In 2013, the multi-location trial was implemented to evaluate the new soybean genotypes for their agronomic performance against the local check. The experiment was conducted in three locations namely Ilonga, Kibaha, and Mlingano in each location a triplicated trial involving six genotypes of soybeans were implemented. The effects of genotype, location and genotype x environment interaction under combined analysis on agronomic yield, and soybean yield were found significant at P<0.05. The highest mean yield was found from TGX 1954-1Fand TGX 1908-8F in all locations. Correlations coefficient for seed yield revealed a positive and significant association with all agronomic yield except 100 seed weight in all locations. The phenotypic coefficient of variation and genotypic coefficient of variation estimates were significantly high for pods per plants (49.49/27.04), while crude protein had the lowest values (1.45/0.98). The finding also revealed that the differences between phenotypic coefficient of variation (PCV) and genetic coefficient of variation (GCV) were significantly lower for crude protein (0.45), followed by pod length (1.45) and 100 seed weight (2.6). The result suggests that the environment had less effect on the expression of these traits. Therefore, selection based on these traits might increase soybeans performance in all locations. The findings have demonstrated the stability of traits in different locations which is a useful information in soybean breeding programs. TGX 194-1F and TGX 1908-8F were genotypes with high crude protein content, and revealed stable performance across the three environments. TGX 1987-10F, TGX 1987-20F and TGX 1910-14F had better performance compared to Bossier.
Artificial Intelligence (AI) and deep learning have the capacity to reduce losses in crop production, such as low crop yields, food insecurity, and the negative impacts on a country's economy caused by crop infections. This study aims to find the knowledge and technological gaps associated with the application of AI-based technologies for plant disease detection and pest prediction at an early stage and recommend suitable curative measures. An evidence-based framework known as the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology was used to conduct systematic reviews of the state-of-the-art of AI and deep learning techniques for crop disease identification and pest prediction in developing countries. The results demonstrate that conventional methods for plant disease management face some challenges, such as being costly in terms of labour, having low detection and prediction accuracy, and some are not environmentally friendly. Also, the rapid increase in data-intensive and computational-intensive tasks needed for plant disease classification using traditional machine learning methods poses challenges such as high processing time and storage capacity. Consequently, this paper recommends a deep learning and AI-based strategy to enhance the detection, prediction and prevention of crop diseases. These recommendations will be the starting point for future research.
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