Crop Maturity Research Articles

Relative Abundance, Emergence Profile and Parasitization of Lac Insect (<i>Kerria lacca</i>) Associated Fauna in Rangeeni Crops

Relative abundance, emergence profile and parasitization of parasitoids and predators associated with lac insect were recorded during rangeeni summer season (baisakhi) and rainy season (katki) lac crops on ber and palas under two different conditions viz., spray of fipronil + chlorothalonil and only chlorothalonil. It revealed that three parasitoids (Aprostocetus purpureus, Tachardiaephagus tachardiae, Tyndarichus clavicornis) and one predator Eublemma amabilis were abundant in both lac crops. Lac associated fauna were recorded more on lac crop sprayed with chlorothalonil only compared to fipronil + chlorothalonil on both crops on both lac hosts. Among the lac associated fauna, A. purpureus was recorded in more numbers in both crops and spray conditions. Maximum level of parasitization was recorded at the time of sexual maturity period (19 to 22 Weeks After Inoculation (WAI)) during baisakhi whereas in katki, at crop maturity period (16 to 19 WAI) on ber and palas.

SiNF-YC2 Regulates Early Maturity and Salt Tolerance in Setaria italica.

Early maturity is an important agronomic trait in most crops, because it can solve the problem of planting in stubble for multiple cropping as well as make full use of light and temperature resources in alpine regions, thereby avoiding damage from low temperatures in the early growth period and early frost damage in the late growth period to improve crop yield and quality. The expression of genes that determine flowering affects flowering time, which directly affects crop maturity and indirectly affects crop yield and quality. Therefore, it is important to analyze the regulatory network of flowering for the cultivation of early-maturing varieties. Foxtail millet (Setaria italica) is a reserve crop for future extreme weather and is also a model crop for functional gene research in C4 crops. However, there are few reports on the molecular mechanism regulating flowering in foxtail millet. A putative candidate gene, SiNF-YC2, was isolated based on quantitative trait loci (QTL) mapping analysis. Bioinformatics analysis showed that SiNF-YC2 has a conserved HAP5 domain, which indicates that it is a member of the NF-YC transcription factor family. The promoter of SiNF-YC2 contains light-response-, hormone-, and stress-resistance-related elements. The expression of SiNF-YC2 was sensitive to the photoperiod and was related to the regulation of biological rhythm. Expression also varied in different tissues and in response to drought and salt stress. In a yeast two-hybrid assay, SiNF-YC2 interacted with SiCO in the nucleus. Functional analysis suggested that SiNF-YC2 promotes flowering and improves resistance to salt stress.

Aflatoxins in cereal crops and their integrated management: A review

Cereals and cereal-based products are the most common foods consumed by humans globally. Among cereals, rice, maize and sorghum are mostly contaminated by aflatoxins in natural conditions due to changes in agricultural practices. During the early phase of crop development and the second phase of crop maturation, aflatoxins are contaminated. Warm, humid, and even hot deserts and drought conditions increase contamination. The aflatoxins are produced both in pre-harvest and post-harvest conditions. Cereal crops like rice is more prone to aflatoxins contamination as compared to other cereals. The poor drying of rice grains results in greater moisture content (>14%) and the growth of the fungus Aspergillus flavus. Hence, these fungus result in grain and/or husk discoloration, which lowers grain quality. The extent of fungal growth and aflatoxins production in cereals depends on temperature, moisture, soil type, and storage conditions. The current review provides insight into the sources of integrated management practices of aflatoxins to ensure food safety and security.

Photosynthesis rate, radiation and water use efficiencies of irrigated potato in a semi-arid climate using Eddy covariance techniques

AbstractUnderstanding the photosynthetic behaviour, radiation use efficiency (RUE) and water use efficiency (WUE) of potatoes in response to diurnal and seasonal fluctuations in weather, is important for optimizing growth and production. The net ecosystem exchange (NEE) of carbon dioxide (CO2), is a parameter used to measure the balance between gross CO2 assimilation (also referred to as gross primary production (GPP)) and ecosystem respiration (Reco). The objectives of this study were (i) to quantify the NEE of potato grown in two contrasting agro-ecologies and partition NEE into Reco and GPP; (ii) to describe seasonal patterns in daily photosynthesis, RUE and WUE in potato; and (iii) to analyse the variability in photosynthesis rate, RUE and WUE as affected by weather and crop growth stage. We measured CO2 and water vapour fluxes using eddy covariance techniques in potato fields in two production regions of South Africa. The winter crop had higher mean RUE and WUE values, whereas the summer crop absorbed more CO2. RUE had a negative relationship with incident radiation. To optimize dry matter production, WUE and RUE, potato crops must be well established and develop a full canopy early during the season. Maintaining the crop for a longer period in the field has yield benefits, although they appear to decline over time due to reduced efficiencies as the crop matures. The RUE observed over the growing period in the present study was high, relative to findings by other field studies, despite relatively warm growing conditions that were likely sub-optimal for potato.

Ten years of green manuring and biofumigation alters soil characteristics and microbiota

Green manure and biofumigant crops are common within cropping rotations. These practices aim to maintain or increase the yield of agricultural soils by preventing degradation and protecting important ecosystem services, and their benefits to organic carbon levels, soil aggregation, erosion prevention and overall soil fertility are well documented. However, most studies have only examined the short term (one to three year) impacts of green manures and biofumigants, and information on their long term effects is lacking.This study examines the effects of 10 to 13 years of continuous ryegrass (Lolium multiflorum, var. Tetila) green manuring and brassica biofumigation (Brassica juncea, var. Caliente 199) on the soil's chemical, physical characteristics, microbial communities and resulting yields of a fertile, clay rich soil (Red Ferrosol) within an intensive vegetable cropping rotation. The aim was to assess the treatments' efficacy for improving or altering the forementioned parameters to improve sustainable yields, and to examine how the treatments affected the soil's microbial ecology. Soil aggregate stability was assessed at cash crop maturity, and soil chemistry, soil microbiota, and vegetable yields were sampled after the cash crop harvest for three consecutive years. To determine how the soil microbial communities had changed in response to the treatments soil DNA extracts were analysed by high throughput amplicon sequencing.Ryegrass green manuring and brassica biofumigation both altered the soil microbial communities and appeared to favour copiotrophic, organic matter degrading bacteria and fungi. Both treatments increased the relative abundance of arbuscular mycorrhizal fungi (AMF) by 800 % to 1400 % relative to the fallow in two of the three years. Soil aggregate stability was consistently higher in the ryegrass and biofumigant plots and a 20.4 % increase in soil carbon was observed in the treated plots on one side of the trial site. A 20 % increase in cash crop yield (carrots) was recorded in one of the three years.

Machine Learning Based Crop Prediction on Region Wise Weather Data

Agriculture is a primordial occupation for human civilization, whereby farmers cultivate domesticated species of food. It refers to farming in general, which is an art and science that attempts to reform a component of the Earth's exterior through the cultivation of plants and other crops, as well as raising livestock for sustenance or other necessities for the soul and economic gain. As a result of the vital role that sustainable agriculture plays in the overall health of the nation, this sector of the economy has been the incubator for some of the most cutting-edge technological advances in recent history. Scientists and farmers have been working together to discover new methods that will allow them to increase crop production while simultaneously decreasing their water consumption and lessening their negative effects on the environment. Machine learning, deep learning, and a number of other methodologies are some examples of these approaches. A crop's expansion and maturation are both heavily influenced by the climate in which it is grown. The local climate, namely its wind speed, temperature, rainfall, and humidity, is the most exigent factor in determining the advancement or failure of crop production. If the weather is predicted prior to crop cultivation, it will be beneficial to the farmer. Machine learning is a new innovation that can solve people’s real-life problems. It is a technique where a machine can act like a human and learn through experiences and the use of different types of data. Now a day, Agriculture is one of the fields of machine learning where we use different types of machine learning algorithms to predict crop production based on climate data which can benefited farmers to increase the production of the crop. In these studies, we are going to predict crop yield using LSTM based on predicted weather data.

Prediction of Maturity Date of Leafy Greens Based on Causal Inference and Convolutional Neural Network

The prediction of the maturity date of leafy greens in a planting environment is an essential research direction of precision agriculture. Real-time detection of crop growth status and prediction of its maturity for harvesting is of great significance for improving the management of greenhouse crops and improving the quality and efficiency of the greenhouse planting industry. The development of image processing technology provides great help for real-time monitoring of crop growth. However, image processing technology can only obtain the representation information of leafy greens, and it is difficult to describe the causal mechanism of environmental factors affecting crop growth. Therefore, a framework combining an image processing model and a crop growth model based on causal inference was proposed to predict the maturity of leafy greens. In this paper, a deep convolutional neural network was used to classify the growth stages of leafy greens. Then, since some environmental factors have causal effects on the growth rate of leafy greens, the causal effects of various environmental factors on the growth of leafy greens are obtained according to the data recorded by environmental sensors in the greenhouse, and the prediction results of the maturity of leafy greens in the study area are obtained by combining image data. The experiments showed that the root mean square error (RMSE) was 2.49 days, which demonstrated that the method had substantial feasibility in predicting the maturity for harvesting and effectively solved the limitations of poor timeliness of prediction. This model has great application potential in predicting crop maturity in greenhouses.

Impact of climate change on lemon (Citrus limon L.) production in eastern Bangladesh

The study was conducted to assess the impact of climate change on lemon production at Sreemangal of eastern Bangladesh as the area is moderately vulnerable to climate change. The study sample consisted of 80 randomly selected lemon growers who were interviewed and the data were collected to identify their perceptions, social characteristics, and the impact of climate change on lemon production. The results of the survey indicated that the grower’s perception of climate change was impactful as a majority of growers claimed increased annual precipitation (48.8%), increased summer temperature (48.8%), and reduced winter temperature (46.3%). In the case of extreme events, 56.3% of growers mentioned that the intensity of storms has increased in the last 5 years (2015-2019) and 58.8% of lemon growers said that the intensity of rainfall had increased substantially. However, in terms of environmental hazards, the findings indicate that excess rainfall, pest infestation, cloudy skies, hail storms, and drought are the major problems in lemon production. On the surface, overcast skies cause most of the damage to lemon production. Apart from this, soil fertility, pests, diseases, excess temperature, crop sowing time, maturity period, and drought had a significant impact on lemon production in the study area. From the correlation coefficient table, it appears that many socioeconomic characteristics were also influenced by the impact of climate change on lemon production. In the study area, there is a positive significant relationship between changes in lemon production and changes in environment and risks. The lemon crop requires moderate temperatures and average rainfall for improved production. Bangladesh J. Agri. 2022, 47(2): 64-70

Ensuring the stability of the suspended chisel-cultivator processing depth

The depth of tillage and its uniformity are the main performance indicators of chisel cultivators, as well as all tillage machines. Only when these indicators are at the level of demand, favorable conditions are created for the uniform development and ripening of crops throughout the field and their high yield. The article theoretically explores the problem of ensuring a smooth movement of the suspension chisel cultivator along with the working depth. The results show that the given depth of tillage and its uniformity at the required level is mainly due to the correct selection of the vertical distance from the base plain of the chisel-cultivator to the lower hanging points. The vertical distance from the base plain to the lower hanging points must be at least 48.1 cm in order for the condition N>0 to be fulfilled and the chisel-cultivator to sink to the specified depth and run steadily at this depth. Suspended chisel-cultivators with 3-4 class wheeled tractors, which are widely used in agricultural production of the republic, the vertical distance from the base plains to the lower hanging points. Determined to be at least 48.1 cm.

Silver nanoparticles protect tillering in drought-stressed wheat by improving leaf water relations and physiological functioning.

The tillering phase of wheat (Triticum aestivum) crops is extremely susceptible to drought. We explored the potential of silver nanoparticles (AgNPs) in protecting wheat genotypes from drought injury during this sensitive stage. After treating with AgNPs (60ppm), the plants were submitted to different water levels; i.e. 100% field capacity (FC), 75% FC (mild drought), 50% FC (moderate drought) and 25% FC (severe drought) from 15 to 41days after sowing (tillering phase). Leaf physiological data were collected at stress termination, while yield attributes were recorded at crop maturity. We found that increasing drought intensity significantly impaired leaf physiology and grain yield of both studied genotypes. Compared with control, moderately and severely drought-stressed plants produced 25% and 45% lesser grain yield per spike, respectively (averaged across genotypes and years of study). Likewise, moderate and severe drought reduced photosynthesis by 49% and 76%, respectively, compared with control. In contrast, AgNPs significantly restored leaf physiological functioning and grain yield formation at maturity. For example, under moderate and severe drought, AgNPs-treated plants produced 22% and 17% more grains per plant, respectively, than their respective water-treated plants. Our study suggests that exogenous AgNPs can protect wheat crops from drought during early development stages.

Yield, Fiber Quality, and Textile Outcomes from In-Field Blending of Cotton Seed at Planting

Cotton is a highly variable natural material that is routinely blended during textile processing to create a uniform product. Harvesting and ginning can introduce some blending before the mill. Blending earlier in the supply chain could produce a more consistent and predictable product. There has been limited research on the benefits of in-field blending of cotton cultivars, especially from a textile perspective. Experiments were conducted over two seasons to determine the economic and performance impacts of in-field blending. The seed of three cultivars with different quality parameters were blended in combinations of two cultivars at 25% increments before planting. Crop maturity, lint yield, fiber quality, and textile processing were evaluated for both years. Some combinations resulted in differences in micronaire, fineness (linear density), and fiber length, which mostly followed the blend rates of the constituent cultivars. Although there were some statistical differences, no functional differences were observed for yield, textile processing, or textile quality. The most significant result was the movement of one set of blends from the base range of micronaire to the premium range. The results showed that infield blending of cultivars could be done without harming quality or resultant textiles, as long as the cultivars are carefully selected for similar seed characteristics and maturation timing. Individual producers will have to determine if the benefits and risks are worthwhile for their specific situation.

Sclerotia of rice false smut disease in Bangladesh

It is perceived that sclerotia of the pathogen, in addition to chlamydospores, play an important role in the epidemiology of rice false smut disease. The propagule has been identified only in five rice growing countries of the world, and never recorded in Bangladesh. This study was undertaken to identify the sclerotia, quantify their concentration on smut balls and estimate the time of its formation. Intensive field-by-field monitoring was conducted during 2014, 2015 and 2016 at experimental farm of the Bangladesh Rice Research Institute, Gazipur. After rigorous search, sclerotia of rice false smut pathogen was identified on the 14th of December 2014. The was observed in the later part of crop maturity during the ‘T. Aman’ season (early-November to mid-December) on olivaceous greenish-black smut balls, but not on orange smut ball. During the three years study, on average, 30.61±9.79% (± is 95%confidence interval) olivaceous greenish-black smut balls borne sclerotia, the average number being 1.63±0.09 per sclerotia-bearing ball. The ‘threshold temperature’ for sclerotia formation was estimated as above 11°C difference in day-night temperature. Consistence presence and high concentration of sclerotia in smut balls signify its role in the epidemiology of the disease in the agroecology of Bangladesh.

The use of remote sensing, ground survey and the yield mapping system in the conditions of northern Kazakhstan for food production and food security

Food security is among the most important aspects of our physical health; it is necessary to live a full and peaceful life. Food security is a wide term that encompasses more than crop production since it necessitates taking into consideration geographical and temporal variability, along with economic and physical availability. Drought and salt stress are two major variables that restrict agricultural yield. These pressures are expected to worsen as a result of climate change, posing even greater threats to global food security. Earth remote sensing (ERS) using unmanned aerial vehicles is operational monitoring of the state of fields, the quality of processing of fallow lands; provides control of the degree of weediness of fields during the period of spring fieldwork, which makes it possible to rationally distribute agricultural machinery for closing moisture, intermediate treatments and pre-sowing chemical treatments sowing quality control; monitoring the vegetation index makes it possible to construct effective schemes for plant feeding, as well as effectively distribute agricultural machinery during the harvesting period, depending on the degree of crop maturation. The yield mapping system allows evaluating the effectiveness of precision agriculture techniques, making it possible to accumulate data on productivity zones, considering the actual yield.

Identification of suitable chemotype of Ocimum gratissimum L. for cost effective eugenol production

Clove basil ( Ocimum gratissimum L.) is an aromatic herb and grown across tropical and sub tropical parts of the world. It is grown in disturbed area and cultivated due to broad-leaves among basils. It is rich in essential oil and extensively used in traditional medicines of Asia and Africa. Flower bud of Syzygium aromaticum L. and bark from Cinnamomum verum L. are general source of natural eugenol production. Cost of cultivation and commercial extraction of eugenol from these trees are very high and time taking due to long gestation period essential for crop maturity and harvesting. It induced an idea for evaluation of available elite accessions rich in herbage and quality components . The experiment was conducted in Kharif 2021 in RBD design with seven replications. Three diverse accessions were observed significant variation for leaf morphology, quantitative, qualitative and molecular parameters under evaluation. Essential oils of the three accession were found to be rich in eugenol. Among the three accessions α-bergamotene and β-copanene were found to be the other major compounds in DOGr-1 and DOGr-2, while α-bergamotene and caryophyllene were found to be the other major compounds in DOGr-3. Maximum yield in terms of herbage, essential oil and eugenol content was observed in DOGr-3. DOGr-3 was found to be unique and diverse for phenotypic characters, yield contributing traits, chemical content and genetic makeup. • Selected accessions are diverse in nature for morphology, quality and genetic level. • Essential oils of accession DOGr-2 found rich in α-bergamotene and β-copanene. • Maximum herbage, essential oil and eugenol yield were observed in DOGr-3. • DOGr-3 is a cheaper substitute for natural eugenol production.

A cultivar phenology classification scheme for wheat and barley

The relative time to maturity of grain crops is an important consideration for producers, yet there are no universally accepted classification schemes for cultivar phenology to guide decisions on variety selection and time of sowing. A first edition of an industry guide for wheat variety maturity was recently developed for use across Australia, representing a significant step forward for the grains industry. The aim of this paper was to revise and extend this industry guide to make it more robust, agronomically functional and meaningful to industry. The Australian Cereal Phenology Classification (ACPC) presented herein was developed using an unprecedented phenological data set with a diverse array of genotypes, environments and management. Field experiments were carried out with 70 wheat and 30 barley cultivars at 15 sites across Australia between 2017 and 2020. Thermal time to anthesis data were used to rank cultivars according to their relative phenology and divide them into classes, and then boundary cultivars of both species were selected to separate these classes. The resulting classification scheme divides wheat and barley into phenology classes ranging from ‘quick’ to ‘mid’ to ‘slow’. New cultivars to market can be assigned a phenology classification based on their thermal time to anthesis relative to the boundary cultivars. The ACPC will help growers, agronomists, breeders and researchers make informed decisions regarding cultivar comparison and selection while reducing misclassification and confusion across regions. The same methodology used to derive and validate the ACPC can be applied internationally to standardise descriptions of crop phenology.

Abundance and variation of gaseous NH3 in relation with inorganic fertilizers and soil moisture during Kharif and Rabi season.

In an agricultural country like India, inorganic fertilizers are the major contributors of atmospheric NH3 in rural areas affecting soil, vegetation and water bodies. In this study, day-night and seasonal variation of ammonia emissions were measured from July 2017 to June 2018 during Kharif and Rabi crop seasons at a rural agricultural site in Jhajjar district of Haryana. Also, NH3 emission inventory is prepared for the amount of fertilizers applied during its basal and top dressing. NH3 concentrations were noticed significantly lower after basal dressing of DAP fertilizers as compared to the concentrations after top dressing of urea. NH3 concentration in air increased with decrease in water saturation of the soil. NH3 emission was recorded as 1.4 to 45.2, 63.1 to 190.9, and 98.9 to 187.5μgm-3 during sowing, fertilizer addition, and grain filling stages, respectively, in Kharif season. Apart from these crop stages, NH3 was measured as 56.8 to 249.5μgm-3 during crop residue burning period. On the other hand, NH3 emissions ranged from 22.9 to 68.4, 59.4 to 104.71, 26.3 to 56.0, 48.2 to 147.2, and 21.5 to 80.4μgm-3 during sowing, crown root initiation (CRI), panicle initiation, grain filling, and maturity crop, respectively, in Rabi season. The average NH3 concentrations during Kharif season (125.3μgm-3) were significantly greater than the concentrations during Rabi season (51.8μgm-3). However, a reduction in the NH3 values was observed in the period between Kharif and Rabi seasons, which could be attributed to the wet deposition during monsoon and gas to particle conversion due to less temperature conditions during the periods.

Root system architecture and environmental flux analysis in mature crops using 3D root mesocosms.

Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft3 (1.27 m3), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.

Prediction of Buckwheat Maturity in UAV-RGB Images Based on Recursive Feature Elimination Cross-Validation: A Case Study in Jinzhong, Northern China

Buckwheat is an important minor grain crop with medicinal and edible functions. The accurate judgment of buckwheat maturity is beneficial to reduce harvest losses and improve yield. With the rapid development of unmanned aerial vehicle (UAV) technology, it has been widely used to predict the maturity of agricultural products. This paper proposed a method using recursive feature elimination cross-validation (RFECV) combined with multiple regression models to predict the maturity of buckwheat in UAV-RGB images. The images were captured in the buckwheat experimental field of Shanxi Agricultural University in Jinzhong, Northern China, from September to October in 2021. The variety was sweet buckwheat of "Jinqiao No. 1". In order to deeply mine the feature vectors that highly correlated with the prediction of buckwheat maturity, 22 dimensional features with 5 vegetation indexes, 9 color features, and 8 texture features of buckwheat were selected initially. The RFECV method was adopted to obtain the optimal feature vector dimensions and combinations with six regression models of decision tree regression, linear regression, random forest regression, AdaBoost regression, gradient lifting regression, and extreme random tree regression. The coefficient of determination (R2) and root mean square error (RMSE) were used to analyze the different combinations of the six regression models with different feature spaces. The experimental results show that the single vegetation index performed poorly in the prediction of buckwheat maturity; the prediction result of feature space "5" combined with the gradient lifting regression model performed the best; and the R2 and RMSE were 0.981 and 1.70 respectively. The research results can provide an important theoretical basis for the prediction of the regional maturity of crops.

Weed Detection in Soybean Crop Using Deep Neural Network

The problematic and undesirable effects of weeds lead to degradation in the quality and productivity of yields. These unacceptable weeds are close competitors of crops as they constantly devour water, air, nutrients, and sunlight which are helpful for the maturation of crops. For better cultivation and good quality production of crops, weed detection at the appropriate time is an essential stride. In recent years, various state-of-the-art (SOTA) architectures were proposed to detect weeds among crop yields, but they lacked computational cost. This paper mainly focuses on proposing a customized state-of-the-art (SOTA) architecture and comparative study with transfer learning models for detecting and classifying weeds among soybean crops by concentrating on the low computational cost. The selected SoTA is beneficial for detecting weeds on a large scale with very low computational costs. In terms of selection, Maximum Validation Accuracy (MVA), Least Validation Cross-Entropy Loss (LVCEL), and Training Time (TT) were considered for proposing an objective function value system. In total, 15 proposed CNNs with 18 Transfer learning models were analyzed with the help of objective function value and various metric evaluations for finding the best and optimal architecture for weed classification. Experimentation and analysis resulted in C13 being robust and optimal architecture which outperformed every CNNs and Transfer learning model by achieving the highest accuracy of 0.9458 with an objective function value of 5.9335 and ROC-AUC of 0.9927 for the classification of weeds from soybean crops.

Pre-Harvest Sprouting Tolerance in 36 Bread Wheat Genotypes

Pre-harvest sprouting (PHS), promoted by rainfall during crop maturity, is a high problem in many wheat-producing regions of the world. Considering its importance in Brazil, 36 national and international varieties and advanced lines of wheat were evaluated for their tolerance to PHS. For this purpose, two experiments were conducted over three years. Seed pericarp rupture was used as an indicator of the beginning of germination. The data were analyzed using analysis of variance, the Scott-Knott test, and the Lin and Binns method. The wide range of germination percentage values allowed the genotypes to be classified as tolerant (in experiment 1 - ND 674 and Grandin*2/RL 4137 and experiment 2 - Frontana and Grandin) and moderately tolerant (Alsen, CD 114, and Milan/3/Attila//Fang 69/CIMMYT 3 in Experiment 1; Avante, BRS 177, IAC 5-Maringá, Onix, OR 1, RL 4137, and Rubi in Experiment 2). Because tolerance to PHS is under genetic control and can be improved through breeding programs, the challenge for wheat breeders is to combine increased PHS tolerance with other requirements to meet market demands.