Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R² = 0.963 and RMSE = 0.334) was higher than those of the PCA (R² = 0.934 and RMSE = 0.555) and the regression models based on CIs (R² = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R² = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.

Background: The in-depth traditional knowledge of medicinal plants is at risk of extinction due to the dependency on oral transmission, and as such, there is an urgent need to document such knowledge. This study aimed to document indigenous uses of medicinal plants among community members in the Ejisu-Juaben Municipality. Methods: Data was collected in 2016 from community members and local herbalists in the Ejisu-Juaben Municipality through a semi-structured questionnaire. Statistical tools and ethnobotanical indices, i.e., informant consensus factor (ICF), fidelity level (FL), and use value (UV) were used to analyse the data. Results: One hundred and six medicinal plants belonging to 45 families were reported to cure 68 different human diseases. The most frequently used plant part in this study was the leaves (52%). Decoction (57.5%) and oral administration (58.3%) were the most utilised herbal preparation and administration route respectively. Cleistopholis patens had the highest UV (0.54) with pain & fevers and skin diseases having the highest ICF values (0.88 and 0.85 respectively). Furthermore, new medicinal uses of Hilleria latifolia and ten other species were recorded for the treatment of the traditional local disease, aseram. Conclusions: The current knowledge and uses of medicinal plants are still high in the study area based on the high degree of consensus among informants. This study could allow for the preservation of knowledge and biodiversity of medicinal plants, both of which are threatened with extinction.

Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world's food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t·ha^-1 across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel^-1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting.

Weeds are rapidly developing resistance to synthetic herbicides, and this can pose a threat to the ecosystem. Exploring allelopathic species as an alternative weed control measure can help minimize the ecological threat posed by herbicide-resistant weeds. In this study, we aimed to evaluate the contribution of some polyphenols to the allelopathy of rosemary (Rosmarinus officinalis L.). The phytotoxic effects of rosemary (leaves, roots, inflorescences, and stems) crude extracts were tested on lettuce (Lactuca sativa L.). Soils incorporated with dried rosemary leaves were also tested on test plants. Reversed-phase high-performance liquid chromatography (HPLC) analysis was used to determine the content of some polyphenols (caffeic, ferulic, gallic, rosmarinic, carnosic, and chlorogenic acids) in rosemary. The specific activity and total activity of crude extracts and individual compounds were evaluated using lettuce. The crude extract of rosemary leaves showed the highest growth inhibitory effect among the rosemary organs tested. Soil amended with rosemary leaf debris reduced the dry matter and seed emergence of lettuce. Carnosic acid was the main compound detected in rosemary leaves and had a high specific activity when tested on lettuce. During the seed germination period, there was observed filter paper coloration among the test plants treated with carnosic acid (250 μg/mL). The high concentration and strong inhibitory effect of carnosic acid could explain the inhibitory activity of the rosemary leaf extract. Hence, we conclude based on the total activity estimation that carnosic acid among the other tested compounds is the major allelochemical in rosemary leaves.

Cropping calendar optimization contributes to an increase in rice yield. Information on the seasonal variation in grain yield and climate conditions is necessary to determine an appropriate cropping calendar. We sought to find the optimal cropping calendar in Mwea, Kenya, in a tropical highland in equatorial East Africa. We conducted a series of 58 experiments using a local popular rice variety, Basmati 370, between 2013 and 2016, using a secured water supply and adequate blast control, sowing every 15 days. The grain yield was 0–2 t ha⁻¹ when the variety was sown between March and June. This poor grain yield was attributable to the low temperature and low solar radiation from May to September. In contrast, the grain yield was always more than 3 t ha⁻¹ when the variety was sown between July and February. Sowing Basmati 370 between March and June is not recommended, because it may lead to a suboptimal yield due to cold stress. The current cropping calendar (July–December or August–January) is acceptable even under abundant year-round water supply, but sowing between October and February is a good alternative sowing period for single rice cropping. Rice production per year is expected to increase to >100% with the introduction of double cropping by adding cultivation from between January and February before the current cropping calendar. These findings serve as useful references for considering and determining the appropriate calendar options for single and double cropping of rice in tropical highlands in equatorial East Africa. Graphical Abstract