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Aug 2026 DOI 10.14302/issn.2998-1506.jpa-26-6394
Jana SnehasisCorresponding author
Background Enhancing crop productivity while minimizing chemical inputs remains a core objective of sustainable agriculture. This study investigated the efficacy of spiritual blessing energy treatments as a non-invasive biostimulant to optimize the growth dynamics and crop yield of radish (Raphanus sativus L.). Methods Seeds and land were exposed to Spiritual Blessing Energy Treatment (SBET) in her physical presence for about 4 minutes once, alongside untreated controls under identical environmental conditions. Morphological parameters and final biomass production were systematically evaluated. Results Photosynthesis traits such as number of leaves per plant, leaf width, and fresh weight of leaves per plant were significantly increased by 28.41% (p = 0.003), 45.81% (p ≤ 0.001), and 28.86% (p ≤ 0.001), respectively, in the blessing/biofield energy-treated radish group (BTRSG) compared to the control radish group (CONRSG). Moreover, root length, root width/girth, and root weight per plant were significantly increased by 39.68% (p ≤ 0.001), 32.13% (p ≤ 0.001), and 30.73% (p ≤ 0.001), respectively, in the BTRSG compared to the CONRSG. Besides, root yield (tons per hectare) was increased by 46.29% in BTRSG compared to the CONRSG. Conclusion These findings suggest that the spiritual blessing energy treatments (Trivedi Effect®) act as a powerful biostimulatory agent capable of optimizing plant ontogeny and radish yield and offering a sustainable paradigm for precision agronomy.
May 2026 DOI 10.14302/issn.3070-2232.jf-26-6197
Jana SnehasisCorresponding author
Background The increasing demand for sustainable and eco-friendly agricultural practices has led to the exploration of non-traditional methods to enhance crop yield and resilience. Spiritual Blessings (Biofield) Energy Treatment (SBET), a form of consciousness-driven energy healing, is increasingly being investigated for its potential to modulate biological systems at the cellular and molecular levels without use of chemical additives Objective This study aimed to evaluate the impact of SBET on the growth characteristics and overall productivity of summer squash (Cucurbita pepo L.). Methods The study was conducted using a controlled experimental design, where seeds and plots were divided into two groups: control and treated. The treated group received a remote SBET by a recognized practitioner, while the control group remained untreated. Both groups were maintained under identical environmental conditions (soil, water). Parameters such as germination rate, plant height, leaf area index, and total fruit yield were monitored over a full growth cycle. Results Results showed that plant height, number of branches, and total number of leaves per plant were significantly improved by 35.14% (p ≤ 0.001), 41.64% (p = 0.011), and 49.01% (p = 0.029), respectively, in the treatment group compared to the control group. Additionally, fruit length and total fruit yield (tons per hectare) were significantly increased by 39.68% (p = 0.002) and 15.92%, respectively, in the treatment group compared to the control group. Conclusion Exposure of SBET significantly improved both vegetative and reproductive development, yielding substantial increases in plant height, branching, and leaf production.
Mar 2025 DOI 10.14302/issn.2638-4469.japb-25-5395
Divakar ChennamsettiCorresponding author
Plant leaf diseases pose significant threats to crop yield and agricultural sustainability, making early and accurate detection crucial for effective disease management. In current years, deep neural network (DNN) techniques have shown remarkable potential in the field of image classification, including plant disease detection. The study aims to investigate the performance of two popular deep learning architectures, namely, VGG16 and InceptionResNetV2, for the detection of tomato plant leaf disease. The proposed methodology involves acquiring a diverse dataset comprising high-resolution images of healthy and diseased leaves from the target crops. Preprocessing techniques such as image augmentation and normalization are applied to enhance the generalization ability of the models and mitigate overfitting. Transfer learning is employed to initialize the deep learning architectures with weights pre-trained on large-scale image datasets to accelerate convergence and improve the models' performance in limited data scenarios. To evaluate performance of proposed networks various metrics such as validation and test accuracies, precision and recall, F1 score, and the area under the curve (AUC) are considered. From the investigations, the classification accuracy of the finest architectures is as follows: 99.8 percent for VGG16 and 99.4 percent for InceptionResNetV2 on Corn Leaves. The results suggest that the models developed during the investigation phase to identify the leaf disease were superior to any existing Deep Neural Networks (DNNs).
Jun 2019 DOI 10.14302/issn.2639-3166.jar-19-2590
Gupta RajCorresponding author
Centre for Advancement of Sustainable Agriculture, National Agriculture Science Centre Complex, Todapur Road, New Delhi, 110012, India
Over last few decades, acreage of total fallow lands (Kharif and Rabi seasons) in India has remained almost unchanged around 25Mha. The acreage of Kharif (summer) and Rabi (winter) Fallows in Madhya Pradesh (MP) are 1.98Mha and 5.51Mha, respectively. In the semi-arid agroclimatic zones of the states, Fallow-Wheat/Gram/Indian-Mustard cropping systems are practiced. After harvest of Kharif rice, kodo-kutki, maize or sorghum, farmers generally practice post-rainy season Rabi fallows in the sub-humid regions, south of Narmada River. Kharif fallowing is largely the result of the inability of the farmers to make planting dates independent of monsoon forecasts, and make efficient use of rain water. It appears that factors responsible for Kharif and Rabi fallows are distinctly different and a general consequence of distinctly different soil moisture regimes prevailing in the two crop seasons. Kharif and Rabi fallows have two distinct resource management domains. Whereas, Kharif fallows can be tackled with “PMP-dry seeding” agronomy, production constraints of Rabi fallows can be substantively tackled by shifting from tilled to zero-till agriculture with residue management to make efficient use of the conserved rain water. Some irrigation support will prove useful to tackle mid-season droughts in both situations. Conservation agricultural practices can significantly improve and stabilize crop yields in black soils and other associated soils of in the semi-arid tropics region of the Central India.