry summer period the moisture deficiency in the soil was noted only in the upper twenty-centimeter soil column (14 mm). The moisture of the lower horizons, where the bulk of tree roots are concentrated, is satisfactory. The model demonstrates the smallest discrepancies with the moisture values measured during the soil reclamation experiment one day after the start. The downward moisture penetration into the lower soil layers is prevented by low pressure in the clayey A and AB horizons. Selection of input parameters and debugging of the model based on experimental data allows it to be used to simulate further processes occurring in the soil. It was revealed that the formation of positive (upward) water flows, which ensure the “pull-up” of salts and substances from the lower part of the profile to the upper layers during dry periods of the year, has a significant impact on moisture distribution in the chernozem under the apple orchard.

Canopy Segmentation of Overlapping Fruit Trees Based on Unmanned Aerial Vehicle LiDAR

Utilizing LiDAR sensors mounted on unmanned aerial vehicles (UAVs) to acquire three-dimensional data of fruit orchards and extract precise information about individual trees can greatly facilitate unmanned management. To address the issue of low accuracy in traditional watershed segmentation methods based on canopy height models, this paper proposes an enhanced method to extract individual tree crowns in fruit orchards, enabling the improved detection of overlapping crown features. Firstly, a distribution curve of single-row or single-column treetops is fitted based on the detected treetops using variable window size. Subsequently, a cubic spatial region extending infinitely along the Z-axis is generated with equal width around this curve, and all crown points falling within this region are extracted and then projected onto the central plane. The projecting contour of the crowns on the plane is then fitted using Gaussian functions. Treetops are detected by identifying peak points on the curve fitted by Gaussian functions. Finally, the watershed algorithm is applied to segment fruit tree crowns. The results demonstrate that in citrus orchards with pronounced crown overlap, this novel method significantly reduces the number of undetected trees with a precision of 97.04%, and the F1 score representing the detection accuracy for fruit trees reaches 98.01%. Comparisons between the traditional method and the Gaussian fitting–watershed fusion algorithm across orchards exhibiting varying degrees of crown overlap reveal that the fusion algorithm achieves high segmentation accuracy when dealing with overlapping crowns characterized by significant height variations.

Flight phenology and influence of region and habitat on the abundance of Xylosandrus germanus and Anisandrus maiche (Coleoptera: Curculionidae: Scolytinae) in New York.

The non-native wood-boring and symbiotic fungus-culturing Xylosandrus germanus (Blandford) was first reported in New York apple orchards in 2013. Trapping surveys have been conducted annually since to assist growers in timely applications of preventative control measures. In 2021, a similar-looking introduced species, Anisandrus maiche (Kurentsov), was identified in traps in west central New York. Anisandrus maiche was first recorded in 2005 in Pennsylvania but its history in New York was unclear due to potential misidentification. We collected and identified ambrosia beetles using ethanol-baited bottle traps in 2022 and 2023 in New York at 2 commercial apple orchards near Lake Ontario and 2 cider apple orchards in the lower Finger Lakes district. Traps were placed in a forest interior, the forest edge, and the orchard edge at each site. Xylosandrus germanus was trapped from mid-April into early October; it was abundant in the Lake Ontario region but less so in the Finger Lakes. In contrast, counts of A. maiche were very high in the Finger Lakes but extremely low near Lake Ontario. It was trapped from late-May to mid-September. Most other bark and ambrosia beetle species were uncommon. Captures of X. germanus and A. maiche were generally highest in the forest interior and declined toward the orchard edge, but each species was usually present in traps across habitats at the same time. Thus, the practice of trapping at forest edges should continue. Both species can potentially infest stressed trees, including in orchards, throughout the growing season.

Remote Sensing-Assisted Estimation of Water Use in Apple Orchards with Permanent Living Mulch

Orchards are complex agricultural systems with various characteristics that influence crop evapotranspiration (ETc), such as variety, tree height, planting density, irrigation methods, and inter-row management. The preservation of biodiversity and improvement of soil fertility have become important goals in modern orchard management. Consequently, the traditional approach to weed control between rows, which relies on herbicides and soil mobilization, has gradually been replaced by the use of permanent living mulch (LM). This study explored the potential of a remote sensing (RS)-assisted method to monitor water use and water productivity in apple orchards with permanent mulch. The experimental data were obtained in the Lis Valley Irrigation District, on the Central Coast of Portugal, where the “Maçã de Alcobaça” (Alcobaça apple) is produced. The methodology was applied over three growing seasons (2019–2021), combining ground observations with RS tools, including drone flights and satellite images. The estimation of ETa followed a modified version of the Food and Agriculture Organization of the United Nations (FAO) single crop coefficient approach, in which the crop coefficient (Kc) was derived from the normalized difference vegetation index (NDVI) calculated from satellite images and incorporated into a daily soil water balance. The average seasonal ETa (FAO-56) was 824 ± 14 mm, and the water productivity (WP) was 3.99 ± 0.7 kg m−3. Good correlations were found between the Kc’s proposed by FAO and the NDVI evolution in the experimental plot, with an R2 of 0.75 for the entire growing season. The results from the derived RS-assisted method were compared to the ETa values obtained from the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) surface energy balance model, showing a root mean square (RMSE) of ±0.3 mm day−1 and a low bias of 0.6 mm day−1. This study provided insights into mulch management, including cutting intensity, and its role in maintaining the health of the main crop. RS data can be used in this management to adjust cutting schedules, determine Kc, and monitor canopy management practices such as pruning, health monitoring, and irrigation warnings.

Multistage Synchronous Telescopic Manipulator With End‐Effector–Biased Rotating‐Pulling Mode for Damage‐Free Robotic Picking

ABSTRACTFruit picking is one of the most time‐consuming and labor‐intensive stages of fruit production, characterized by high labor demands and significant labor costs. Traditional fruit‐picking robotic manipulators typically adopt configurations similar to general‐purpose industrial robots, following a predefined path and employing a direct‐pulling mode to detach the fruit. However, due to the constraints of the orchard environment and the varying conditions of the fruit, manipulators should be designed to accommodate the specific horticultural characteristics of the trees to improve picking efficiency. Additionally, the picking process should be optimized based on the biological characteristics of the fruit to ensure quality. In this study, a five‐degree‐of‐freedom manipulator based on a multistage synchronous telescopic mechanism is proposed for fruit picking. Workspace analysis indicates that the manipulator can cover more than 80% of the fruit distribution on the trees. To ensure motion accuracy, a FreeRTOS‐based motion control system is developed for the manipulator. To evaluate picking efficiency and quality, fruit‐picking experiments are conducted in an apple orchard. A rope‐driven, three‐finger end‐effector is mounted in a biased position at the end of the manipulator, complemented by an RGB‐D camera for fruit detection and a ROS‐based control system for robotic operation. The performance of two picking modes (direct‐pulling and biased rotating‐pulling) are compared in these experiments. The results demonstrate that the biased rotating‐pulling mode yields a higher picking success rate and a lower stem damage rate compared with the direct‐pulling mode. Specifically, the damage‐free success rate for the biased rotating‐pulling mode is 80%, with a 9.18% reduction in stem damage compared with the direct‐pulling mode. Furthermore, the average picking cycle time is approximately 14.5 s. In conclusion, the manipulator and its motion control system successfully achieve efficient, nondestructive fruit picking with a high success rate, offering valuable insights for the development of fully automated fruit‐picking robots in the future.

Hilirisasi Buah Naga Melalui Pemberdayaan Petani di Era Revolusi Industri 4.0

Dragon fruit downstreaming through farmer empowerment is one of the important strategies in increasing added value and competitiveness of agricultural products in the era of the Industrial Revolution 4.0. This research aims to identify challenges and opportunities in the downstream process of dragon fruit through farmer empowerment by utilizing digital technology. This approach emphasizes the use of smart technologies such as the Internet of Things (IoT), big data, and automation to improve production efficiency, distribution, and market access for dragon fruit farmers. Through digitally integrated supply chain optimization, it is expected that farmers can be more empowered in managing crops, improving product quality, and expanding access to local and international markets. The results showed that with technological innovation, farmers have great potential to increase productivity and competitiveness of dragon fruit products in the global market. However, the implementation of this technology also faces challenges, especially in terms of farmers' digital literacy and technological infrastructure. Therefore, collaboration between the government, academia, and the private sector is needed to provide assistance and training to farmers in order to optimize the use of technology in the agricultural sector

Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions

Background: Self-organizing maps (SOMs) are a class of neural network algorithms able to visually describe a high-dimensional dataset onto a two-dimensional grid. SOMs were explored to classify soils based on an array of physical, chemical, and biological parameters. Methods: The SOM analysis was performed considering soil physical, chemical, and microbial data gathered from an array of apple orchards and strawberry plantations managed by organic or conventional methods and located in different European climatic zones. Results: The SOM analysis considering the “climatic zone” categorical variables was able to discriminate the samples from the three zones for both crops. The zones were associated with different soil textures and chemical characteristics, and for both crops, the Continental zone was associated with microbial parameters—including biodiversity indices derived from the NGS data analysis. However, the SOM analysis based on the “management method” categorical variables was not able to discriminate the soils between organic and integrated management. Conclusions: This study allowed for the discrimination of soils of medium- and long-term fruit crops based on their pedo-climatic characteristics and associating these characteristics to some indicators of the soil biome, pointing to the possibility of better understanding the interactions among diverse variables, which could support unraveling the intricate web of relationships that define soil quality.

Productivity of intensive apple orchards in the context of vertical zonality in the North Caucasus

The article presents an analysis of the results of studying the productivity level of intensive espalier -dwarf apple perennial plantations in the context of vertical zonality of horticulture in the Kabardino-Balkarian Republic. The goal of the research is to establish the efficiency of cultivating super-intensive orchards under vertical zonality. Methodology. All experiments were carried out using standard methods generally accepted in the field of fruit growing. The results. It has been established that in terms of productivity in almost all cultivation zones, the best results have been shown by winter-ripening varieties - Golden Delicious, Golden Delicious clone B, Golden Rangers, Granny Smith, Braeburn, Red Delicious Hapke, Fuji Kiku. It has been revealed that the best properties in terms of fruit quality are manifested in the conditions of forest-mountain and foothill ecological zones, where about 90 percent or more of apples are premium and first grade fruits. Calculations on economic efficiency have proven that growing apple trees in forest-mountain and foothill fruit zones is very profitable. Conclusions. In the forestmountain and foothill zones, when developing land plots for super-intensive orchards, it is recommended to plant winter-ripening apple tree seedlings of the following varieties: Granny Smith, Jerome, Fuji Kiku, Early Red Van, Super Chief Sandidge, Golden Rangers. The Early Red Van, Super Chief Sandage, Red Chief Kamspur varieties have a compact tree crown structure, which allows for the establishment of orchards in a compacted pattern with row spacing of up to 0.5...0.7 m. This allows for the rational use of mountain and foothill lands. High-intensity orchards established in the region begin to bear fruit relatively early, providing stable yields of 35...45 t/ha by the 3rd...4th year, with subsequent increases to 50...60 t/ha and more. Thus, the cultivation of high-intensity orchards in the region proves a high level of production profitability and a quick return on investment.

Potential of Arid Fruits Cultivation in Madhya Pradesh, India: A Comprehensive Review

The arid and semi-arid regions of India cover a significant portion of the country, including parts of Madhya Pradesh, presents a unique set of agricultural challenges and opportunities. These regions are characterized by harsh climatic conditions, such as high temperatures, low and erratic rainfall, and poor soil quality. A comprehensive review was made to give an insight on the potentials inherent in the underutilized arid fruit crops like Indian jujube, Indian gooseberry, Bengal quince, Custard apple and tamarind, discussing the nutritional and economic importance and adaptability of arid and semi-arid zones. It also explore the significance of arid and semi-arid fruit crops in enhancing food security, income generation, and sustainable agricultural practices in arid regions, with a particular focus on Madhya Pradesh. This study offers policy recommendations, which include providing subsidies to arid-fruit cultivators for attracting policy change, value addition, and advancement of profitability, as also the creation of targeted marketing strategies. Strengthening the research-extension networks in the arid fruit agronomy sector and bringing these under a government initiative such as 'One District, One Product' can increase the uptake of arid fruit cultivation. A more prudent future for the agricultural economy can be imagined with such strategic moves for arid fruit farming.

Deposition Characteristics of Air-Assisted Sprayer Based on Canopy Volume and Leaf Area of Orchard Trees.

Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies. The spray experiments were conducted on canopies of different regions, using an air-assisted sprayer with varying fan speeds of 1381 r/min, 1502 r/min, and 1676 r/min. The deposition effects were analyzed using water-sensitive papers. The inlet air speed within the canopy did not increase proportionally when the spray fan speed increased, and it showed a significant variation in locations with sparse foliage. Furthermore, droplets exhibited abnormal median volume diameters of the canopy regions with lower wind loss rates and smaller leaf areas. The influences were in the order of canopy thickness, leaf area, and inlet air speed on the cumulative deposition of droplets on both sides of the water-sensitive papers, as well as the ratio of deposition between the two sides, from big to small, are inlet air speed, leaf area, and canopy thickness. The study provides a scientific foundation for air control in precision pesticide application in apple orchards and contributes to the rapid development of precision spraying technologies.

Farmers’ desire to make changes in their agricultural branches in the first wave of COVID-19 pandemic restrictions: The example of Türkiye

The coronavirus pandemic and subsequent protectionary lockdowns have negatively impacted farmers, especially those producing perishable agricultural products worldwide and Türkiye. For this reason, most researchers began to investigate the effects of restrictions on agrarian branches during the sudden shocks of the pandemic. This study was carried out to reveal the farmers’ desire for change in agricultural production branches during the period when coronavirus pandemic restrictions were implemented in Türkiye. A survey study was conducted with broad participation and telephone interviews involving 2125 farmers in different production branches in 22 provinces. Nonlinear Canonical Correlation Analysis was used to analyze variables. According to our analysis results, compared with other agricultural branches, it has been determined that the farmers who do vegetable farming, fruit farming, and livestock farming have a higher desire to change their bare agrarian branches. Approximately half of the farmers interviewed reported difficulties obtaining seeds, fertilizers, and chemicals and providing sufficient labor. Due to these difficulties, 16.5% of the farmers stated that if the first shock effect of the pandemic restrictions continues and this shock effect continues, they will abandon the current main agricultural branches of animal husbandry and vegetable growing. Fruit growing and switch to other farming branches that require less input and labor for agricultural production. For these reasons, policymakers should invest more in market-oriented strategies such as input supply, storage of products, logistics, and processing of manufactured products to maintain the supply chain during pandemic periods. Because these strategies require high costs and the necessary knowledge of post-harvest operations, they are investments that individual farmers cannot afford. Our study revealed that the initial shock effect of the pandemic restrictions in Türkiye had a limited impact on the production of agricultural products. Despite this little impact, 87.3% of farmers stated that their income decreased slightly during this period. We foresee that the restrictions made due to the coronavirus pandemic will affect the planning and social policy in the Turkish economy in the coming years. However, this situation will not change the basic structure of Turkish agricultural production and distribution.

Can the Adoption of Green Pest Control Technologies Reduce Pesticide Use? Evidence from China

The widespread use of pesticides has long been a cornerstone of modern agriculture, but their overuse has led to several challenges, including increased production costs, food safety risks, and environmental damage. Green pest control technologies (GPCTs) have emerged as a promising alternative to traditional chemical methods, although their widespread adoption is still in progress, and their environmental and economic impacts require further examination. This study aims to evaluate the adoption of GPCT in apple orchards by employing a rigorous framework to measure pesticide intensity per unit, assess the impact of GPCT on pesticide reduction, and analyze the associated environmental effects in large-scale apple farming systems in China. Based on survey data collected from apple farmers across key production regions in China, we apply an Endogenous Treatment Effect Regression (ETR) model to evaluate the effects of these technologies on pesticide usage and concentration. This model allows for more accurate estimates of the treatment effects by addressing selection bias and accounting for both observable and unobservable factors. Our results show that the adoption of GPCT leads to a significant reduction in pesticide use intensity. Notably, the reductions are more pronounced among specific groups of farmers, particularly those who are less risk-averse and those with larger or more fragmented landholdings. These findings underscore the dual ecological and economic benefits of GPCT, providing strong support for policy initiatives that promote sustainable agricultural practices and encourage land consolidation.

Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering

In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills from the handlers. This paper, from the perspective of fruit tree industry knowledge dissemination, aims at users such as fruit farmers, fruit tree experts, fruit tree knowledge communicators, and information gatherers. It proposes a fast, cost-effective, and low-technical-barrier method for extracting fruit tree disease knowledge from research paper abstracts—K-Extract, based on large language models (LLMs) and prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs to automate the extraction of fruit tree disease knowledge. The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. The experimental results indicate that LLM models have a high accuracy rate in handling judgment tasks and simple knowledge Q&A tasks. The K-Extract method is simple, efficient, and accurate, and can serve as a convenient tool for knowledge extraction in the agricultural field.

The Yield Estimation of Apple Trees Based on the Best Combination of Hyperspectral Sensitive Wavelengths Algorithm

The Yield Estimation of Apple Trees Based on the Best Combination of Hyperspectral Sensitive Wavelengths Algorithm