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51 resultados, página 3 de 6

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

Mulch application as the overarching factor explaining increase in soil organic carbon stocks under conservation agriculture in two 8-year-old experiments in Zimbabwe

Regis Chikowo Christian Thierfelder Marc Corbeels (2024, [Artículo])

Conservation agriculture (CA), combining reduced or no tillage, permanent soil cover, and improved rotations, is often promoted as a climate-smart practice. However, our understanding of the impact of CA and its respective three principles on top- and subsoil organic carbon stocks in the low-input cropping systems of sub-Saharan Africa is rather limited. This study was conducted at two long-term experimental sites established in Zimbabwe in 2013. The soil types were abruptic Lixisols at Domboshava Training Centre (DTC) and xanthic Ferralsol at the University of Zimbabwe farm (UZF). The following six treatments, which were replicated four times, were investigated: conventional tillage (CT), conventional tillage with rotation (CTR), no tillage (NT), no tillage with mulch (NTM), no tillage with rotation (NTR), and no tillage with mulch and rotation (NTMR). Maize (Zea mays L.) was the main crop, and treatments with rotation included cowpea (Vigna unguiculata L. Walp.). The soil organic carbon (SOC) concentration and soil bulk density were determined for samples taken from depths of 0–5, 5–10, 10–15, 15–20, 20–30, 30–40, 40–50, 50–75 and 75–100 cm. Cumulative organic inputs to the soil were also estimated for all treatments. SOC stocks at equivalent soil mass were significantly (p<0.05) higher in the NTM, NTR and NTMR treatments compared with the NT and CT treatments in the top 5 cm and top 10 cm layers at UZF, while SOC stocks were only significantly higher in the NTM and NTMR treatments compared with the NT and CT treatments in the top 5 cm at DTC. NT alone had a slightly negative impact on the top SOC stocks. Cumulative SOC stocks were not significantly different between treatments when considering the whole 100 cm soil profile. Our results show the overarching role of crop residue mulching in CA cropping systems with respect to enhancing SOC stocks but also that this effect is limited to the topsoil. The highest cumulative organic carbon inputs to the soil were observed in NTM treatments at the two sites, and this could probably explain the positive effect on SOC stocks. Moreover, our results show that the combination of at least two CA principles including mulch is required to increase SOC stocks in these low-nitrogen-input cropping systems.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SOIL ORGANIC CARBON CONSERVATION AGRICULTURE EXPERIMENTATION CROP MANAGEMENT

Multicriteria assessment of alternative cropping systems at farm level. A case with maize on family farms of South East Asia

Santiago Lopez-Ridaura (2023, [Artículo])

CONTEXT: Integration of farms into markets with adoption of maize as a cash crop can significantly increase income of farms of the developing world. However, in some cases, the income generated may still be very low and maize production may also have strong negative environmental and social impacts. OBJECTIVE: Maize production in northern Laos is taken as a case to study how far can farms' performance be improved with improved crop management of maize with the following changes at field level: good timing and optimal soil preparation and sowing, allowing optimal crop establishment and low weed infestation. METHODS: We compared different farm types' performance on locally relevant criteria and indicators embodying the three pillars of sustainability (environmental, economic and social). An integrated assessment approach was combined with direct measurement of indicators in farmers' fields to assess eleven criteria of local farm sustainability. A bio-economic farm model was used for scenario assessment in which changes in crop management and the economic environment of farms were compared to present situation. The farm model was based on mathematical programming maximizing income under constraints related to i) household composition, initial cash and rice stocks and land type, and ii) seasonal balances of cash, labour and food. The crop management scenarios were built based on a diagnosis of the causes of variations in the agronomic and environmental performances of cropping systems, carried out in farmers' fields. RESULTS AND CONCLUSIONS: Our study showed that moderate changes in crop management on maize would improve substantially farm performance on 4 to 6 criteria out of the 11 assessed, depending on farm types. The improved crop management of maize had a high economic attractiveness for every farm type simulated (low, medium and high resource endowed farms) even at simulated production costs more than doubling current costs of farmers' practices. However, while an improvement of the systems performance was attained in terms of agricultural productivity, income generation, work and ease of work, herbicide leaching, improved soil quality and nitrogen balance, trade-offs were identified with other indicators such as erosion control and cash outflow needed at the beginning of the cropping season. SIGNIFICANCE: Using farm modelling for multicriteria assessment of current and improved maize cropping systems for contrasted farm types helped capture main opportunities and constraints on local farm sustainability, and assess the trade-offs that new options at field level may generate at farm level.

Bio-Economic Farm Model Smallholder Farms CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CASH CROPS INDICATORS SMALLHOLDERS CROPPING SYSTEMS MAIZE