Apple Yield Research Articles

Influence of cultivar and fertilization treatment on the yield and leaf nutrients content of apple (Malus domestica Borkh.)

Fertilisation strategy can have a big impact on apple (Malus domestica Borkh) yield, with considerable environmental and economic implications. This research paper presents the yield and leaf nutrients content of three apple cultivars fertilised with three treatment regimes over 2 years (2020–202) in Bosnia and Herzegovina. The specific apple cultivars investigated were: Jonagold Decosta, Red Idared and Gala Schnitzer®Schniga, each fertilised with three treatments: T1 (control‒without fertilization); T2 (300 kg ha−1 NPK (6:18:36) + 150 kg/ha N (calcium ammonium nitrate‒CAN)) and T3 (foliar nutrition‒mixture organic-mineral fertilizer commercially named “FitoFert Kristal” (0.6%) (10:40:10) + “FitoFert Kristal” (0.6%) (20:20:20) + “FoliFetril Ca” (0.5%) (N:Ca)) in Bosnia and Herzegovina in the period of two years (2020–2021). Significant differences of different yield categories (yield per tree, yield per hectare and yield efficiency) were found among cultivar/treatment combinations, cultivars, treatments and years. Yield per tree, yield per hectare and yield efficiency were lowest in cultivar Jonagold DeCosta. Fertilization treatment T1 significantly influenced the lowest yield per tree and yield per hectare with the magnitudes 7.55 kg tree−1 and 27.96 t ha−1, respectively. The highest yield efficiency was found in trees fertilised with treatment T3 with 9.21 55 kg tree−1, 34.11 96 t ha−1 and yield efficiency of 0.25 kg cm−2. Six mineral elements in the apple leaf, such as boron (B), calcium (Ca), manganese (Mn), iron (Fe), potassium (K), and zinc (Zn), were presented in known magnitudes. The cultivar Jonagold DeCosta's leaves had the highest K, B, and Zn contents with 8500.8 mg kg−1 FW (i.e. fresh weight of leaves), 33.8 mg kg−1 FW, and 12.2 mg kg−1 FW, respectively, while cultivar Red Idared's leaves had the highest Ca, Fe, and Mg contents. The fertilisation treatment T3 influenced significantly the highest content of Ca (301.37 mg kg−1 FW), Fe (116.5 mg kg−1 FW), B (41.6 mg kg−1 FW), Mn (22.4 mg kg−1 FW), and Zn (14.9 mg kg−1 FW) in leaves, while the highest content of K was found in leaves from trees fertilised with treatment T2 (8130.5 mg kg−1 FW). The experimental outcomes have proven that the cultivar/treatment combinations, cultivars, treatments, and time duration (in years) are the key factors affecting the potassium, calcium, iron, boron, and manganese contents. It was concluded that the foliar application enables easier mobility of elements, which results in a greater number of fruits and larger fruits, which leads to a higher yield. This study is the first of its kind in Bosnia and Herzegovina, and the findings of this research will pave the way for future research activities involving increasing the number of cultivars and different fertilisation treatments on apple yield and leaf mineral composition.

Adaptogenic Preparations Enhance the Tolerance to Spring Frosts, Yield and Quality of Apple Fruits

The goal of the research is to study the effectiveness of the use of adaptogenic preparations of the Natural Plant Complex “White Pearl” line to increase the yield and quality of apples. The objects of the studies were the apple cultivar “Sinap Orlovsky” and adaptogenic preparations: phytomodulator “White Pearl Universal Antifreeze” and phytocorrector “White Pearl Drip Ca + Mg”. The experiment used the following variants: 1—control (foliar sprays with water); 2—foliar sprays with adaptogenic preparations: 1% solution of “White Pearl Universal Antifreeze” + 1% solution of “White Pearl Drip Ca + Mg”. As a result of the complex use of biological products, the yield was significantly increased by 1.7 times and the average fruit weight was increased by 20.3 g. The adaptogenic preparations increased the yield of marketable apple fruits by 14.8% and contributed to a 2.5-fold decrease in fruits affected by scald and a 2-fold decrease in bitter pitting. The adaptogenic preparations improved the taste qualities of “Sinap Orlovsky” fruits compared to the control by increasing the amount of sucrose by 25.6% and ascorbic acid by 20.5%. The conducted tests of adaptogenic preparations in apple plantations show the prospects of their use as additional techniques in traditional apple cultivation technologies.

Monitoring the Degree of Mosaic Disease in Apple Leaves Using Hyperspectral Images

Mosaic of apple leaves is a major disease that reduces the yield and quality of apples, and monitoring for the disease allows for its timely control. However, few studies have investigated the status of apple pests and diseases, especially mosaic diseases, using hyperspectral imaging technology. Here, hyperspectral images of healthy and infected apple leaves were obtained using a near-ground imaging high spectrometer and the anthocyanin content was measured simultaneously. The spectral differences between the healthy and infected leaves were analyzed. The content of anthocyanin in the leaves was estimated by the optimal model to determine the degree of apple mosaic disease. The leaves exhibited stronger reflectance at a range of 500–560 nm as the degree of disease increased. The correlation between the spectral reflectance processed by the Gaussian1 wavelet transform and anthocyanin was significantly improved compared to the corresponding correlation results with the original spectrum. The VPs-XGBoost anthocyanin estimation model performed the best, which was sufficient to monitor the degree of the disease. The findings provide theoretical support for the quantitative estimation of leaf anthocyanin content by remote sensing to monitor the degree of disease; they lay the foundation for large-scale monitoring of the degree of apple mosaic disease by remote sensing.

Efficiency of application of different apple trees protection systems against harmful organisms in the Precarpathian province of the Carpathian mountain area of Ukraine

Goal. Study of the efficiency of application of chemical, biological and biological-chemical protection systems of apple trees against harmful organisms in the conditions of the Precarpathian province of the Carpathian mountain zone of Ukraine.
 Methods. Phytosanitary monitoring was carried out visually and with the help of pheromone traps. Phytophagous colonization and disease development were recorded according to generally accepted methods in the phases of apple development: «swelling of buds», «green cone», «bud extension», «bud separation», «pink bud», «blooming», «end of flowering», «fruit formation», «fruit growth» and «fruit ripening». The technical effectiveness of insecticides was determined after 5 and 7 days, and the effectiveness of fungicides — after 7 days. Statistical processing of research results was carried out by the method of variance analysis.
 Results. The results of monitoring the phytosanitary state of apple plantations established that the most common pests in 2018—2020 in the conditions of the Precarpathian Province of the Carpathian Mountain Zone of Ukraine were: apple fruit borer (37.2%), aphids (19.5%), leafhoppers (12.2%), mites (8.0%), apple moth (8.3%), flower eater (7.9%), hairy deer (6.9%); the most common diseases: scab (38.2%), powdery mildew (32.8%), moniliosis (14.2%), fruit rot (14.8%). All studied apple tree protection systems against harmful organisms in the conditions of the Precarpathian province of the Carpathian mountain zone of Ukraine obtained high yield rates, and all protection systems allowed to reliably protect apple orchards from pests and diseases. The technical efficiency of the chemical protection system, which included 12 treatments with chemical pesticides, averaged 82.5—96.5%. Productivity using this system was 26.5—26.7 t/ha. The biological protection system, which was based on 7 treatments with biological preparations, made it possible to obtain an apple yield 22.4—22.9 t/ha. The technical efficiency of the preparations of the biological defense system was at the level 66.1% — 72.3%. The system of biological and chemical protection of apple trees against harmful organisms included 7 treatments, reducing the use of chemical protection agents to one treatment, which made it possible to significantly reduce the amount of chemical agents used, minimize the negative impact of pesticides on the ecosystem of the apple orchard, and obtain productivity 23.5—25.1 t/ ha, for the effectiveness of drugs 66.3 94.3%.
 Conclusions. When apple orchards are heavily colonized by pests and diseases, it is recommended to use a chemical protection system that includes 12 treatments with chemical pesticides (9 treatments with a mixture of insecticides and fungicides, 2 treatments with fungicides and one treatment with insecticides) in accordance with the 12 main phases of apple tree development in which the most harmful organisms inhabit and damage the ecosystem of the apple orchard during the period from the «green cone» phenophase to the «fruit ripening» phenophase. To reduce the use of chemical protection agents in apple orchards, a biological and chemical system should be used: in the «green cone» phenophase, one treatment with chemical pesticides, and in the «end of flowering» phenophase — «fruit ripening» — with biological preparations. Under the condition of complete withdrawal from the use of chemical pesticides, the system of biological protection of the apple tree should be used, which is based on seven treatments with biological preparations in the phenophase «separation of buds» — «ripening of fruits».

Overexpression of the FERONIA receptor kinase MdMRLK2 enhances apple cold tolerance.

Cold is one of the main abiotic stresses in temperate fruit crops, affecting the yield and fruit quality of apple in China and European countries. The plant receptor-like kinase FERONIA is widely reported to be involved in abiotic stresses. However, its function in apple cold resistance remains unknown. Modification of cell wall components and accumulation of soluble sugars and amino acids are important strategies by which plants cope with cold. In this study, expression of the apple FERONIA receptor-like kinase gene MdMRLK2 was rapidly induced by cold. Apple plants overexpressing MdMRLK2 (35S:MdMRLK2) showed enhanced cold resistance relative to the wild type. Under cold conditions, 35S:MdMRLK2 apple plants had higher amounts of water insoluble pectin, lignin, cellulose, and hemicellulose, which may have resulted from reduced activities of polygalacturonase, pectinate lyase, pectinesterase, and cellulase. More soluble sugars and free amino acids and less photosystem damage were also observed in 35S:MdMRLK2 apple plants. Intriguingly, MdMRLK2 interacted with the transcription factor MdMYBPA1 and promoted its binding to MdANS and MdUFGT promoters, leading to more anthocyanin biosynthesis, particularly under cold conditions. These findings complemented the function of apple FERONIA MdMRLK2 responding to cold resistance.

Cascading effects of management and landscape on insect pollinators, pollination services and yield in apple orchards

Recent insect pollinator declines, mainly due to the expansion and intensification of agricultural land use, are jeopardizing ecosystem service provisioning in agroecosystems. Organic farming has been suggested as a biodiversity-friendly alternative to conventional farming, but additional insights evaluating its effectiveness in perennial cropping systems are needed. Here, we provide a comprehensive analysis of local and landscape effects on the provided pollination services and yield in apple crops. We first used mixed models to disentangle effects of agricultural management and landscape effects on both taxonomic and functional diversity of wild bees and hoverflies during bloom in both organic and integrated pest management (IPM) apple orchards in Belgium, and then compared these biodiversity indicators with semi-natural grasslands as a benchmark. Next, we applied piecewise structural equation modeling to quantify the direct and indirect effects of orchard management type and landscape on fruit set, seed set and ultimately on crop yield. Orchards, regardless of their type of management, hosted lower taxonomic and functional pollinator diversity compared to the semi-natural benchmark. Yet, pollinator abundance and diversity were higher in orchard edges and increased with small-scale landscape diversity, which was positively associated with the presence of semi-natural habitat, as well as arable and urban land. Investigating the cascading effects on apple yield, we found that yield levels were not shaped by the observed pollinator diversity or by the measured pollination services, but were lower in organic orchards. Overall, we conclude that apple yield was likely not pollinator limited in our study system, but that conserving and restoring semi-natural habitats, maintaining and expanding orchard edges through reducing field sizes and promoting landscape diversity are the most promising avenues for pollinator conservation in orchard landscapes.

The Impact of Stem Site and Foliar Application of Zinc and Boron in Growth, Flowers and Yield of Apples (Brahimi Class)

The research was conducted in one of the private orchards in al-Saqlawiya district of Fallujah province Anbar province, for two seasons, 2021-2022, to study the impact of the site of fruitful stem site and spraying with zinc and boron on some characteristics of growth and flowers and the product of the apple of the Class Brahimi was chosen 5 4 homogenous trees as long as five years old and planted at dimensions of 4x4 meters, carried out a working experiment (3x3x2) using the design of the entire random sectors RCBD if the first factor represents the choice of the location of the fruitful stem site and its symbol (S1, S2, S3) and the second factor spraying zinc and its mark (ZN0, ZN1) and the third factor foliar with boron, and its marked as (P0.P1.P2) and trees were sprinkled with elements twice each season among the results that the effect of spraying by study factors led to a moral increase in the qualities studied (percentage of total sugars, vitamin C, total acidity, T.S.S., carbohydrate ratio)

Plant-Leaf Disease Prediction Using Deep Learning

Abstract: Brown spot, Mosaic, Grey spot, and Rust all significantly reduce apple yield. Rust is a sign of Foliar illness in this instance. The primary factor influencing apple output is the occurrence of apple leaf diseases, which results in significant yearly economic losses. Therefore, it is very important to research apple leaf disease identification. Plants are frequently attacked by pests, bacterial diseases, and other microorganisms. Inspection of the leaves, stem, or fruit usually identifies the attack's signs. Powdery Mildew and Leaf Blight are two common plant diseases that can cause severe harm if not treated quickly. In the realm of agriculture, image processing is frequently utilized for classification, detection, grading, and quality control. Finding and identifying plant diseases is crucial, especially when trying to produce fruit of the highest caliber. The real-time identification of apple leaf diseases is addressed in this research using a deep learning strategy that is based on enhanced convolutional neural networks (CNNs). This study uses data augmentation and image annotation tools to create the foliar disease dataset, which is made up of complex images captured in the field and laboratories. Overall, we can identify the illness present in plants on a massive scale by utilizing machine learning to train the vast data sets that are publically available. The project explains how to identify plant leaf diseases, how they affect plant yield, and which pesticides should be used to treat them. in agriculture. To monitor huge plant fields and automatically identify disease symptoms as soon as they develop on plant leaves, research on automatic plant disease is crucial. In this essay, we'll demonstrate how to identify plant illnesses by obtaining photos of their leaves

Artificial Neural Network Based Apple Yield Prediction Using Morphological Characters

The yield of the crop is a complex function of a number of dependent traits, which makes yield prediction a statistically difficult task. A number of work on yield prediction using morphological characters already exists in the literature. Most of the work used statistical techniques such as linear regression and crop yield models, which assume a linear relationship between yield and the morphological traits; in actual practice, such a linear relationship is seldom achieved. With the advancement in the field of machine learning techniques, these methods can provide a viable alternative for dealing with nonlinear relationships for yield prediction. Globally, apples are the most consumed fruit. In this paper, attempts have been made to predict the yield of the apple crop using morphological traits. PCA was used for selection of the significant variables. These variables were later used as input variables in the ANN model with different hidden layers for predicting crop yield. The predictive performance of the model was evaluated using standard statistical tests. Sensitivity analysis was performed to find out the individual effects of each character on the apple yield. The study contributes to a better understanding of the complex relationships between crop yield and morphological traits.

Using lightweight deep learning algorithm for real-time detection of apple flowers in natural environments

Flower thinning at the most appropriate stage could achieve high and stable yield of apple. Achieving the accurate and real-time detection of apple flowers can provide necessary technical support for the vision system of thinning robots. An apple flower detection method based on lightweight YOLOv5s algorithm was proposed. The original Backbone of YOLOv5s was replaced by ShuffleNetv2, and the Conv module of the Neck part of YOLOv5s network was replaced by Ghost module. ShuffleNetv2 reduced the memory access cost through Channel Split operation. Ghost module reduced the computing cost of the general volume layer while maintaining the similar detection performance. The combination of these two methods in the improvement of YOLOv5s network can greatly reduce the size of the model and improve the detection speed, which was convenient for the migration and application of the model. To verify the effectiveness of the model, 3005 apple flower images in different environments were used for training and testing. The Precision, Recall, and mean Average Precision (mAP) of YOLOv5s-ShuffleNetv2-Ghost model were 88.40 %, 86.10 %, and 91.80 %, respectively, the model size was only 0.61 MB, and the detection speed was 86.21 fps. The detection speed of YOLOv5s-ShuffleNetv2-Ghost model on the Jetson nano B01 development board was 2.48 fps. The results showed that the method was feasible for real-time and accurate detection of apple flowers. The research can provide technical reference for the development of orchard flower thinning robots.

LAD-Net: A Novel Light Weight Model for Early Apple Leaf Pests and Diseases Classification.

Aphids, brown spots, mosaics, rusts, powdery mildew and Alternaria blotches are common types of early apple leaf pests and diseases that severely affect the yield and quality of apples. Recently, deep learning has been regarded as the best classification model for apple leaf pests and diseases. However, these models with large parameters have difficulty providing an accurate and fast diagnosis of apple leaf pests and diseases on mobile terminals. This paper proposes a novel and real-time early apple leaf disease recognition model. AD Convolution is firstly utilized to replace standard convolution to make smaller number of parameters and calculations. Meanwhile, a LAD-Inception is built to enhance the ability of extracting multiscale features of different sizes of disease spots. Finally, the LAD-Net model is built by the LR-CBAM and the LAD-Inception modules, replacing a full connection with global average pooling to further reduce parameters. The results show that the LAD-Net, with a size of only 1.25MB, can achieve a recognition performance of 98.58%. Additionally, it is only delayed by 15.2ms on HUAWEI P40 and by 100.1ms on Jetson Nano, illustrating that the LAD-Net can accurately recognize early apple leaf pests and diseases on mobile devices in real-time, providing portable technical support.

Економіко-математичне моделювання оптимізації виробничих витрат у садівництві

Goal. To build an economic-mathematical model of optimizing the structure of production costs and to reveal the relationship between the investigated indicators and the factors that influence their value. Methods. Scientific abstractions — for the formation of theoretical generalizations and conclusions; the economic-mathematical method — to optimize the cost structure in horticulture; the method of system analysis and synthesis — to establish cause-and-effect relationships in the formation of production costs in horticulture. Results. To improve the management of expenses in the direction of their optimization, it is advisable to use the method of optimization modeling for various structures of apple plantations. The analysis of the simulation results indicates the possibility of reducing the cost of growing apples under the condition of rational distribution of costs. A comparison of the actual indicators of financing apple cultivation with those obtained in the modeling process shows the need for a reasonable approach to spending money on mineral and organic fertilizers, means of protection, and wages. It is these cost items that are the most important in shaping the yield of apples according to the correlation-regression analysis and the data obtained during the calculation of the economic-mathematical model of cost optimization. From the equation obtained as a result of conducting such an analysis, it follows that the listed factors should be placed in the following order according to the degree of importance: depreciation of agricultural machinery, other direct costs, costs of organic fertilizers and mineral fertilizers, fuel and lubricants, general production costs, costs of protective equipment, depreciation of perennial plantings, labor costs. Using the results of cost optimization modeling, it is quite possible not only to increase the yield of apples by 11.88–28.81% but also to reduce their cost price and to reduce total costs by 6.3–67.7%. Conclusions. Optimizing the cost structure using the methods of economic-mathematical modeling is one of the ways to effectively manage funds. The optimal cost structure justified for the horticulture industry makes it possible to reduce the cost price and increase the yield of apples.

Potential to reduce pesticides in intensive apple production through management practices could be challenged by climatic extremes

Apples are the third most produced fruit in the world, but their production is often pesticide-intensive. Our objective was to identify options for pesticide reduction using farmer records from 2549 commercial apple fields in Austria during five years between 2010 and 2016. Using generalized additive mixed modeling, we examined how pesticide use was related to farm management, apple varieties, and meteorological parameters, and how it affected yields and toxicity to honeybees. Apple fields received 29.5 ± 8.6 (mean ± SD) pesticide applications per season at a rate of 56.7 ± 22.7 kg ha−1, which included a total of 228 pesticide products with 80 active ingredients. Over the years, fungicides accounted for 71 % of the pesticide amounts applied, insecticides for 15 %, and herbicides for 8 %. The most frequently used fungicides were sulfur (52 %), followed by captan (16 %) and dithianon (11 %). Of insecticides, paraffin oil (75 %) and chlorpyrifos/chlorpyrifos-methyl (6 % combined) were most frequently used. Among herbicides, glyphosate (54 %), CPA (20 %) and pendimethalin (12 %) were most often used. Pesticide use increased with increasing frequency of tillage and fertilization, increasing field size, increasing spring temperatures, and drier summer conditions. Pesticide use decreased with increasing number of summer days with maximum temperatures >30 °C and number of warm, humid days. Apple yields were significantly positively related to the number of heat days, warm humid nights, and pesticide treatment frequency, but were not affected by frequency of fertilization and tillage. Honeybee toxicity was not related to insecticide use. Pesticide use and yield were significantly related to apple varieties. Our analysis shows that pesticide use in the apple farms studied can be reduced by less fertilization and tillage, partly because yields were >50 % higher than the European average. However, weather extremes related to climate change, such as drier summers, could challenge plans to reduce pesticide use.

A multimodal and multiscale investigation of factors affecting the juice yield of cider apples

Apple cider juice yield at harvest and after 15 and 30 days of storage durations was studied by analyzing the mechanical properties of fresh and plasmolyzed flesh, water distribution, cell wall polysaccharide composition and organization of the apples; in this study, the apple varieties used were Avrolles, Douce coetligne, Douce moen, Judor, Petit jaune. Juice yield mainly depended on the apple variety and the storage duration. Cellulose organization and cell wall pectin hydration were affected by ripening and are related to fruit firmness. Flesh viscoelastic mechanical properties were not general indications of juice yields. However, these properties helped distinguish the varieties according to flesh damage caused by ice crystals upon freezing. Cell encapsulation of the juice in the flesh contributed to lower yields. The apple variety and harvesting mode are recommended as a means to better control juice yield variations.

Precision Nitrogen Fertilizer and Irrigation Management for Apple Cultivation Based on a Multilevel Comprehensive Evaluation Method of Yield, Quality, and Profit Indices

Precise and efficient fertilizer and irrigation management is critical for apple production in the Loess Plateau, China. In this study, we established three levels of nitrogen application and irrigation in nine treatments in an apple orchard based on a completely randomized block design. Then, we analyzed different apple indicator responses to nitrogen and irrigation and their related interactions. More importantly, we used the multilevel comprehensive fuzzy evaluation value (MFCE) method to combine the weights of all indicators to obtain the comprehensive growth indicators for apples. Finally, we analyzed the effect of nitrogen and irrigation coupling on the comprehensive growth of apples and then determined the optimal interval for nitrogen application and irrigation. The results indicated that an increase in the amount of irrigation was beneficial for apple yield, but excessive nitrogen fertilizer application significantly reduced apple yield. The apple indicators were not sensitive to irrigation and nitrogen application and their related interactions; they were mainly controlled by the apple cultivar. On the other hand, an increase in the amount of nitrogen fertilizer or a reduction in the amount of irrigation could improve apple quality. The results of the comprehensive evaluation showed that the T5 treatment was most beneficial for the comprehensive growth of apples. To obtain the optimal interval for nitrogen application and irrigation more precisely, we used multiple linear regression based on the MFCE values of apples, nitrogen, and irrigation in R language. Nitrogen and irrigation showed a positive effect on the comprehensive growth of apples when the irrigation amount was low. However, nitrogen application and irrigation had a negative effect on the comprehensive growth of apples when the irrigation level was high. After optimization, the optimal nitrogen application and irrigation amounts were 170.5–189.5 kg·hm−2 and 38.4–42.7 mm, respectively. We recommend using this irrigation and fertilizer management scheme for apple orchards in China’s Loess Plateau.

Planting Age Identification and Yield Prediction of Apple Orchard Using Time-Series Spectral Endmember and Logistic Growth Model

In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future.

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

Our study established a machine learning (ML) model that could predict the apple yield based on various satellite multisensor data, such as climatological, SAR backscatter, terrain distribution, and soil factors, grouped as 26 subcriteria. A total of 986 apple orchards database were collected from 2018 to 2021 in Kashmir Valley, India covering an area of 277953.7 ha farmland. The novelty of our research is the integration of Google Earth Engine cloud and ML models, namely random forest, support vector machine, extreme gradient boosting, K-nearest neighbors, and Cubist along with the geographic information system/remote sensing technology to create an accurate and comprehensive apple yield prediction model in the precision agriculture realm for highlands. The multicollinearity testing indicated that the tolerance and VIF values of all the conditioning factors were <0.1 and <6.85, respectively, indicating no multicollinearity problems among the apple yield suitability factors. Among the tested ML models, the Cubist model performed best, with R2 of 0.83, root-mean-squared error of 0.56 t / ha, and mean absolute error of 0.2 t / ha. The results showed a low mean fruit yield during 2018 of 12.36 ton / ha, whereas maximum fruit yield was reflected in 2021 of 14.05 ton / ha. The heat map revealed the highest normalized differential vegetation index along with vertical-vertical/ vertical-horizontal polarization backscatter, detected during the pre-event of severe snowfall compared to on- and postevent of snowfall for the respective years. Untimely snowing and infestation due to fungi and bacterial diseases regularly reduce fruit yield in the study area. Our study successfully used of high-resolution optical-SAR data combined with ML models as a promising tool for monitoring the yield variability over the highland areas.

Effects of Meteorological Factors on Apple Yield Based on Multilinear Regression Analysis: A Case Study of Yantai Area, China

Evaluating the impact of different meteorological conditions on apple yield and predicting the future yield in Yantai City is essential for production. Furthermore, it provides a scientific basis for the increase in apple yield. In this study, first, a grey relational analysis (GRA) was used to determine the quantitative relationship between different meteorological factors and meteorological yield which is defined as affected only by meteorological conditions. Then, the comprehensive meteorological factors extracted by a principal component analysis (PCA) were used as inputs for multiple linear regression (MLR). The apple yield accuracy was compared with the lasso regression prediction. Trend analysis showed that the actual apple yield increased annually, but the meteorological yield decreased annually over a long time. Correlation ranking illustrated that the meteorological yield was significantly correlated with the frost-free period, the annual mean temperature, the accumulated temperature above 10 °C, etc. The good consistency between GRA and MLR–PCA showed that the accumulated temperature above 10 °C, the March–October mean temperature, and the June–August mean temperature are key meteorological factors. In addition, it was found that the principal components F2, F4, and F5 were negatively correlated with meteorological yield, while the principal components F1 and F3 were positively correlated with meteorological yield. Moreover, the MLR–PCA model predicted the apple yield in 2020 as 47.256 t·ha−1 with a 7.089% relative error. This work demonstrates that the principal component regression model can effectively extract information about different meteorological factors and improve the model’s accuracy for analyzing key meteorological factors and predicting apple yield.

Effects of laser scanner quality and tractor speed to characterise apple tree canopies

Precision orchard management requires non-destructive sensor technologies to identify key sources of variation in apple yield and quality so operations such as fruit thinning, root-pruning as well as harvesting can be better targeted and optimised. We compared the data quality of two light detection and ranging (LiDAR) scanners to assess their ability to estimate the volume of ‘Braeburn’ apple trees. Scanner one was a high-quality tractor-mounted LiDAR (SICK, LDMRS 400001, Düsseldorf, Germany) and the second system consisted of two lower-cost SICK LiDAR sensors fitted on a commercial orchard sprayer. To compare the two scanner systems, two tree row sections with different growth vigour were created by targeted root pruning to reduce annual shoot growth. LiDAR scans and reference assessments were made at the end of the growing season. In addition, LiDAR data were obtained at two tractor speeds: 1.5 and 7.0 km/h (standard spraying speed). Results show key canopy features like the beginning/end of rows or large gaps can be detected with the lower-cost sensors. However, tractor speed had a major effect on data quality. At 1.5 km/h, alpha-shaped tree volumes of the high-quality LiDAR show a high correlation (R2 = 0.84) with the reference assessments, whereas the data quality of the lower-cost sensors was not high enough to represent the tree volume with the alpha-shape algorithm (R2 = 0.09). Moreover, this work raises the question of how precise a sensor-based tree volume needs to be determined to describe heterogeneity in an orchard. Specifically, how to best match sensor data to manual methods of canopy measurement and allow sensor data to be used for multiple applications.

Optimization of apple irrigation and N fertilizer in Loess Plateau of China based on ANP-EWM-TOPSIS comprehensive evaluation

It is imperative to scientifically and credibly optimize the irrigation and nitrogen (N) fertilizer level of apple orchards in Loess Plateau, where is water-scarce and over-fertilized with N. In 2017-2018, four irrigation levels (W1, rainfed; W2, 60% ETc; W3, 80% ETc; W4, 100% ETc) and four N fertilizer levels (N1, 375 kg·ha−1; N2, 490 kg·ha−1; N3, 605 kg·ha−1; N4, 720 kg·ha−1) were applied to investigate the response of apple yield, quality, water use efficiency (WUE), partial factor productivity (PFP) of N, economic benefit and N balance to irrigation and N managements. The results proved that irrigation and N managements could improve apple yield, quality, WUE, PFP, economic benefit and N surplus. The highest apple yield, Chroma, Vitamin C (VC), PFP and economic benefit were obtained by 80% ETc in different irrigation levels. High N levels significantly increased apple yield, Chroma, VC, WUE and economic benefits. However, excessive irrigation and N could cause negative effects. 100% ETc reduced firmness, titratable acid and WUE. High N levels not only increased N surplus but also decreased firmness, shape index, titratable acid, soluble sugar and PFP. Clearly, the optimal irrigation and N managements were varied for apple yield, quality, WUE, PFP, economic benefit and N surplus. Therefore, an integrated model based on Analytical Network Process (ANP), Entropy Weight Method (EWM) and TOPSIS was used to comprehensively evaluate the multi-objectives of apple cultivation in Loess Plateau. It was concluded that the irrigation (1500-1800 mm) and N levels (540-660 kg·ha−1) were recommended for improving apple cultivation in this region. Our results played an important role in guiding irrigation and N management of apple cultivation in Loess Plateau of China.