Over time we continue to improve our solution so the newer publications may contain better results. Notably our software processing was greatly improved in Q4 2022, and our spectrometer light sensor was introduced in Q4 2023. The use of our calibration targets and ambient light sensor are required for the most accurate results.
"The vegetation indices obtained from drone imagery were used to create spatial maps of plant growth. These maps can be used for accurately identifying stressed or infected plant areas, targeting fertilizer, pesticide, and irrigation applications precisely where needed, reducing waste,and increasing efficiency."
"The UAV and Mapir camera have potentially been utilized to estimate the N, P, K, and Mg content of the leaves and to monitor the nutritional status of the leaves after fertilizing. By generating data from the field quickly and precisely, crop production inputs can be managed in an environmentally friendly way, which is fundamental to sustainability in oil palm plantations."
"The manual data collection process is very expensive, both in terms of time and money.The multispectral image approach to facilitate manual collection of crop yield data is one of the contributions of this research."
"Os resultados obtidos no presente estudo são promissorese apresentam uma nova forma de avaliação da viticultura nosemiárido, com uso de imagens centimétricas e de técnicascomputacionais no processamento e obtenção de informaçõesuteis para a gestão da produção de frutíferas."
"The results obtained in this study are promising and present a new way of evaluating viticulture in the semi-arid region, using centimetric images and computational techniques in the processing and obtaining of useful information for the management of fruit production."
"With a higher accuracy to analyze the agronomic parameters, the use of multispectral images for classification and monitoring of the crop constitutes a low-cost option for large-scale, reliable, and constant monitoring of the crop, reaching up to 86.66% of global accuracy in the RGN data classification and vegetation indices using the Randon Forest algorithm."
"All the results of the analysis of NDVI, GNDVI, and SAVI on healthy oil palm plants, consistently have high values and decrease in line with the level of infection..."
"This study demonstrates the potential of using multispectral images to monitor early-stage infection of coffee plants by H. vastatrix, the most important pathogen of coffee plants worldwide, using an unmanned aerial vehicle (UAV). The early detection of this pathogen in the field with low-cost technology can be an important tool for the monitoring of coffee leaf rust and, consequently, a more sustainable management of this pathogen, making applications of chemical fungicides only when necessary."