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A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm.

Ali Mirzazadeh Afshin Azizi Yousef Abbaspour_Gilandeh José Luis Hernández-Hernández Mario Hernández Hernández Iván Gallardo Bernal (2021, [Artículo])

Estimation of crop damage plays a vital role in the management of fields in the agricultura sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds¿ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of Deep learning-based models to classify other damaged crops.

rapeseed classification damaged crops deep neural networks INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS

Spatiotemporal analysis of rainfall and temperature variability and trends for climate resilient maize farming system in major agroecology zones of northwest Ethiopia

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

Spatiotemporal studies of the annual and seasonal climate variability and trend on an agroecological spatial scale for establishing a climate-resilient maize farming system have not yet been conducted in Ethiopia. The study was carried out in three major agroecological zones in northwest Ethiopia using climate data from 1987 to 2018. The coefficient of variation (CV), precipitation concertation index (PCI), and rainfall anomaly index (RAI) were used to analyze the variability of rainfall. The Mann-Kendall test and Sen’s slope estimator were also applied to estimate trends and slopes of changes in rainfall and temperature. High-significance warming trends in the maximum and minimum temperatures were shown in the highland and lowland agroecology zones, respectively. Rainfall has also demonstrated a maximum declining trend throughout the keremt season in the highland agroecology zone. However, rainfall distribution has become more unpredictable in the Bega and Belg seasons. Climate-resilient maize agronomic activities have been determined by analyzing the onset and cessation dates and the length of the growth period (LGP). The rainy season begins between May 8 and June 3 and finishes between October 26 and November 16. The length of the growth period (LGP) during the rainy season ranges from 94 to 229 days.

Climate Trends Spatiotemporal Analysis Agroecology Zone CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGROECOLOGY CLIMATE CLIMATE VARIABILITY MAIZE