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1. A Comparative Estimation of Maize Leaf Moisture Content on Smallholder Farming Systems Using Unmanned Aerial Vehicle (UAV) Based Proximal Remote SensingUnderstanding maize moisture conditions is necessary for crop monitoring and developing early warning systems to optimise agricultural production in smallholder farms. Therefore, this study evaluated the utility of UAV derived multispectral imagery and machine learning techniques in estimating maize leaf moisture indicators; equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables ... S. Ndlovu |
2. Assessment of Nitrogen and Phosphorus Content (NP) in Citrus Trees Using UAV-imagery Derived Vegetation Indices and Machine Learning AlgorithmsMonitoring nutrient status of citrus trees is fundamental to ensure optimum fruit yield and quality. However, this task is traditionally time-consuming and laborious. Unmanned Aerial Vehicles (UAVs), with their high temporal and spatial resolution imagery, are demonstrating a great potential to substitute traditional methods in assessing nutrient status of several crops, including citrus. In this study, we evaluated the performance of vegetation indices (VIs) derived from UAV multispectral im... Z. Abail, H. Benaouda, M. Chikhaoui, H. Benyahia, O. Iben halima, M. Baraka, A. Douaik, H. Iaaich, A. Zouahri, F. Omari |
3. Chlorophyll Meter Based Precision Nitrogen Management in Maize Grown in Alluvial Soil in EgyptPrecision nitrogen (N) management is essential for profitable crop production and to minimize N losses to the environment that are a consequence of an excessive N supply. Chlorophyll meter-based N management can help to achieve high N use efficiency, as aquick and non-destructive spectral characteristics of leaves, which can be used to diagnose plant N deficiency and graduate N fertilization with improve N use efficiency. Field experiments were conducted during two consecutive years, 2019 and... A. Soaud, S. Abou-zeid , A. Ali, D. Hassan |
4. Drivers of Post-harvest Aflatoxin Contamination: Evidence Gathered from Knowledge Disparities and Field Surveys of Maize Farmers in the Rift-valley Region of KenyaMaize-dependent populations in sub-Saharan Africa are continually exposed to aflatoxin poisoning owing to their regular consumption of this dietetic cereal. Being a staple in Kenyan households, consumption of maize-based meals is done almost daily, thereby exposing consumers to aflatoxicoses. This study assessed awareness levels, knowledge disparities and perceptions regarding aflatoxin contamination at the post-harvest phase among farmers in the Rift-valley region of Kenya. Households were r... G.W. Gachara, R. Lahlali, R. Suleiman, B.M. Kilima |
5. From Drone to Satellite – Does It Work?Multispectral drone-sensors are useful for detailed studies of crop characteristics in field trials, e.g. to create prediction models on nitrogen (N) uptake, or even estimates of optimal N rate to apply. To enable wide application of such models, they may be applied in satellite image-based decision support systems for farmers. However, successful transfer of models based on spectral data from one platform to another, requires strong and stable correlation between data from the different sens... M. Söderström, K. Persson |
6. Maximisation De L’efficience D’utilisation Des Nutriments : Recommandation De Fertilisation à La Carte Pour Le Maïs Sur Les Ferralsols Du Sud-togoL'amélioration de la nutrition des plantes à travers l'agriculture de précision devient incontournable pour l'optimisation de l'entreprise agricole et la protection de l'environnement. Nous avons conduit pendant la grande saison culturelle de 2019 et 2020, sous culture de maïs (Zea mays L.), des essais soustractifs à base de l'azote (N), du phosphore (P) et du potassium (K) à la station d'expérimentations agronomiques ... J. Sogbedji, L. William, M. Lare, A. Sekaya, K. Sika , E. Tagba |
7. Potato Yield Prediction Using Multi-temporal Sentinel-2 Data and Multiple Linear RegressionTraditional potato growth models have a number of flaws, i.e., the cost of data collection, quality of input data, and the absence of spatial information in some cases. To address these challenges, we created a multiple linear regression model (MLRM) that uses the multi-temporal Sentinel-2 derived indices to predict potato yield. Along the growing season (from October 2019 to February 2020) eight Sentinel-2 imageries were collected, afterwards, the normalized difference vegetation index ... M.E. Amin, M.A. Abdelfattah, E.S. Mohamed, A.A. Belal, M. Nabil, A.G. Mahmoud |