Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables.

Machine learning (ML) can make use of agricultural data related to crop yield under varying soil nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production. The aim of this study was to evaluate the efficacy of five distinct ML models for a dataset sourced from the Kaggle repository to generate practical recommendations for crop selection or determination of required nutrient(s) in a given site. The datasets contain information on NPK, soil pH, and three climatic variables: temperature, rainfall, and humidity. The models namely Support vector machine, XGBoost, Random forest, KNN, and Decision Tree were trained using yields of individual data sets of 11 agricultural and 10 horticultural crops, as well as combined yield of both agri-horticultural crops. The results strongly suggest to evaluate individual data sets separately for each crop category rather than using combined the data sets of both categories for better predictions. Comparing the five ML models, the XGBoost demonstrated the highest level of accuracy. The precision rates of XGBoost for recommending agricultural crops, horticultural crops, and a combination of both were 99.09 % (AUC 1.0), 99.3 % (AUC 1.0), and 98.51 % (AUC 0.99), respectively. This non-intrusive method for generating crop recommendations in diverse environmental conditions holds the potential to provide valuable insights for the development of a user-friendly AI cloud-based interface. Such an interface would enable rapid decision-making for optimal fertilizer applications and the selection of suitable crops for cultivation at specific sites.

Assessing deep convolutional neural network models and their comparative performance for automated medicinal plant identification from leaf images.

Medicinal plants have got notable attention in recent years in the field of pharmaceutical and drug research. The high demand of herbal medicine in the rural areas of developing countries and drug industries necessitates correct identification of the medicinal plant species which is challenging in absence of expert taxonomic knowledge. Against this backdrop, we attempted to assess the performance of seven advanced deep learning algorithms in the automated identification of the plants from their leaf images and to suggest the best model from a comparative study of the models. We meticulously trained VGG16, VGG19, DenseNet201, ResNet50V2, Xception, InceptionResNetV2, and InceptionV3 deep neural network models. This training utilized a dataset comprising 5878 images encompassing 30 medicinal species distributed among 20 families. Our approach involved two avenues: the utilization of public data (PI) and a blend of public and field data (PFI), the latter featuring intricate backgrounds. Our study elucidates the robustness of these models in accurately identifying and classifying both interfamily and interspecies variations. Despite variations in accuracy across diverse families and species, the models demonstrated adeptness in these classifications. Comparing the models, we unearthed a crucial insight: the Normalized leverage factor (γω) for DenseNet201 stands at 0.19, elevating it to the pinnacle position for PI with a remarkable 99.64 % accuracy and 98.31 % precision. In the PFI scenario, the same model achieves a γω of 0.15 with a commendable 97 % accuracy. These findings serve as a guiding beacon for shaping future application tools designed to automate medicinal plant identification at the user level.

Salinity hazard drives the alteration of occupation, land use and ecosystem service in the coastal areas: Evidence from the south-western coastal region of Bangladesh.

Understanding the salinity effects on the rural livelihood and ecosystems services are essential for policy implications and mitigations. Salinity-driven modulation in land use and land cover, community traditional occupations, and ecosystem service have been elucidated in the present investigation. The study was carried out in the south-western region of Bangladesh as a representative case using focus group discussions, questionnaire survey, and remote sensing techniques. The findings showed that salinity-induced land use changes seriously threatened ecosystem services, employment and livelihoods. Shrimp farming was found to have replaced the majority of agricultural and bare lands, which led to the poor locals losing their land. The increasing land transformation to shrimp ponds as a coping strategy with salinity was not reported to be a viable option as maximum marginal poor people were unable to run the capital-intensive shrimp aquaculture. Eventually, many rich people occupied the cropland for shrimp farming which forced the traditional farmers and fishermen to leave their job and sell their labor. Many of the traditional services derived from the ecosystems were drastically reduced or got lost. The ultimate effect on the traditional livelihoods of the communities increased vulnerability and reduced resilience. The findings could aid in formulating realistic policies and action for ensuring the future resilience of the community through an appropriate adaptation strategy, such as introducing salinity-tolerant crops and integrated farming to safeguard the interest of the poor farmers. Despite the geographical locality of the study, its implications are global given the identical salinity concerns in other emerging nations' coastal regions.

First Report of Rhizopus stolonifer Causing Premature Soft Rot of Jackfruit in Bangladesh.

The jackfruit tree (Artocarpus heterophyllus) is native to South and South-east Asia including Bangladesh. It is a commercially important tropical tree species that produces fruit, food, fodder, and high-quality wood (Gupta et al. 2022). During surveys in February 2022, soft rot on immature fruit at approximately 70% incidence was observed in several plantations and homesteads in the Sylhet district of Bangladesh. Infected fruit had black patches surrounded by wide bands of white, powdery masses. The patches enlarged with fruit maturation, and in some cases, covered the entire fruit. Symptomatic fruit were collected, surface sterilized with 70% ethanol for 1 min, and washed 3 times with sterile distilled water. Fen air-dried, and small pieces from the margins of lesions were transferred to potato dextrose agar (PDA). The plates were incubated at 25°C in the dark. Two-day-old colonies had diffuse, gray cottony mycelia that were hyaline and aseptate under the microscope. Sporangiophores measuring 0.6-2.5mm in length and 18 to 23µm in diameter had rhizoids and stolons at their bases. Sporangia were almost spherical and were 125µm (±65µm, n=50) in diameter. Sporangiospores were ellipsoid to ovoid and measured 3.5 to 9.32µm × 2.82 to 5.86µm (x̄= 5.86×4.1µm, n=50). Based on these morphological features, the isolates were identified preliminarily as Rhizopus stolonifer (García-Estrada et al. 2019; Lin et al. 2017). To identify the pathogen molecularly, genomic DNA was extracted using the FavorPrep Fungi/Yeast Genomic DNA extraction Mini Kit (Taiwan). Polymerase chain reaction (PCR) amplification of ITS1-5.8S-ITS2 rDNA was done using primers ITS4 and ITS5 (White et al. 1990) following the procedure of Khan and Bhadauria (2019). The PCR product was then sequenced by Macrogen, Korea. A BLAST search in GenBank revealed that isolate JR02 (GenBank accession OP692731) was 100% identical to R. stolonifer (GenBank accession MT256940). In pathogenicity tests,10 healthy young fruit at a similar maturity stage as the ones found diseased were collected from a orchard where the disease was not observed. Fruit were surface sterilized with 70% ethylalcohol and washed with sterile distilled water. Wounded (using a sterilized needle) and non-wounded fruits were inoculated with 20µl of a spore suspension (1×106/ml). Sterile distilled water was used for the controls. Inoculated fruit were covered with sterile cloth, transferred to perforated plastic bags with moistened blotting paper, and incubated at 25°C in the dark. Symptoms were first observed after 2 days on wounded fruit, but no symptoms developed on controls and non-wounded fruit. Rhizopus stolonifer was re-isolated from infected fruit, thus fulfilling Koch's postulates. Rhizopus rot is a devastating disease causing premature fruit drop, reduced crop yield, and post-harvest rot of jackfruit and other fruits and vegetables (Sabtu et al. 2019). Three Rhizopus species namely R. stolonifer, R. artocarpi and R. oryzae have been reported causing fruit rot of jackfruit in the tropics including Mexico, India and Hawaii (García-Estrada et al., 2019; Babu et al., 2018; Nelson, 2005). Appropriate management strategies are needed to be developed to prevent premature rot of jackfruit. To our knowledge, this is the first report of R. stolonifer causing premature soft rot of jackfruit in Bangladesh.

Assessment of remote sensing-based indices for drought monitoring in the north-western region of Bangladesh.

Drought is a widespread hazard that can tremendously affect the biodiversity, habitat of wild species, and ecosystem functioning and stability, especially in the dry region. Due to its geographic location, the north-western region of Bangladesh has a comparatively arid climate which is very much susceptible to drought occurrence and is marked as a red zone. Despite the growing evidence of the impact of drought on food security and ecosystem functioning, little effort has been paid to mitigate the drought in this region. The present study aimed to assess the drought condition of the north-western region of Bangladesh using earth observation techniques. For this purpose, Landsat data from 1990 to 2020 was used to determine various vegetation indices such as Normalized Difference Vegetation Index (NDVI), Water Index (NDWI), Moisture Index (NDMI) and Soil Adjusted Vegetation Index (SAVI), along with Land Surface Temperature (LST). Results show that the depletion of forests (2832 km2) and water bodies (6773 km2) resulted from the expansion of settlement (6563 km2) and agricultural land (1802 km2) for the period 1990-2020. Examination of the temporal changes of vegetation indices and LST showed that the values of all indices decreased while the LST increased. The negative correlation between NDVI value and LST indicates that the vegetation in our study was subject to drought-induced shocks. This study reveals the current situation of the vegetation health in the north-western region of Bangladesh in relation to the drought conditions. The findings of this study have practical implications for the policymakers in implementing necessary measures for agriculture, forests, water development, and economic zone planning.

'Le Rouge et le Noir': a decline in flavone formation correlates with the rare color of black dahlia (Dahlia variabilis hort.) flowers.

BACKGROUND: More than 20,000 cultivars of garden dahlia (Dahlia variabilis hort.) are available showing flower colour from white, yellow and orange to every imaginable hue of red and purple tones. Thereof, only a handful of cultivars are so-called black dahlias showing distinct black-red tints. Flower colour in dahlia is a result of the accumulation of red anthocyanins, yellow anthochlors (6'-deoxychalcones and 4-deoxyaurones) and colourless flavones and flavonols, which act as copigments. White and yellow coloration occurs only if the pathway leading to anthocyanins is incomplete. Not in all cultivars the same step of the anthocyanin pathway is affected, but the lack of dihydroflavonol 4-reductase activity is frequently observed and this seems to be based on the suppression of the transcription factor DvIVS. The hitherto unknown molecular background for black colour in dahlia is here presented. RESULTS: Black cultivars accumulate high amounts of anthocyanins, but show drastically reduced flavone contents. High activities were observed for all enzymes from the anthocyanin pathway whereas FNS II activity could not be detected or only to a low extent in 13 of 14 cultivars. cDNA clones and genomic clones of FNS II were isolated. Independently from the colour type, heterologous expression of the cDNA clones resulted in functionally active enzymes. FNS II possesses one intron of varying length. Quantitative Real-time PCR showed that FNS II expression in black cultivars is low compared to other cultivars. No differences between black and red cultivars were observed in the expression of transcription factors IVS and possible regulatory genes WDR1, WDR2, MYB1, MYB2, 3RMYB and DEL or the structural genes of the flavonoid pathway. Despite the suppression of FHT expression, flavanone 3-hydroxylase (FHT, synonym F3H) enzyme activity was clearly present in the yellow and white cultivars. CONCLUSIONS: An increased accumulation of anthocyanins establishes the black flowering phenotypes. In the majority of black cultivars this is due to decreased flavone accumulation and thus a lack of competition for flavanones as the common precursors of flavone formation and the anthocyanin pathway. The low FNS II activity is reflected by decreased FNS II expression.