Regional Crop Research Articles

Redesigning and validation of fertilizer use in maize for variable plant densities in central rift valley and Jimma in Ethiopia.

Due to low adoption and sub-optimal fertilizer use and planting density recommendation in maize, redesigning and testing these technologies are required. The study was conducted to evaluate redesigned fertilizer use of maize in two pant densities (32,443 and 53,333 plants ha-1 in Central Rift Valley (CRV); 27724 and 62,000 plants ha-1 in Jimma) on farmers' fields in contrasting agro-ecologies of Ethiopia. The on-farm study was conducted in the 2017 and 2018 cropping seasons with 3 × 2 fertilizer and plant density, factors in both regions of Ethiopia. In redesigned fertilizer use, nutrients were estimated based on the target yield. In this study, 40.8, 0.0, and 12.2 kg ha-1 N, P, and K were estimated for the redesigned fertilizer use in CRV (50% of water-limited potential yield (Yw) = 3.1 t ha-1) whereas in Jimma (50% of Yw = 7.5 t ha-1) 149.8, 9, 130.6 kg ha-1 N, P and K were estimated to produce the 50% of Yw. Linear mixed modeling was used to assess the effect of fertilizer-plant density treatments on maize yield and nutrient use efficiency. The result revealed that the average estimated maize yield for WOF, FFU, and RDFU fertilizer treatments were 2.6, 3.6, and 4.5 t ha-1 under current plant density (32,443 plants ha-1) in CRV whereas the average yields of these treatments were 3.2, 4.5 and 4.5 t ha-1 respectively when maize was grown with redesigned plant density (53,333 plants ha-1) in the same location. The average maize yield with WOF, FFU, and RDFU were 3.0, 4.6, and 4.6 t ha-1 with 27,774 plants ha-1 plant density in Jimma whereas the average maize yields over the two seasons with the same treatments were 4.3, 6.0 and 8.0 t ha-1 respectively when the crop is planted with 62,000 plants ha-1 plant density. The RDFU and redesigned plant density resulted in significantly higher yield compared to their respective control CRV but RDFU significantly increased maize yield when it was planted at redesigned (62,000 plant ha-1) in Jimma. FFU and RDFU were economically viable and redesigned plant density was also a cheaper means of improving maize productivity, especially in the Jimma region. Soil organic carbon and N were closely related to the grain yield response of maize compared to other soil factors. In conclusion, this investigation gives an insight into the importance of redesigned fertilizer use and redesigned plant density for improving maize productivity and thereby narrowing the yield gaps of the crop in high maize potential regions in Ethiopia like Jimma.

Comparison of robot concepts for new sustainable crop production systems

In recent decades the agricultural intensification led to a landscape simplification, where once heterogeneous landscapes turned into more monocultured cropping regions. The main driver behind it was to be able to use larger and more powerful agricultural machinery in order to increase productivity and specially to decrease labor costs. With the advent of robots in combination with new methods of AI it is obvious that this is not only a new step of automated mechanization. New robotic systems open up new opportunities of how to cultivate crop plants. Different robotic concepts with different properties are discussed and how these might contribute to achieve the indisputably needed improvements in sustainable and environmental-friendly crop production systems. In a performed case study, it is demonstrated that even if larger robotic solutions when operated in isolation still can achieve higher area outputs, smaller and often specialized robotic systems can achieve a substantially improved work quality in the use case of seeding.

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.

The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly. T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.

Оптимальная норма высева и междурядье для повышения урожайности и качества семян сорта сои Персона

The need for research is due to the use by farmers of the region of continuous row soybean crops with high seeding rates, without taking into account the characteristics of the variety. The purpose of the work is to establish the optimal method and rate of sowing seeds of the Persona soybean variety to obtain a high yield of high-quality seeds. A two-factor field experiment was carried out on the experimental field of the Far Eastern State Agrarian University of the Amur Region in 2017-2019. Options: factor A – seeding rate: 1. 250; 2. 400 (control); 3. 550; 4. 700; 5. 850 thousand pieces per hectare; factor B – row spacing: 1. 15 cm (control); 2. 30; 3. 45; 4. 60 cm. Each row spacing corresponded to five seed sowing rates. The Persona soybean variety produces the highest yield with wide-row sowing with a row spacing of 45 cm and a seeding rate of 550 thousand units/ha – 2,6 t/ha. Not significantly by 0,1 t/ha inferior to him sowing the same row spacing with a seeding rate of 400 thousand pcs./ha and row sowing at 30 cm with a seeding rate of 550 thousand pcs. for 1 hectare. Such crops provide the yield of high-quality seeds with a high fat content and are practically not inferior to the control in protein content. In the Amur region conditions for obtain the largest number of high-quality seeds, it is advisable to sow soybeans of the Persona variety with row spacing of 30 or 45 cm and a seeding rate of 550 thousand pieces per 1 ha. Keywords: SOYBEAN, PERSONA VARIETY, SEEDING RATE, GERMINATION, QUALITY, WEIGHT, YIELD, SEEDS

Integrated assessment of crop planting suitability: A case study in the Hetao Irrigation District of China using HJ-1A/1B satellite data

The increasing demand for food due to population growth and climate change poses significant challenges to achieve the Sustainable Development Goal of zero hunger by 2030. A key aspect in overcoming these challenges is to determine appropriate planting patterns for various crops, aimed at enhancing regional-scale crop water productivity despite the constraints of limited water and land resources. Remote sensing data and models provide the possibility for accurately estimating water productivity of different crops on a regional scale, but studies on remote sensing-based assessments of regional crop water productivity and its applications in agricultural management are still limited. In this study, we present a satellite-based integrated approach to assess crop planting suitability based on regional crop water productivity estimation. Focusing on the Hetao Irrigation District (HID) in the upper Yellow River basin, a representative irrigation district in arid region of Northwest China, we first use remote sensing data (HJ-1A/1B) to estimate water productivity for the two major crops, maize and sunflower, within the HID from evapotranspiration and yield estimates. Additionally, we introduce a novel crop planting suitability index based on the frequency distribution of crop water productivity, facilitating the determination of appropriate crop planting patterns. Our findings reveal that Dengkou, the periphery of Hangjinhouqi, and the southern part of Linhe are optimal for maize cultivation, while Wuyuan and the northern part of Linhe are ideal for sunflower cultivation. This is attributed to higher water productivity levels for maize in Dengkou (2.46 kg/m³) and Linhe (2.15 kg/m³), and for sunflower in Wuyuan (0.86 kg/m³). Following the optimization of crop planting distribution, the average water productivity for maize and sunflower increases by 7.6 % and 5.0 %, respectively. The proposed method can be generalized to other regions, and the results offer valuable insights for local governments in decision-making to regulate cropping pattern and maximize regional crop water productivity.

A Review of the Research Status and Prospects of Regional Crop Yield Simulations

A Review of the Research Status and Prospects of Regional Crop Yield Simulations

Spring sown camelina (Camelina sativa) contributes to the management of three summer weeds

Camelina (Camelina sativa (L.) Crantz) is an attractive drought-tolerant crop for Mediterranean regions due to its rapid growth and ability to out-compete many dicotyledonous winter annual weeds. In this experiment the weed suppression capacity of spring sown camelina against Chenopodium album L. (common lambsquarters), Polygonum aviculare L. (prostrate knotweed), and Xanthium spinosum L. (spiny cocklebur) was studied. The trial was conducted in Lleida (Spain) between 2019 and 2021. Camelina was sown in March each year. Experimental plots contained quadrats with each weed species as well as weed-free and crop-free quadrats. Height and aboveground biomass of weeds in competition with camelina decreased by over 50 % compared to the controls. However, crop and weed growth had seasonal differences depending on the weather conditions: (1) a moderately dry spring promoted crop production (1573 kg ha−1); (2) a rainy spring benefited weed development, negatively affecting crop growth and yield (739 kg ha−1); and (3) a severe dry spring affected growth of both crop and weeds, reducing crop production by up to 80 % (298 kg ha−1). The summer weed suppression capacity of camelina is enhanced by drought conditions, which makes camelina useful for managing these weeds.

An Evaluation of Dryland Ulluco Cultivation Yields in the Face of Climate Change Scenarios in the Central Andes of Peru by Using the AquaCrop Model

Ullucus tuberosus is an Andean region crop adapted to high-altitude environments and dryland cultivation. It is an essential resource that guarantees food security due to its carbohydrate, protein, and low-fat content. However, current change patterns in precipitation and temperatures warn of complex scenarios where climate change will affect this crop. Therefore, predicting these effects through simulation is a valuable tool for evaluating this crop’s sustainability. This study aims to evaluate ulluco’s crop yield under dryland conditions at 3914 m.a.s.l. considering climate change scenarios from 2024 to 2100 by using the AquaCrop model. Simulations were carried out using current meteorological data, crop agronomic information, and simulations for SSP1-2.6, SSP3-7.0, and SSP5-8.5 of CMIP 6. The results indicate that minimum temperature increases and seasonal precipitation exacerbation will significantly influence yields. Increases in rainfall and environmental CO2 concentrations show an opportunity window for yield increment in the early stages. However, a negative trend is observed for 2050–2100, mainly due to crop temperature stress. These findings highlight the importance of developing more resistant ulluco varieties to heat stress conditions, adapting water management practices, continuing modeling climate change effects on crops, and investing in research on smallholder agriculture to reach Sustainable Development Goals 1, 2, and 13.

First Gynogenesis of Vanilla planifolia for Haploid Production and Ploidy Verification Protocol.

Vanilla orchids are members of the Vanilloideae orchid subfamily, and they hold significant economic value as a spice crop in tropical regions. Despite the presence of 180 known species within this subfamily, commercial production focuses on only three species (Vanilla planifolia, V. odorata, and V. pompona) and one hybrid (V. × tahitensis), prized for their aromatic qualities and bioactive compounds. Limited modern breeding initiatives have been undertaken with vanilla orchids, although recent advancements in genomic research are shedding light on this crop's potential. The protracted breeding cycle of vanilla, coupled with increasing demand for germplasm, underscores the importance of research and breeding efforts in vanilla. This paper outlines a protocol for haploid production in V. planifolia using unfertilized ovaries in tissue culture conditions. Additionally, we present a methodology to confirm the haploid nature of putative haploid lines through stomatal size comparison, chromosome counting, and flow cytometry analysis, proving the successful development of haploid vanilla plants. These findings contribute to the advancement of breeding programs and genetic improvement strategies for the vanilla industry.

Analysis and Testing of Pre-Cut Sugarcane Seed Stalk Sawing Performance Parameters

Sugarcane is an important economic crop in tropical and subtropical regions. Presawing planting is an important method for achieving automated and precise planting with sugarcane planting machines. The sawing process is a key stage in planting management, affecting not only the germination and survival rates of sugarcane, but also reflecting the mechanical performance of sawing. To reduce the peak sawing force and enhance the sawing surface quality of sugarcane seedlings, this study utilized a central composite experimental design method. Single-factor and multi-factor experiments were conducted with a specially designed sugarcane stalk sawing experimental rig to investigate the impact of factors such as the stalk diameter feeding speed, and sawing speed on the peak sawing force and sawing surface quality. Upon being developed and validated, multivariate mathematical regression models elucidated the relationships among these factors. The experimental results showed that the order of influence of each factor on the peak sawing force was the stalk diameter, feed speed, and sawing speed, while for the sawing surface quality, the sequence was the sawing speed, stalk diameter, and feed speed. Correspondingly, the determination coefficients for the peak sawing force and sawing surface quality prediction models were 0.9708 and 0.9675. With a maximum error of 7.6% for the peak sawing force and an average relative error of 7.1%, and a maximum error of 3.5% for the sawing surface quality and an average relative error of 2.83%, the calculated results from the regression models were in good agreement with the experimental findings. This indicates that the models are capable of quickly and accurately predicting the peak sawing force and sawing surface quality of sugarcane stalks under different conditions. The research findings provide valuable insights for the development and optimization of sugarcane stalk presawing equipment and related experimental studies.

Optimizing food crop layout considering precipitation uncertainty: Balancing regional water, carbon, and economic pressures with food security

As the global food supply chain becomes increasingly unstable, food security is gaining importance in China. Nevertheless, factors such as water scarcity, limited land resources, and global climate change pose great challenges to increasing food production. Therefore, it is crucial to explore sustainable development models for food systems. In this paper, a multi-objective crop layout model considering crop rotation is constructed and solved using the INSGA2-DS algorithm. Various precipitation scenarios were established for climate change. Data analysis and optimization were conducted under different precipitation scenarios. Applied in the plain area of southwestern Shandong Province, the model can increase soybean yield by 6.78%, net carbon sequestration intensity by 0.04%, net benefit by 2.09%, and crop water productivity by 3.91% during a median flow year. Optimization tends to increase the total area planted with food crops during wet years and decrease it during dry years. The optimization model can enhance water productivity and carbon sequestration intensity of crops, as well as prevent unreasonable fluctuations in the area planted with food crops. This enables the benefits of water conservation and yield increase to be fully utilized, and reduces yield and economic losses due to dry years. The models help to rationally allocate soil and water resources, effectively respond to climate change risks, and provide robust decision support for regional food crop planning.

Chemical constraints are the major limiting factor of root deepening in southern Brazil soils

Large soybean yield gaps are related to irregular rainfall patterns in several rainfed soybean crop regions, such as southern Brazil. More efficient rainwater use can be achieved in these regions through deeper roots. Therefore, assessing the status of soybean rooting and soil-related factors that drive root growth in soybean fields is mandatory for making management decisions that minimize the yield gap. To this end, we conducted a survey in 10 fields in the state of Rio Grande do Sul (RS), Brazil, during the 2021/22 season. Undisturbed soil blocks and cores and disturbed soil samples were collected in trenches up to approximately 80 cm deep when the soybean was at the R5 stage. The roots washed from the soil blocks were scanned to determine the relative root length density (RRLD), whereas the soil cores and disturbed samples were used for physical and chemical analyses. Hierarchical cluster analysis based on the RRLD was performed to group fields according to their similarities in root distribution and soil-related factors. Soybean rooting in RS fields is quite shallow, with >72% of the total RRLD in all fields located within the 0–20 cm layer and <15% deeper than 40 cm in most fields. Chemical constraints, such as high gradient of acidity-related variables along soil profile and low concentration of nutrients below 20 cm depth, limited soybean root deepening in 50% of the fields, while physical constraints were predominant in 30% of the fields. Thus, management strategies for reducing soybean yield gap by increasing rooting depth in fields of RS need to focus on improving subsurface chemical conditions for root growth.

Assessing the impact of climate and crop diversity on regional greenhouse gas emissions and water demand of cropland

To tackle the challenges of climate warming and freshwater scarcity, evaluating greenhouse gas (GHG) emissions and water demand in agricultural production can contribute positively to sustainable agricultural practices. This study aims to quantify the GHG emissions and water requirements of three major grains in diverse environmental regions in China, with a specific focus on exploring their spatial heterogeneity due to regional differences. The parameterized DNDC model was used to quantify crop GHG emissions, while the Penman-Monteith equation was utilized to assess crop water demand. The result showed that the total GHG emission of major grain in China was 372.43 Tg/yr (CO2), 11.68 Tg/yr (CH4), 475.56 Gg/yr (N2O) respectively; and the water demand was 473.60 Gm3, in 2015. The Middle-lower Yangtze Plain (MLYP) had the highest emission and water demand. High-temperature and high-precipitation regions had greater GHG emission intensity; the intensity of different GHG emissions was significantly influenced by the type of crops. Late rice had the highest emission intensity. The geospatial distribution of GHG emissions and water demand displayed opposite patterns, with meteorological conditions and crop types as the main reasons. Soil bulk density was the most significant soil element influencing regional emissions. These results will help to assess the spatial heterogeneity of agricultural “water and carbon”, and the quantitative results can be used as evaluation indicators to establish resource- and environment- friendly agricultural cropping patterns suitable for different cropping regions.

Effect of grafting on success and survivability of jamun (&lt;i&gt;Syzygium cumini &lt;/i&gt;Skeels.) varieties

Jamun (Syzygium cumini Skeels) is a vital fruit crop for arid regions. An investigation was conducted to assess the propagation success of four jamun varieties (CISH-42, CISH-37, Konkan Bahadoli and Goma Priyanka) in the south western region of Punjab. Data were collected on grafting success, graft survival, sprout initiation, number of sprouts and leaves per graft, graft height, and chlorophyll index. The results revealed that the variety Konkan Bahadoli recorded highest graft success (83.7%, 82.8%) and survival rates (76.6%, 73.9%) during both the years, followed by Goma Priyanka. The var. Konkan Bahadoli recorded earliest sprouting (16.6 days, 15.3 days, respectively in both years) and superior growth parameters, including the number of sprouts and leaves, graft height, and chlorophyll index. Overall, the var. Konkan Bahadoli responded best to grafting performed in March under south-western region of Punjab.

Effect of grafting on success and survivability of jamun (&lt;i&gt;Syzygium cumini &lt;/i&gt;Skeels.) varieties

Jamun (Syzygium cumini Skeels) is a vital fruit crop for arid regions. An investigation was conducted to assess the propagation success of four jamun varieties (CISH-42, CISH-37, Konkan Bahadoli and Goma Priyanka) in the south western region of Punjab. Data were collected on grafting success, graft survival, sprout initiation, number of sprouts and leaves per graft, graft height, and chlorophyll index. The results revealed that the variety Konkan Bahadoli recorded highest graft success (83.7%, 82.8%) and survival rates (76.6%, 73.9%) during both the years, followed by Goma Priyanka. The var. Konkan Bahadoli recorded earliest sprouting (16.6 days, 15.3 days, respectively in both years) and superior growth parameters, including the number of sprouts and leaves, graft height, and chlorophyll index. Overall, the var. Konkan Bahadoli responded best to grafting performed in March under south-western region of Punjab.

Recent Trends in the Pre-Drying, Drying, and Post-Drying Processes for Cassava Tuber: A Review.

Cassava tuber is an essential staple crop in tropical regions with versatile applications in the food, feed, and industrial sectors. However, its high moisture content and perishable nature necessitate efficient preservation methods to extend its shelf life and enhance its value. Pre-drying, drying, and post-drying processes play pivotal roles in maintaining the quality and usability of cassava products. This review comprehensively examines the current status and future directions in the pre-drying, drying, and post-drying processes of cassava tuber. Various pre-drying or pretreatment methods and drying techniques are evaluated for their impacts on drying kinetics and product quality. Additionally, challenges and limitations in achieving high-quality processing of cassava flour are identified. Future directions in cassava drying methods emphasize the integration of combined pre-drying and drying techniques to optimize resource utilization and processing efficiency. Furthermore, the adoption of advanced online measurement and control technologies in drying equipment is highlighted for real-time monitoring and optimization of drying parameters. The importance of optimizing existing processes to establish a comprehensive cassava industrial chain and foster the development of the cassava deep-processing industry is emphasized. This review provides valuable insights into the current trends and future prospects in cassava drying technologies, aiming to facilitate sustainable and efficient utilization of cassava resources for various applications.

CHEMICAL COMPOSITION AND INSECTICIDAL EFFICACY OF VITEX NEGUNDO L. ESSENTIAL OIL AGAINST FALL ARMYWORM, SPODOPTERA FRUGIPERDA (J. E. SMITH)

The fall armyworm, Spodoptera frugiperda, is a key pest ofmaize crops in tropical and subtropical regions and has developed resistanceagainst synthetic pesticides and Bacillus thuringiensis toxins. Thus,the experiments were designed to evaluate the chemical analysis andinsecticidal efficacy of Vitex negundo against S. frugiperda.Chemical analysis of V. negundo essential oil revealed a total of 36compounds, and the oil contained more monoterpenes (68.16%) than sesquiterpenes(22.21%) and diterpenes (13.56%). For the ovicidal and insecticidal activities,the essential oil was found to be most effective, with a median lethalconcentration (LC50) value of 51.31 µL/mL and 0.29 L/larvae, respectively.Larval antifeedant and growth inhibitory activity of essential oil werepositively correlated (R2 = 0.9645) and (R2 = 0.9727) with oil concentrations,respectively. Therefore, the V. negundo essential oil can be used forthe development of bioinsecticides to control S. frugiperda, a highlyinvasive and polyphagous pest, which will replace chemical pesticides.

Characterization of High and Stable Yield Genotype for Maize based on Multivariate stability Analysis and Prediction of Crop Yield using Ensemble Learning Techniques

The production of maize globally surpassed the wheat and rice, because it is a staple crop in many regions of the world. Also, in addition to being directly consumed by people, maize is also used to make corn ethanol, animal food and several other products of maize, therefore the stability and high yield of maize is an extremely crucial part to promote long-term growth and food protection. This study looked at the effects of G ? E interaction on yield stability in 23 hybrids of maize in 53 distinct Indian environments. On the basis of analyses of variance, stability tests for multivariate stability parameters were carried out. In terms of all characteristics, the genotype and environment (G ? E interaction) differences were highly significant (p &lt; 0.01), according to the pooled analysis of variance. A GGE biplot was created with the two principal components, which accounted for 66.02% and 8.51% variation in GEI for the corresponding yield per hectare. The GGE biplot and AMMI model exposed genotypes SYN916801, Bio 9682, DKC 9215, NMH 4313 as good with an indication of high mean yield and stability within the environment that were tested. By the results, we suggest breeding might boost output yield, and also the identified genotypes may be suggested for commercial farming. Also, for the creation of successful agricultural and food policy, reliable crop production estimates are essential. So, examinations were made on the machine learning techniques such as random forest and Gradient Boosting algorithm to predict crop yield responses in maize. While assessing statistical performance, GBM was found highly capable of predicting yield as compared to RF. Result showed that GBM is a good and adaptable machine learning technique for yield predictions.. KEYWORDS :Additive main effect and Multiplicative interaction, G ? E interaction, Machine learning techniques, Gradient boosting machine, Random forest.

Arecanut Status Detection Using Deep Learning

Arecanut, a significant cash crop in many tropical regions, undergoes distinct stages of ripening, posing challenges for timely harvest and market readiness. This research presents a comprehensive framework employing cutting-edge deep learning methodologies, specifically TensorFlow Lite, for accurate and real-time detection of Arecanut status, encompassing ripe, unripe, and dry stages. The integration of OpenCV for image preprocessing and deployment on Raspberry Pi enhances the system's accessibility and usability, enabling on-site detection using the Raspberry Pi camera module. The study begins with a comparative analysis between traditional machine learning techniques, particularly Support Vector Machine (SVM), and deep learning architectures. While SVM exhibited commendable accuracy at 75%, the superior performance and scalability of deep learning models motivated the adoption of TensorFlow Lite. Extensive experimentation, conducted on a diverse dataset of Arecanut images, underscored the efficacy of deep learning in surpassing the accuracy thresholds achieved by conventional approaches. The synergy between TensorFlow Lite and Raspberry Pi leverages the computational capabilities of the latter, facilitating efficient deployment in resource-constrained agricultural settings. This synergy enables real-time inference directly on the device, ensuring data privacy, low latency, and autonomy from cloud connectivity, thus empowering farmers with actionable insights at the point of need, distinguishing between ripe, unripe, and dry stages. The experimental validation of our approach across varied environmental conditions underscores its robustness and adaptability. The deployed system not only offers a portable and cost-effective solution but also contributes to the paradigm shift towards precision agriculture, where datadriven decision-making enhances productivity and sustainability.

Mango crop maturity estimation using meta‐learning approach

AbstractMangos are a significant fruit crop that is widely cultivated in tropical and subtropical regions. However, manual detection of mango crop maturity is time‐consuming and labor‐intensive. It is a vital agricultural commodity, and accurate assessment of the mango maturity stage is critical for harvesting and post‐harvest handling. Accurately detecting mango crop maturity is essential for ensuring optimal harvesting and quality. This study introduced a specialized Meta‐learning method for classifying mango crop images with limited data. The conventional approach to maturity classification, often hindered by limited labeled data, is significantly enhanced by meta‐learning's ability to adapt quickly to new tasks with minimal examples. The proposed approach works as initial image segmentation to isolate mango crop regions, comprehensive feature extraction to capture meaningful data representations, and a critical meta‐training. This phase involves refining a classifier within a metric space by utilizing cosine distance and an adaptable scale parameter, this is followed by a meta‐testing stage. where the adapted classifier is utilized for maturity prediction with minimal support samples. This research holds substantial promise for the agricultural sector, offering practical solutions to optimize harvest management practices and improve mango crop yield predictions.Practical applicationsOptimized Harvest Scheduling: Scenario: Farmers can use crop maturity estimation to schedule harvest times more precisely. Application: Knowing crop maturity optimizes harvest, minimizing losses. Resource Management: Scenario: Efficient use of resources such as water, fertilizers, and pesticides is essential for sustainable agriculture. Application: Crop maturity guides resource use, for example, reducing late‐stage water/nutrients prevents overuse, saves resources. Quality Assurance: Scenario: Crop quality is often linked to its maturity level. Application: Maturity estimation assesses crop quality, guiding post‐harvest planning and determining market value. Data‐Driven Decision Making: Scenario: Modern agriculture relies on data for decision‐making. Application: Maturity estimation promotes data‐driven farming, aiding trend analysis, optimizing planting, and enhancing farm management.