<div style="text-align:justify">Background: The shading caused by maize inhibited the growth of soybean, reduced the yield of soybean under maize-soybean relay strip intercropping system. To investigate whether foliar spraying different types of plant growth regulators could improve the growth and yield of intercropped soybean. Methods: A field experiment was conducted in 2018 to evaluate the effect of foliar spraying 6BA (6-benzylaminopurine), S3307 (uniconazole) and DTA-6 (diethyl aminoethyl hexanoate), at the soybean beginning flowering stage, on the growth and yield of three soybean varieties (i.e., Nandou 25 (ND25), Gongqiu 8 (GQ8) and Guixia 3 (GX3)). Result: The leaf area index of three soybean varieties were highest in DTA-6 at 35 days after spraying (DAS) and no significant difference (p&lt;0.05) were observed among three plant growth regulators. The chlorophyll a and chlorophyll b content under 6BA and DTA-6 treatments were significantly higher than CK and S3307 at 35 DAS. The activities of antioxidant enzymes (i.e., SOD (Superoxide dismutase) and CAT (Catalase) and antioxidant substances (i.e., GSH (Glutathione) were increased after spraying 6BA, S3307 and DTA-6 in three soybean varieties at 35 DAS. Independent soybean varieties, foliar spraying 6BA, S3307 and DTA-6 increased the soybean yield, pods per plant, 100-grains weight and grain per plant and the effect of DTA-6 (60 mg l -1) treatment were best than others.</div>

<div style="text-align:justify">Plant Growth Regulators Increase Soybean Yields by Delaying Leaf Senescence in Maize (Zea mays L.)-Soybean [Glycine max (L.) Merr] Relay Strip Intercropping System</div>

<div>Background: A two-year study was conducted at Giza Agricultural Research Station, Agricultural Research Center (ARC), Egypt, to evaluate the productivity of six genotypes of soybeans under three mineral nitrogen (N) fertilizer rates, as well as their resistance to insects, in comparison to four check varieties. 
 
Methods: The study took place during the 2021 and 2022 summer seasons. The treatments included three mineral N fertilizer rates (N1=67% of N fertilizer rate &ldquo;71.4 kg N/ha&rdquo;, N2=33% of N fertilizer rate &ldquo;35.7 kg N/ha&rdquo; with seed inoculation by Bradyrhizobium japonicum and N3=100% of N fertilizer rate of the recommended rate &ldquo;107.1 kg N/ha&rdquo;) and ten soybean genotypes (H4L4, H6L198, H18L54, H29L115, H129, Misr 10, along with Dr101 and Giza 111 &ldquo;resistant&rdquo; and Giza 82 and Crawford &ldquo;susceptible&rdquo;). The experiment used a split plot design with three replications. The main plots were assigned the mineral N fertilizer rates, while the subplots, were assigned the soybean genotypes.&nbsp; &nbsp; 
 
Result: Fewer cotton leaf worms, whiteflies and leaf miners were found after applying N1 or N2. In the 6th week from sowing, Misr 10 and Dr 101 had fewer cotton leaf worms and whiteflies. In contrast, throughout the 7th, 8th and 9th weeks from sowing, Misr 10 and H6L198 had fewer cotton leaf worms. Misr 10 had fewer leaf miners in every week and fewer whiteflies in the 7th, 8th and 9th weeks from sowing. In the 6th week of the first season, Misr 10, Dr101, H18L54 and Giza 111 that received N1 or N2 had fewer cotton leaf worms; in the 7th and 9th weeks from sowing, Misr 10, Dr101 and H6L198 harbored fewer whiteflies. The lowest pod weight/plant, seed yield/plant, 100-seed weight, seed yield/ha and HI were obtained by using N3. Higher biological yield/ha, seed yield/plant, 100-seed weight, seed yield/ha and HI were attained by Misr 10. In the second season, seed yield per plant and seed yield per hectare were significantly affected by the interaction between soybean genotypes and mineral N fertilizer rates. Growing Misr 10 with 67 per cent N of the recommended rate increased seed yield/plant and seed yield/ha, along with fewer cotton leaf worm, whiteflies and leaf miners compared to the commercial cultivar Giza 111 receiving the recommended mineral N fertilizer rate.</div>

Response of Some Soybean Genotypes to Insect Infestation under Three Mineral Nitrogen Fertilizer Rates

Background: Soybean, a vital global crop, faces threats from diverse leaf diseases impacting yield and quality. By utilizing cutting-edge Machine Learning-Convolutional Neural Network (CNN) models, this study develops a Smart Detection System for the precise identification of soybean leaf diseases. 
 
Methods: Convolutional Neural Networks (CNNs) are used in this study to identify soybean leaf diseases. Different images that depict different diseases are used to train the CNN model. The dataset obtained from Mendeley includes three essential categories: Diabrotica Speciosa, Caterpillar and Healthy soybean leaves. Labeling, grayscale conversion and scaling are all part of image processing.&nbsp;80% of the dataset is used for training, 20% is used for validation and the accuracy of the model is assessed. 
 
Result: The CNN model showcases exceptional capabilities, achieving an impressive 95 per cent accuracy in precise soybean leaf disease classification. The Smart Detection System emerges as a powerful and timely tool for disease identification, holding significant implications for advancing precision agriculture. This study underscores the transformative potential of advanced machine learning in reshaping sustainable soybean crop management practices.

Soybean Leaf Disease Identification Through Smart Detection using Machine Learning-convolutional Neural Network Model

<div>Background: Alternaria leaf spot caused by Alternaria alternata is one of the most important and destructive disease of soybean causing severe yield loss in all soybean growing areas of southern and eastern part of Rajasthan. Successful management of Alternaria leaf spot is mainly dependent on accurate and efficient detection of pathogen, amount of genetic and pathogenic variability present in pathogen population. The main reason for frequent &ldquo;breakdown&rdquo; of effective resistance is the variability that exists in the pathogen population, which necessitates a continual replacement of cultivars due to disease susceptibility. 
 
Methods: The twelve fungal isolates randomly were collected from six districts of major soybean growing part of Rajasthan i.e., Udaipur, Chittorgarh, Pratapgarh, Kota, Baran and Jhalawar. The culture was purified single spore techniques. These were then further compared among each other for any variations in cultural characters, colour of colonies, Growth rate, conidial morphology and pathogenic variability. 
 
Result: Twelve different isolates of A. alternata were obtained in pure culture and characterized for cultural, morphological variation and aggressiveness of this fungus varied in their cultural characters, colour of colonies, growth rate of isolates, conidial morphology and isolates also exhibited variations in incubation period, latent period, number and size of lesions were produced.</div>

Cultural, Morphological Characterization and Aggressiveness of Alternaria alternata Causing Leaf Spot of Soybean

Background: Soybean is one of the important leguminous crops grown mainly in the middle states of India. Soybean plants are prone to leaf diseases like spot and bacterial blight. Early detection of such diseases is an important task for the farmers to avoid loss in production. In this background, deep learning techniques are used for identifying the diseases in leaves of Soybean plants. 
&nbsp; 
Methods: This study investigates the utilization of convolutional autoencoder in extracting features from the lesion leaf images. Images are preprocessed and converted to latent space features using convolutional autoencoder. Multinomial logistic regression model is employed over the extracted features to find the type of disease in Soybean plants. 
&nbsp; 
Result: The experimental result shows that convolutional autoencoder model along with multinomial logistic regression model achieves an average accuracy of 92% in disease identification. In addition to providing disease identification, the ideas in this paper provide support for food security through the application of deep learning techniques.

Detection of Leaf Diseases in Soybean Plant using Autoencoder and Multinomial Logistic Regression

<div>Background: Crops are constantly exposed to various organisms feeding on above and belowground parts under both natural and protected conditions. As compare to open field, the warm and humid conditions besides abundant food under protected conditions provide a stable environment for pest and disease development either alone or together. However, the natural enemies i.e. predators, parasites and parasitoids that keep pests and pathogens under control naturally are not present under protected environment. For these possible reasons, pest and pathogens often develop in polyhouses more rapidly and with greater severity than open fields. 
 
Methods: A polyhouses study was carried out during last two months June and July in Patudi block of district Gurugram, Haryana where growers are facing failure of cucumber crop due to outbreak of insect pests- disease complexes on common host cucumber. On closer observation, symptoms of leaf miner as mining lines on leaves and galls or knots on roots the symptoms of root knot nematode were encountered. Further detailed lab based investigation were carried out on affected cucumber plant and root samples with the objective for isolation and identification of both pests and pathogens. For leaf miner morphological identification, the pattern of the feeding tunnel and the layer of the leaf being mined by leaf miner is one of the diagnostic key are useful to determine the species and instar&nbsp;of the leaf miner. However, Sieving and decantation method for nematode isolation and soil dilution method for fungal isolation were carried out and followed by morphological identification.&nbsp; 
 
Result: Two types of herbivore i.e. leaf miner Liriomyza trifolii&nbsp;(Burgess) on aerial parts while root knot nematode, Meloidogyne javanica and wilt causing fungus Fusarium oxysporum f. sp. cucumerinum were identified on the basis of their specific morphological characters. Galled roots of cucumber were affected by combined attack of vascular bundle feeders, root knot nematode, Meloidogyne javanica and wilt causing fungus Fusarium oxysporum f. sp. cucumerinum identified in both soil and root samples. This kind of field report on cucumber crop under protected cultivation has not been reported so far in which three types of pest and pathogens are feeding on the same host. Poly houses or &lsquo;hot spots&rsquo; are selected for detailed investigation especially for growers&rsquo; friendly management in addition to interaction studies of upper and lower ground herbivores on cucumber crops.&nbsp;</div>

<div>Outbreak of Leaf Miner and Root Knot Nematode-wilt Disease Complex on Cucumber- A New Threat under Protected Cultivation in District Gurugram Haryana</div>

Background: Agricultural specialists usually examine leaves closely to check for plant diseases, that process takes a long time and can have errors. To overcome these problems, nowadays, machine learning methods, specifically sequential convolution neural networks (CNN) are widely used, because of their extensive potential to extract features and patterns from the image dataset.&nbsp; 
 
Methods: This study introduces a complete methodology for detecting diseases in soybean plants by employing Convolutional Neural Networks (CNNs). A dataset sourced from the Mendeley database, containing images of soybean legume crops affected by caterpillars, Diabrotica Speciosa diseases and undamaged (healthy) foliage is used., The research explores the efficacy of the CNN Model in accurately classifying these diseases. The CNN algorithm is trained in such a way that it can handle the complex nature of soybean leaf imaging by utilizing convolutional layers for extracting features, pooling layers for reducing dimensionality and softmax layers for classification.&nbsp; 
 
Result: The training and validation results exhibit remarkable accuracy rates (97.64% accuracy) after 150 epochs. The evaluation metrics, such as precision, recall and F1-score, demonstrate the model&rsquo;s performance across different leaf diseases of soybean, suggesting its ability to accurately identify instances inside each category. The classification matrices provide complete knowledge of the accuracy of the prediction of different diseases. The overall accuracy of the proposed model is 94.05%. This study can be utilized as a reference to increase the progress of agricultural disease detection, in turn, can enable enhance the reduction of crop losses and ensure food security.

Early Leaf Disease Detection of Soybean Plants using Convolution Neural Network Algorithm&nbsp;

The critical problem of soybean seed is susceptibility to mechanical processes due to structural limitations of seed and seed coat. &nbsp;The X-radiography of soybean seed revealed the orientation of seed coat around the cotyledons and cultivars like JS 335, JS 9752, JS 71-05, JS 95-60, MACS 124, Pusa 40, Punjab 1 and Type 49 had very compact seed coat around cotyledons. In NRC 12, NRC 37, RKS 18 and SL 525 where the gap between seed coat and cotyledons was significantly very high and non uniform making seed coat very much vulnerable to cracking. Seed coat minerals- calcium, silicon, zinc, iron and lignin content were found to vary significantly among cultivars. Significant genotypic variability for strength of seed coat was observed among the soybean cultivars. A very close relationship was found between strength of seed coat and seed coat compactness. Highly significant correlation was found between mechanical strength and seed coat lignin, iron and zinc content. Quantitative and qualitative variation for seed coat protein among soybean cultivars was revealed and higher protein content was found linked with mechanically strong seed coat. The result revealed that selection for these seed coat parameters in varietal improvement programme may be very fruitful for development of varieties having resistance to field weathering and mechanical damage.&nbsp;

Influence of seed coat compactness around cotyledons, protein and&nbsp;mineral composition on mechanical strength of soybean&nbsp;[Glycine max (L.) Merrill] seed coat

Background: Poultry meat production, particularly broiler chicken, plays a crucial role in meeting the protein demands of Asia&rsquo;s growing population. Cassava, a widely available regional crop, shows promise as an alternative feed ingredient, particularly its nutrient-dense leaves, which provide protein, minerals and vitamins. 
 
Methods: A 35-day feeding trial was conducted to assess the effects of replacing soybean meal with cassava leaf meal (CLM) in broiler diets. Four groups of 100 Arbor Acres broilers (five replicates per group, 20 birds per replicate) were assigned different diets: control (basal diet), T1&nbsp;(10% CLM), T2&nbsp;(20% CLM) and T3&nbsp;(30% CLM). Growth performance, hematobiochemical parameters and meat quality were evaluated. 
 
Result: Broilers fed cassava leaf meal showed significantly (P&lt;0.05) higher body weight than the control, though higher cassava levels led to reductions. Feed intake and FCR varied across weeks, with optimal performance observed at 10-20% inclusion. Mortality rates decreased significantly (P&lt;0.001) in all treatment groups. Biochemical parameters indicated improved protein metabolism, lower cholesterol and enhanced antioxidant activity. Hematological analysis showed increased WBC and neutrophil counts, with reduced lymphocytes. Muscle composition revealed higher metabolizable energy and crude fiber, while meat quality improved with reduced cooking loss and enhanced water-holding capacity.

Effect of Cassava (Manihot esculenta Crantz) Leaf Meal as Partial Substitute for Soybean Meal on Growth Performance, Blood Profile and Meat Quality of Broilers

Background: Groundnut (Arachis hypogea L.) is one of the most important oilseed crops in India, playing a crucial role in the country&rsquo;s economy. This crop is vulnerable to various insect pests starting from vegetative to maturity phase, of which, the leaf miner Aproaerema modicella (Deventer) (Lepidoptera: Gelechiidae) cause substantial yield loss especially in rainfed groundnut cultivation. Most of the farmers were reluctant to adopt integrated management strategies and prefer to take up only chemical pesticides. Biological control is one of the effective management options since the groundnut ecosystem harbours several biological agents. Natural biocontrol agents including hymenopteran parasitoids like Bracon gelechiae, Goniozus sp., Apanteles sp., Stenomesius japonicus etc. plays a vital role in managing the pest under irrigated ecosystem. Only limited studies were available on the effectiveness of these natural bio-control agents in rainfed ecosystem and hence, present study was focused on the natural parasitization of A. modicella in rainfed groundnut ecosystem. 
&nbsp; 
Methods: The field survey was conducted during Summer and Kharif, 2023 in Nambiyur block, Erode District, Tamil Nadu, India to record the natural parasitization percentage on 10 randomly selected plants at weekly interval. The parasitized larva and pupa of A. modicella were collected and reared under laboratory conditions to record the emergence of parasitoids The natural parasitization was correlated with the weather parameters prevailed during the cropping season. 
&nbsp; 
Result: Bracon sp. has been identified as the major natural enemy of A. modicella under rainfed ecosystem. During Summer 2023, minimum parasitization level of 8.0 per cent was observed in the 12th&nbsp;Standard Mean Week (SMW) i.e., second fortnight of March. Subsequently, the parasitization levels were increased during Kharif, 2023 with the moderate (18.00%) during 23rd to 30th SMW i.e., June-July and peaked (26.00 %) during 31st&nbsp;to 33rd&nbsp;SMW i.e., July-August months. The correlation analysis results revealed that the maximum temperature (0.768), minimum temperature (0.533), rainfall (0.015) and Sunshine hours (0.483) were positively correlated and Relative humidity (-0.146) was negatively correlated with the per cent parasitization.&nbsp;

Natural Field Parasitization of Groundnut Leaf Miner, Aproaerema modicella (Deventer) and Influence by Weather Parameters in Rainfed Groundnut Ecosystem