Multispectral Image Analysis for the Detection of Diseases in Coffee Production

Coffee is produced in Latin America, Africa and Asia, and is one of the most traded agricultural products in international markets. The coffee agribusiness has been diversified all over the world and constitutes an important source of employment, income and foreign exchange in many producing countries. In recent years, its global supply has been affected by adverse weather factors and pests such as rust, which has been reflected in a highly volatile international market for this product [1]. This paper shows a method for the detection of coffee crops and the presence of pests and diseases in the production of these crops, using multispectral images from the Landsat 8 satellite.

[1]  Amelec Viloria,et al.  Fuzzy Logic Applied to the Performance Evaluation. Honduran Coffee Sector Case , 2018, ICSI.

[2]  Y. Takahashi,et al.  Discrimination of areas infected with coffee leaf rust using a vegetation index , 2018, Remote Sensing Letters.

[3]  Anne-Katrin Mahlein,et al.  Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases , 2012, Plant Methods.

[4]  Onisimo Mutanga,et al.  Mapping the spatial distribution of the yellowwood tree (Podocarpus henkelii) in the Weza-Ngele forest using the newly launched Sentinel-2 multispectral imager data , 2020 .

[5]  Z. Niu,et al.  Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging , 2007, Precision Agriculture.

[6]  José Blasco,et al.  VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits , 2016 .

[7]  Marcelo de Carvalho Alves,et al.  Multispectral radiometric characterization of coffee rust epidemic in different irrigation management systems , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[8]  B. Sierra,et al.  A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks, Remote Sensing, and Deep Learning: Case Study of the Caturra Variety in Colombia , 2020 .

[9]  David A. Landgrebe,et al.  Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..

[10]  Marcelo de Carvalho Alves,et al.  Multispectral radiometric monitoring of bacterial blight of coffee , 2018, Precision Agriculture.

[11]  O. Mutanga,et al.  Machine learning prediction of coffee rust severity on leaves using spectroradiometer data , 2018, Tropical Plant Pathology.

[12]  Uwe Rascher,et al.  Observation of plant-pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. , 2016, Functional plant biology : FPB.

[13]  Marcelo de Carvalho Alves,et al.  Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[14]  Elaine Ribeiro de Faria,et al.  Segmenting and Detecting Nematode in Coffee Crops Using Aerial Images , 2019, ICVS.

[15]  Onisimo Mutanga,et al.  Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions , 2017, Precision Agriculture.

[16]  Viloria Amelec,et al.  Increased Efficiency in a Company of Development of Technological Solutions in the Areas Commercial and of Consultancy , 2015 .

[17]  Amelec Viloria.  Commercial Strategies Providers Pharmaceutical Chains for Logistics Cost Reduction , 2016 .