Effects of irrigation frequency and water table depths on root growth and yield of tomato in a tropical soil
Abstract The effects of irrigation frequency and water table depths on tomato ( Lycopersicon esculentum , Mill, cv. Ife 1) root growth and fruit yield on a tropical soil were investigated. Tomato was grown at two water table depths (deep > 100 cm, and shallow Root length density, rooting depth, and fruit yield were generally higher at the deep than at the shallow water table location. At the deep water table location, the one-week irrigation frequency gave significantly higher root length density and fruit yield than the other irrigation frequencies. At the shallow water table location the two-week irrigation frequency gave higher fruit yield than the one- and three-week irrigation frequencies. There was a significant positive correlation between root length density and fruit yield.
Portable apparatus for separating roots from soil cores
One of the most widely used methods of root study involves obtaining soil cores and washing the roots contained free of soil (Wellbank & Williams, 1968; Ellis & Barnes, 1971; Kolesnikov, 1971; Schuurman & Geodewaagen, 1971; Bohm, Maduakor & Taylor, 1977). However, the process of root washing is time consuming and laborious. Some of the washing assemblies elsewhere (Peyev, 1952; Schuurman & Goedewaagen, 1971; Wellbank et al. 1974) have not gained popularity with researchers in developing countries because of the high costs involved or the sophisticated facilities needed. There is therefore a need for a low-cost root washing apparatus which can be fabricated with locally available materials. This note reports the development of such an apparatus.
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.