Background: Diseases have always been considered one of the major challenges for the efficient and profitable growing of potatoes, with such diseases as Early Blight and Late Blight causing high yield loss and financial costs all around the world. Even though deep learning algorithms proved to be quite successful in the task of automated plant diseases identification, their efficiency is often hampered due to class imbalance in the datasets, lack of annotated agricultural datasets and low performance in practice, due to bad generalization from training to real-world situations. Besides, most existing researches concentrate on the issue of overall accuracy, ignoring the question of class-wise accuracy, especially for minority classes of the diseases that are important for practical disease diagnosis. That is why there is a necessity in developing a robust classification system that will be able to deal with class imbalance in the datasets and give reliable and accurate disease identification. Methods: In the current paper, an ensemble deep learning framework for potato leaf disease classification using imbalanced dataset is proposed. The methodology includes the following steps: image preprocessing, data augmentation, transfer learning, ensemble learning, and recommendation of a fertilizer. The images of potato leaves were divided into three classes: Healthy, Early Blight, and Late Blight. Four deep learning classifiers-Custom CNN, ResNet50, VGG16 and MobileNetV2 were used as the base learners using transfer learning when possible. The predictions of the models were combined using Hard Voting, Soft Voting, and Stacking ensembles. The evaluation of the framework was conducted with the help of Accuracy, Precision, Recall, F1-score, ROC-AUC, 5-fold cross-validation, confidence intervals and class-wise performance. Also, the agronomic decision-making module for fertilizer recommendation was included in the system. Result: As shown by experiments, transfer learning models perform much better than the Custom CNN base model, while ensemble learning improves the classification accuracy even more. Among the tested methods, the proposed Stacking ensemble gives the best results with the accuracy of 99.70%, precision of 99.70%, recall of 99.70% and F1-score of 99.70%. It also shows the smallest variability of the predictions (standard deviation 0.0012) and narrowest 95% confidence interval (0.9955-0.9985). Besides, the proposed method demonstrates superior class-wise performance, which confirms its efficiency for dealing with the problem of class imbalance in agricultural datasets.
Background: Traditional waste management often struggles to efficiently recycle organic waste into high-quality soil amendments. This study evaluated the efficacy of the earthworm Eudrilus eugeniae in converting black tea waste (BTW) and floral waste (FW) into nutrient-rich vermicompost, comparing the outcomes against standard composting methods to find a sustainable environment. Methods: Five distinct treatment groups (T1 to T5) were constructed using varying percentages of black tea waste and floral waste. These organic waste mixtures were subjected to E. eugeniae-mediated vermicomposting and evaluated against standard composting control setups to measure biodegradation efficiency, nutrient enrichment and chemical stability. Result: Vermicomposting significantly enhanced waste biodegradation, leading to superior humification and stability compared to standard compost (p less than 0.01). Chemical Properties: Vermicompost achieved near neutral pH (up to 7.46), optimal electrical conductivity (up to 2.4 dS/m) and decreased total organic carbon and C/N ratios. Nutrient Profile: Macronutrients (TN, TP, TK), available NPK, total calcium and total magnesium all demonstrated significant comparative increases (p less than 0.01). Optimal Treatment: Treatment T5 (20% BTW+80% FW) performed the best, exhibiting the highest breakdown efficiency, the lowest C/N ratio and the greatest overall nutritional enrichment, demonstrating that E. eugeniae-mediated vermicomposting is a superior strategy for transforming organic wastes into high-quality biofertilizer.
Hyperspectral imaging (HSI) has emerged as a game-changing technology for precision agriculture because it captures detailed spectral signatures over hundreds of finely-resolved, continuous wavelength bands. This feature makes possible a detailed description of species-specific plant physiological states to enable early, precise and non-destructive identification of diseases in crops like wheat. With the rising demands worldwide for food security and healthy cultivation, the application of HSI in agro-diagnostics has expanded considerably. This paper provides a comprehensive and state-of-the-art review of hyperspectral imaging strategies devoted to wheat disease discrimination. It essentially introduces the theoretical backgrounds of HSI, such as data acquisition, preprocessing and spectral-spatial feature extraction. The survey further details a plethora of analytical approaches from classical statistical learning methods to deep learning and their utility in modelling HSI data. In particular, a comparative study of these methods is carried out and discussed for distinguishing healthy and diseased wheat, as well as different types of diseases under diverse environmental conditions. The paper also considers the benchmark datasets, sensors and platforms (field/laboratory-based) for which there are practical limitations. Key challenges are addressed, such as data dimensionality, variability as a consequence of atmospheric conditions and the requirement for online implementation in open-field scenarios. A set of suggestions for applying HSI in other domains has been proposed, such as the fusion of HSI with additional remote sensing information, introduction of compact models to edge devices and construction of a scalable and economical solution of HSI in commercial agriculture.
Background: Productivity of fodder cowpea (Vigna unguiculata L.) in sandy loam soils is often constrained by inadequate phosphorus and zinc availability. Balanced fertilization is essential to enhance nutrient uptake, fodder yield and soil fertility. Methods: In Andhra Pradesh’s Southern Agro-Climatic Zone, a field experiment was carried out during the rabi season. The effects of zinc (0, 25 and 50 kg ZnSO4 ha-1 as factor-II) and phosphorus (0, 20, 40 and 60 kg P2O5 ha-1 as factor-I) on nutrient uptake, post-harvest soil nutrient status and cowpea fodder output were investigated using a factorial randomised block design. Result: Application of 60 kg P2O5 ha-1 significantly increased nutrient uptake and produced the higher green and dry fodder yields. Zinc application at 50 kg ZnSO4 ha-1 significantly enhanced fodder yield and nutrient uptake and was at par with 25 kg ZnSO4 ha-1. Higher nutrient levels reduced residual soil nitrogen and potassium due to increased uptake, while soil phosphorus and zinc status improved. Interaction effects were non-significant. Combined application of 60 kg P2O5 ha-1 with 25 kg ZnSO4 ha-1 was found optimal for fodder cowpea.
Background: Effective weed management is critical for improving rice productivity in transplanted rice. This study evaluated the effect of different adjuvants on unmanned aerial vehicle (UAV)-based herbicide application for weed management in transplanted rice during the Navarai season (January-May) of 2025-26. Methods: The experiment was conducted in a Randomized Block Design with seven treatments replicated thrice. Florpyrauxifen-benzyl + Cyhalofop-butyl @ 150 g ha-1 was applied at 20 days after transplanting (DAT) using UAV with three different adjuvants [silicon-based adjuvant (T1), all-purpose spray adjuvant (T2) and methylated seed oil adjuvant (T3)], compared with UAV application without adjuvant (T4), knapsack sprayer application (T5), weed-free check (T6) and weedy check (T7). Result: The experimental findings indicated that UAV-based herbicide application with silicon-based adjuvant (T1) recorded the lowest weed density and dry weight at 40 and 60 DAT, achieving 76.65% and 74.99% weed control efficiency, respectively. Silicon-based adjuvant treatment also resulted in the highest grain yield of 4,335.6 kg ha-1, which was comparable to the weed-free check (4,464.2 kg ha-1) and significantly superior to other treatments. The results demonstrated that adjuvants play a crucial role in enhancing herbicide efficacy of UAV spray, with silicon-based adjuvants providing superior spray characteristics and herbicide performance compared to oil-based or multipurpose adjuvants. The study conclusively showed that UAV-based herbicide application combined with appropriate adjuvants offers an effective, sustainable and economically viable strategy for weed management in transplanted rice.
India’s economy is heavily reliant on agriculture, with a diverse array of crops grown on vast tracts of land. Fruit cultivation, especially papaya, has become more popular in recent years because of its high nutritional and financial value. To increase yield, maximize resource use and lessen reliance on chemical pesticides, modern techniques like protected cultivation and hydroponics are being used more and more. Fruit crops grown in controlled or semi-controlled environments are still susceptible to nutrient imbalances despite these developments, which can have a substantial impact on plant health, fruit quality and overall productivity. Papaya leaf nutrient deficiencies frequently show up in the early stages of growth and can result in poor fruit development and decreased yield if they are not detected in time. To support early diagnosis and better crop management, the current study focuses on creating an effective method for identifying nutrient deficiencies in papaya leaves using a deep learning (DL) framework based on transfer learning (TL). In this nutrient and micronutrient deficiency study and field work observation during year 2024 to 2026 with different climate and weather conditions done in order to tackle a new but related classification task, in the context of plant health assessment, several well-established architectures including InceptionV3, VGG19, DenseNet and Xception have been widely explored for leaf image analysis. Studies commonly utilize publicly available datasets, such as papaya leaf image repositories hosted on platforms like IEEE DataPort, to fine-tune these models for efficient feature extraction and accurate identification of nutrient and micronutrient deficiency patterns. This body of work demonstrates the growing role of transferring convolutional neural network (CNN) models in advancing automated crop monitoring and decision support systems.
Background: Accurate and timely diagnosis of foliar diseases is the most crucial factor in efforts to maximize crop yield and ensure sustainability. Existing deep learning models, especially single-backbone CNNs, have achieved promising results; however, they often fail to generalize well in different orchard conditions. Methods: In this study, the AFS-PLDCNet framework has been proposed for robust leaf disease classification. This framework uses an attention-based feature-fusion approach to combine deep representations extracted from EfficientNetV2S, MobileNetV2 and ResNet18. A learnable attention mechanism assigns adaptive weights dynamically to each feature and a lightweight meta-learner is used for classification. A new dataset of 9000 apple leaf images was captured in Himachal Pradesh’s orchards, encompassing Alternaria leaf blotch, Marssonina blotch and healthy leaves. Result: The experimental results demonstrate that AFS-PLDCNet achieved superior classification accuracy compared to existing single-backbone CNNs. The proposed model is well-suited for real-time, field-level leaf disease classification and precision agriculture systems.
Background: Plant diseases significantly threaten global food security, reducing potential harvests and sometimes causing total crop failure. Traditional detection methods, which rely on manual inspection and laboratory testing, are time-consuming, costly and prone to human error. Methods: To address these challenges, this study applies transfer learning techniques using deep convolutional neural networks for accurate and efficient plant disease detection. A comparative analysis of nine pretrained models, VGG16, VGG19, ResNet50, ResNet101V2, MobileNetV2, InceptionV3, DenseNet121, InceptionResNetV2 and Xception was conducted on the PlantVillage dataset, focusing on apple, potato and peach leaf images. Result: Results show that DenseNet121 and ResNet101V2 achieved the highest accuracy, particularly for potato leaves with 98.5%, while MobileNetV2 also performed well with up to 99% accuracy for apple and peach leaves. The study demonstrates that transfer learning effectively enhances plant disease classification, enabling faster, more reliable and resource efficient detection for precision agriculture.
Background: Dry rot of potato (Solanum tuberosum L.) is often caused by Fusarium sambucinum and is a major factor accounting for loss of productivity of this economically important food crop. Repeated use of synthetic fungicides has led to worries about pollution risk and emergence of fungicide resistant populations of the pathogen, underscoring the importance of using biological methods as alternatives to chemistry. Both Bacillus subtilis MGM123 and its lipopeptides were tested in the present study for management of potato dry rot disease condition under green house. Methods: A greenhouse experiment was conducted during 2025-2026 using potato variety Kufri Khyati in a completely randomized design with seven treatments comprising untreated control, F. sambucinum, B. subtilis MGM123, Fusarium + Bacillus, Fusarium + Trichoderma, Fusarium + Mancozeb and Fusarium + lipopeptides. Vegetative growth, defence-related enzymes (PAL, POD and PPO), total chlorophyll content, disease severity index (DSI) and tuber yield were recorded. Data were analysed using analysis of variance and treatment means were separated by Tukey’s HSD test at P≤0.05. Result: Significant differences were observed among treatments for vegetative growth, defence-related biochemical responses, disease severity and tuber yield. Bacillus subtilis MGM123 recorded the highest plant height (64.01 cm), shoot length (13.09 cm), root length (14.91 cm) and tuber yield (140.00 g plant-1). Among the pathogen-challenged treatments, lipopeptides effectively reduced disease severity from a Disease Severity Index (DSI) of 4.67 in pathogen-inoculated plants to 1.67 at 28 DAT while increasing tuber yield to 129.00 g plant-1. Biological treatments also enhanced phenylalanine ammonia-lyase (PAL), polyphenol oxidase (PPO) and peroxidase (POD) activities and maintained higher chlorophyll content than the pathogen-inoculated treatment. These findings demonstrate that B. subtilis MGM123 and its lipopeptides are promising environment-friendly alternatives to chemical fungicides for the sustainable management of potato dry rot.
Background: Traditional maize production in subtropical regions is increasingly constrained by the “agronomic trap” of intensive conventional tillage and blanket fertiliser recommendations. These practices have led to diminished nutrient use efficiency (NUE) and the depletion of soil biological health. Integrating precision nutrient management (PNM) with conservation tillage offers a potential pathway to reverse these trends, yet their combined influence on maize development within semi-arid tropical environments requires further investigation. Methods: A field experiment was conducted over the 2023-2025 research period at the MRU and MRECW research stations in Hyderabad (17.55°N, 78.45°E). The study utilised a split-plot design with three replications. Main-plot treatments consisted of three tillage levels: Zero tillage (ZT), reduced tillage (RT) and conventional tillage (CT). Sub-plot treatments comprised four nutrient levels: N1 (100% recommended dose of fertilisers; RDF), N2 (Site-specific nutrient management; SSNM), N3 (75% RDF + organic amendments) and N4 (Precision integrated model). Pooled data were subjected to rigorous homogeneity and normality testing prior to statistical analysis at the P=0.05 level. Result: The synergy of ZT and SSNM significantly optimised crop physio-morphological traits and yield potential. The ZT + SSNM combination achieved a maximum leaf area index (LAI) of 4.82, a plant height of 210.50 cm and a stem girth of 8.65 cm. These growth improvements translated into a 15.6% yield increase over conventional practices. The results indicate that conservation tillage, when paired with precision nutrient delivery, enhances sink-source partitioning and soil microbial biomass, establishing a sustainable high-yield model for maize.
Background: The importance of early identification of tomato bacterial leaf spot (BLS) is essential to minimize the losses in yields and enhance the use of precision agriculture. Methods: The paper demonstrates a lightweight deep learning model, the Dual Multi-Layer Perceptron Feature Fusion Network (DMLPFFN) as a hyperspectral-based approach to the four tomato disease stages, which includes healthy, asymptomatic, early and late infection. The USDA hyperspectral data (168300 bands) has spectral heterogeneity which is solved by per-image Principal Component Analysis (PCA) to normalize the inputs into a single 128-dimensional space. Spectral-spatial patches of 9×9 are overlapping and normalized and then trained upon before training the model. The DMLPFFN combines the feature extraction process that consists of multi-scale dilated convolutions and global contextual modelling with lightweight element-wise fusion. Training strategies that are imbalance-aware are useful in increasing robustness. Result: The model had a weighted F1-score of 0.9718 and a validation accuracy of 98.22% and test accuracy of 97.18%. These findings can be defined as good generalization and lower computational complexity, which makes the framework applicable to real-time agricultural application.
Background: Antimicrobials are agents that kill or prevent the growth of microorganisms like fungi, bacteria, viruses, or parasites. These agents (bioactive compounds) are obtained from plants. Incorporated into foods to extend their shelf life by inhibiting the effectiveness corruption microbe. Methods: Antimicrobials were extracted from date kernel powder by soaking them in cold water, hot water and ethanol alcohol for 24 and 48 h. The effectiveness of antimicrobials against the growth of a number of microbes was evaluated by measuring the inhibition zone diameter (IZD) and minimum inhibitory concentration of antimicrobials (MIC) by. using Well diffusion method. Result: Antimicrobial extracts by ethanol showed high inhibition with an average inhibition zone diameter of 12.35 mm, which is higher than the inhibition zone diameter resulting from antimicrobials extracted by hot water (6.03 mm) and cold (2.74 mm). The minimum inhibitory concentration (MIC) of the produced antimicrobials that was able to inhibit the growth of Klebsiella pneumoniae, Staphylococcus aureus and Fusarium oxysporum was 10 mg/mL and the corresponding inhibition zone diameters were 10.46, 10.03 and 11.00 mm, respectively. The growth of Candida albicans and Aspergillus niger was inhibited at concentrations of 2.5 and 5 mg/mL respectively, with inhibition zones ranging from 6.92 to 11.00 mm.
Background: This study aims to analyze the risks and management strategies of rubber farms and to identify suitable policy options for risk management in the rubber sector of Thailand. Methods: Data were collected through a nationwide field survey using purposive sampling of 1,079 respondents. Additional information was obtained from structured questionnaires, in-depth interviews with 87 key informants and focus group discussions across four regions. The data were analyzed using factor analysis, risk analysis and descriptive statistics. Result: The findings show eight major farm risk factors. These include market and price risk, climate change and natural hazard risk, financial risk, labor availability and quality risk, market access and middleman risk, farmer group and institutional risk, production and land resource risk and farmer skill risk. Among these, five factors were identified as high-risk. They include market and price risk, climate change and natural hazard risk, financial risk, labor availability and quality risk and market access and middleman risk. The remaining risks were classified as moderate. Effective management of both ex-ante and ex post risks requires a focus on reducing, mitigating and coping strategies. Recommended policy options include the establishment of a farm income stability fund, a rubber insurance scheme, improved financial support for rubber farmers, development of central rubber markets, promotion of sustainable rubber plantations and creation of new rubber farms.
Background: Macropropagation is a farmer friendly technology complementing field sucker production. An investigation was conducted to examine the impact of biocontrol-enriched growing media on the corm propagation of red bananas in order to utilize the biopesticide or bionematicide potential in producing disease-free plantlets as well as the plant multiplication potential of soilless substrates. Methods: Polybags containing sawdust, cocopeat, or a 50:50 mixture of sawdust and cocopeat media, enhanced with varying concentrations of Vesicular Arbuscular Mycorrhizal, Bacillus subtilis and Pochonia, were used to plant the decapitated and decorticated suckers. Result: The growing medium consisting of cocopeat + sawdust (1:1) + VAM (30 g/corm) + Pochonia chlamydosporia (60 g/corm) produced the best results in terms of the number of days taken for first bud emergence (24.11 days), secondary bud emergence (46.34 days), tertiary bud emergence (67.35 days), total number of buds/ corm (9.54), plant height (73.56 cm) and girth of the pseudostem (12.34 cm). The plantlets regenerated from the same growing media showed superior performance for survival percentage, number of leaves/plant, plant height, pseudostem girth, number of roots, plant fresh and dry weight, chlorophyll content (SPAD) and total phenol content at 45 days of hardening. The population of Bacillus subtilis was found maximum (2.4×107cfu/g) in the Cocopeat and Sawdust media enriched with Bacillus subtilis (30 g/corm) and Pochonia chlamydosporia (60 g/corm), while the population of Pochonia chlamydosporia (9×107 cfu/g) and higher root colonizaation (100.00%) was found maximum in the growing media Cocopeat + Sawdust (1:1) + VAM (30 g/corm) + Pochonia chlamydosporia (60 g/corm) at 120 days after planting.
Background: Bacteria play a crucial role in converting agricultural waste into compost. Rice straw and corn waste show promise as organic fertilizers. This study aims to compare the effectiveness of different decomposer bacteria in the composting process of straw and corn waste. Methods: Conducted in Bowan, Klaten, from February to April 2024, the study utilized a factorial Completely Randomized Design with eight treatments and four replications. The first factor examined was the type of bacteria, which included: no bacteria; a combination of cellulolytic, rhizomonas, subtilisand cattle rumen bacteria; a mix of cellulolytic, rhizomonasand subtilis; and only cellulolytic bacteria. The second factor was the type of composting media, specifically straw and corn waste. Observations focused on compost pH, weight shrinkage, water holding capacityand water content. An economic analysis was conducted to compare the cost of producing compost with the best decomposer bacteria and the cost of using commercial compost. Result: Results indicated that straw combined with cellulolytic bacteria produced compost that best met the quality standards outlined in SNI 19-7030-2004. The type of compost material influenced compost pH and water content, while the type of bacteria affected all parameters, including pH, weight shrinkage, water holding capacityand water content. Additionally, the interaction between compost materials and bacteria types impacted water holding capacity and water content. The cost of derived straw waste compost input is IDR52,709,000 per hectare for two planting seasons, which is lower than the commercial compost which amounts to IDR72,000,000, making self-produced compost a more economical choice.
Background: Accurate global crop yield predictions are crucial for ensuring food security, effective agricultural planning and climate adaptation. However, existing machine learning and deep learning approaches lack crop-specific feature learning, uncertainty quantification and multiscale spatial contexts, which limits their application in precision agriculture. Methods: This study presents a hybrid ensemble deep learning framework integrated with a crop-aware transformer encoder with heteroscedastic uncertainty for global multi-crop yield prediction (Maize, rice, wheat, soybean) at a 5-arcminute (~9 km) global resolution. The architecture integrates three key innovations: (1) conservative feature engineering using historical yields (4-year sequences with a 3-year temporal gap), geographic coordinates, climate zone indicators alongside temporal trends and stress indicators; (2) crop-aware multi-head attention different mechanisms with crop-specific Query/Key/Value projections enabling differential pattern learning per crop and (3) heteroscedastic output heads predicting both mean yield (μ) and uncertainty (σ) via negative log-likelihood optimization. We implemented rigorous validation using temporal splitting (training: years ≤2013; test: years greater than 2013) and geographic blocking (5-fold GroupKFold with 0.5° spatial blocks) to prevent both spatial and temporal data leakage. Result: Evaluated on the GlobalCropYield5min dataset (1982-2015) across four crops with 60,000 samples in 34 years. The proposed model achieved an overall test R2 of 0.9281, RMSE of 0.585 t/ha and MAE of 0.379 t/ha, with statistical significance confirmed by a paired t-test (p=0.011). Five-fold geographic cross-validation yielded R2 of 0.9337±0.0074 and rice R²=0.9462 (best), maize R²=0.9268, wheat R²=0.9201 and soybean R²=0.8298. Uncertainty quantification achieved excellent calibration (Expected calibration error = 0.024), with empirical coverage matching theoretical values (68% intervals: 69.1% coverage; 95% intervals: 94.8% coverage). Regional analysis showed consistent performance across continents (R²=0.871-0.941), with data-scarce regions showing the expected performance reduction. Ablation studies confirmed that crop-aware attention contributed +3.4% to R2, multiscale spatial features contributed 5.8% and temporal sequence features contributed +3.66%.
Background: Soil organic carbon (SOC) is essential for soil health, food security and climate change mitigation. However, reliable data on SOC distribution and its environmental drivers remain limited in data-scarce dryland regions like Eritrea. This hinders effective soil management and restoration planning. Methods: SOC modelling was conducted across an altitudinal gradient landscape using environmental covariates and machine learning. Three predictor sets (46, 28 and 11 variables) were used selected through three approaches: 1) no selection, 2) removal of highly collinear (r≥0.90) and non-significant variables and 3) boruta algorithm-based selection. The predictive performance of cubist, random forest (RF) and partial least squares (PLS) algorithms were evaluated. Result: SOC levels across the study area were generally low (mean = 0.71%). Rainfed croplands and communal grazing areas showed particularly depleted SOC, attributed to unsustainable land management, while forest and irrigated systems retained significantly higher SOC, indicating greater carbon sequestration potential. The Cubist model with 46 predictors performed best (R² = 0.7465, RPD = 2.0895), whereas PLS with 11 variables had the lowest accuracy (R² = 0.5930, RPD = 1.6491). Temperature emerged as the strongest predictor, followed by land use, altitude, Soil Organic Carbon Index, Landsat 8 band B10 and rainfall. The dominance of temperature for SOC prediction was supported by the strong negative correlation of SOC with temperature (r = -0.582) and positive with altitude (r = 0.580). These underscore the role of climate on the spatial-temporal dynamics of SOC and highlight for climate-smart strategies. Thus, we conclude that cost-effective assessments and monitoring of SOC that support evidence-based strategies for enhancing soil health, land restoration and climate resilience are possible through the developed Cubist and RF models from the Eritrean Central Highlands to the Western Midlands and similar environments.
Background: As corporate bioprospecting has been a prominent strategy for medicinal modernities in India, the traditional herb-based ethnomedicinal practice of the Indigenous communities continues to be a largely marginalized and localized phenomenon. In the form of biopiracy, the Indigenous knowledge of traditional ethnomedicinal practices is dominantly replaced by widely capitalized modern healthcare practices where medicinal plants often meet scientific and technological experiments in a lab, only to be mixed with chemicals, fungus, biologically engineered mechanisms, artificial substances, etc. through a process of insolation. The commercialization of forest-based medicinal plants through modern experiments has not only been ecologically extractive, bio-precarious and therefore ecocidal, it has also combined political ecology of power, marginalization/exploitation of Indigenous ecologies and an overall subjectification of ethnobotanical ecologies. Methods: The research has followed an extensive review of relevant literatures, case studies and statistical data pertaining to ethnomedicinal knowledge framework and traditional healing practices in India. This study was carried out from 2022 to 2026 at Badungduppa Kalakendra, Assam; Ramakrishna Mission Residential College, Narendrapur; Cooch Behar, West Bengal; Thiruvananthapuram, Kerala; and Chiang Mai University, Thailand. Result: The findings of this research reveal a complex structure of the reception, corporatisation and sensitisation of ethnobotanical knowledge and bioprospecting practices in India. Although ethnobotanical healing practices still survive as a prominent mode of cultural and traditional healthcare in India, they face multidimensional challenges and adversities. Although the tribal communities are careful about the conservation of the medicinal plants and herbs, a wider conservational initiative on the part of the government is necessary to promote more sustainable harvesting, gathering, utilising and selling of medicinal plants. This research proposes that there should be community driven corporate promotion of ethnobotanical knowledge and traditional healing practices. This would also potentially check commercially extractive bioprospecting and biopiracy activities in modern healthcare industries in India and the globe.
Background: Chickpea (Cicer arietinum L.) is an important pulse crop in India but suffers severe yield losses due to the pod borer Helicoverpa armigera. Chemical control remains common, yet indiscriminate use affects natural enemies and profitability. Hence, field-based evaluation of eco-compatible insecticidal options under Integrated Pest Management (IPM) is essential for sustainable productivity. Methods: Frontline Demonstrations (FLDs) were conducted for three consecutive rabi seasons (2020–21, 2021–22, and 2022–23) on farmers’ fields in Ananthapuramu district, Andhra Pradesh. The IPM module consisted of Spinosad 45 SC (0.3 mL L⁻¹) and Profenophos 40% + Cypermethrin 4% EC (2 mL L⁻¹), evaluated against an untreated control across 10 farmer-participatory plots. Larval populations of H. armigera and natural enemies were recorded before and after spraying, and the data were analysed using Randomized Block Design (RBD) with Duncan’s Multiple Range Test (DMRT). Economic indicators, adoption and yield gap indices, and phytotoxicity effects were assessed following standard protocols. Results: The trials revealed that Spinosad 45 SC (0.3 mL L⁻¹) was significantly more effective than Profenophos 40% + Cypermethrin 4% EC (2 mL L⁻¹) in suppressing H. armigera populations, recording 70.84%–84.03% reduction compared to 19.00%–21.88% under the latter treatment. Both insecticides were found to be safe for the key natural predators Cheilomenes sexmaculata and Coccinella septempunctata. Demonstration plots under IPM recorded higher grain yields (23.75–24.23 q ha⁻¹) and net returns (₹79,026–₹1,05,646 ha⁻¹) compared to farmer practices (19.95–21.18 q ha⁻¹; ₹52,033–₹70,691 ha⁻¹). The benefit–cost ratio also improved markedly from 0.89–1.07 under traditional practices to 1.74–1.75 under IPM. These results clearly demonstrate the superiority of Spinosad-based IPM modules in enhancing productivity, profitability, and ecological safety in Bengal gram cultivation.
Background: Host plant resistance is a durable, permanent solution for effective disease management in cotton. It reduces agrochemical use, is eco-friendly and is durable. Field evaluation is a practical method to identify cotton genotypes with natural resistance to diseases, including HxB hybrids with higher yields. Methods: Parental Bt G. hirsutum, G. barbadense and HxB cotton hybrids were screened under field conditions for Alternaria leaf spot (ALS), Tobacco Streak Virus (TSV), grey mildew, external boll rot and rust diseases. Pathological data were recorded at monthly intervals and PDI was calculated. Yield levels were compared with disease-tolerant H´B hybrids for selection. Result: Of sixty-six G. barbadense male parents, 19 entries, namely B1, B3, B8, B12, CCB26, ICB53, ICB58, ICB40(B), ICB183(B), CCB141, CB99, ICB284, CCB143(B), CCB25(B), CCB29(B), CCB11A(B), SM14, ICB86 and CCB64, were free from all diseases. Another 14 entries were disease-free, except for ALS. Five entries were free from all diseases except rust. Five entries were susceptible to most diseases. All four female G. hirsutum parents were infected with ALS and rust. Among 18 BG II G. hirsutum parents, V3 was free of all diseases. Ten entries were free from TSV and grey mildew but infected with ALS and rust. Seven entries were infected with ALS, TSV and rust. Among 50 HxB hybrids of BG I, six crosses, namely GJHV 374 Bt x ICB 28, GJHV 374 Bt x ICB 174, GJHV 374 Bt x ICB 194, Rajat Bt x CCB 141, Rajat Bt x ICB 75 and PKV 081 Bt x ICB 258, were free from all diseases. Four crosses were free of diseases except rust. ALS, TSV and rust were recorded in 14 crosses. Of the 14 BG entries, one entry, 211-437, was found free of all diseases. The entries 211-445 and GJHV 374 Bt were found disease-free, except for rust. Four entries, including 211-431, PKV 081 Bt, Rajat Bt and Suraj Bt, were infected with ALS, TSV and rust. All seven commercial BG II hybrids were infected with ALS and rust. Hybrids Ankur Anish, RCH659 and US7067 were also infected with TSV. The six HxB-F1 BG I cotton hybrids that remained free of all the diseases studied and recorded higher seed cotton yields with improved fibre parameters. Hybrids, such as GJHV 374 Bt x ICB 264 and Rajat Bt x CCB 141, were free from ALS and TSV but showed rust incidence, yet maintained reasonably good yields and fibre quality.
