Farmers require diverse and complex information to make agronomical decisions about crop management including intervention tasks. Generally, this information is gathered by farmers traversing their fields or glasshouses which is often a time consuming and potentially expensive process. In recent years, robotic platforms have gained significant traction due to advances in artificial intelligence. However, these platforms are usually tied to one setting (such as arable farmland), or algorithms are designed for a single platform. This creates a significant gap between available technology and farmer requirements. We propose a novel field agnostic monitoring technique that is able to operate on two different robots, in arable farmland or a glasshouse (horticultural setting). Instance segmentation forms the backbone of this approach from which object location and class, object area, and yield information can be obtained. In arable farmland, our segmentation network is able to estimate crop and weed at a species level and in a glasshouse we are able to estimate the sweet pepper and their ripeness. For yield information, we introduce a novel matching criterion that removes the pixel-wise constraints of previous versions. This approach is able to accurately estimate the number of fruit (sweet pepper) in a glasshouse with a normalized absolute error of 4.7% and an R2 of 0.901 with the visual ground truth. When applied to cluttered arable farmland scenes it improves on the prior approach by 50%. Finally, a qualitative analysis shows the validity of this agnostic monitoring algorithm by supplying decision enabling information to the farmer such as the impact of a low level weeding intervention scheme.
Previous studies have shown that deep tillage, so‐called subsoiling, is beneficial for yield development, and that tillage of deeper soil layers can promote water and nutrient availability during dry periods. The application of composts to the topsoil has been widely studied and evaluated, and it has been shown to improve soil stability and plant N uptake. These effects can differ over time depending on the compost type. Since dry periods have become more frequent, sustainable soil tillage and fertilizer practices must be developed. A combination of deep soil tillage and compost application might be a way to ensure proper plant supply during dry periods. Therefore, a field experiment on spring barley growth was carried out to evaluate the short‐term effects of in‐row subsoiling with simultaneous admixing of compost. Two types of composts and one organic fertilizer (Bio: decomposed organic waste, Green: decomposed green cuttings and CM: cattle manure) were admixed into the subsoil, and a control treatment received single deep loosening (DL) to a depth of 0.6 m. Yield development, yield parameters and grain quality were analysed and showed that the DL and Bio treatments resulted in the highest yields, and a significantly increased ear density and number of kernels. The TKW (100‐kernel weight) of the CM treatment was significantly lower than the other treatments. In all treatments, a clear trend of decreasing yields with increasing distance from the subsoil tillage was observed. Thus a subsoil tillage every meter can increase overall yield development and offers a new perspective for sustainable crop production.
Plant-specific herbicide application requires sensor systems for plant recognition and differentiation. A literature review reveals a lack of sensor systems capable of recognizing small weeds in early stages of development (in the two- or four-leaf stage) and crop plants, of making spraying decisions in real time and, in addition, are that are inexpensive and ready for practical use in sprayers. The system described in this work is based on free cascadable and programmable true-color sensors for real-time recognition and identification of individual weed and crop plants. The application of this type of sensor is suitable for municipal areas and farmland with and without crops to perform the site-specific application of herbicides. Initially, databases with reflection properties of plants, natural and artificial backgrounds were created. Crop and weed plants should be recognized by the use of mathematical algorithms and decision models based on these data. They include the characteristic color spectrum, as well as the reflectance characteristics of unvegetated areas and areas with organic material. The CIE-Lab color-space was chosen for color matching because it contains information not only about coloration (a- and b-channel), but also about luminance (L-channel), thus increasing accuracy. Four different decision making algorithms based on different parameters are explained: (i) color similarity (ΔE); (ii) color similarity split in ΔL, Δa and Δb; (iii) a virtual channel ‘d’ and (iv) statistical distribution of the differences of reflection backgrounds and plants. Afterwards, the detection success of the recognition system is described. Furthermore, the minimum weed/plant coverage of the measuring spot was calculated by a mathematical model. Plants with a size of 1–5% of the spot can be recognized, and weeds in the two-leaf stage can be identified with a measuring spot size of 5 cm. By choosing a decision model previously, the detection quality can be increased. Depending on the characteristics of the background, different models are suitable. Finally, the results of field trials on municipal areas (with models of plants), winter wheat fields (with artificial plants) and grassland (with dock) are shown. In each experimental variant, objects and weeds could be recognized.
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