Labour shortages and supply chain disruptions due to the COVID-19 pandemic increase adoption of digital agriculture, according to a report by MarketsandMarkets. In the latest report, called ”COVID-19 Impact on Digital Agriculture Market by Smart Farming Systems”, MarketsandMarkets concludes that the digital agriculture market will grow from USD 5.6 billion in 2020 to USD 6.2 billion by 2021.
The world’s first fleet of autonomous robots set to scan plants, kill weeds and drill crops is set to become commercially available for UK farmers in just two years. The Small Robot Company, based in Salisbury, is developing three autonomous farmbots (named Tom, Dick and Harry), which will map weeds, enabling targeted follow-up treatments in crops. Sam Watson Jones, co-founder of the group, said this is a major technical milestone for British agriculture, offering an end-to-end farm service operation for arable farmers.
Chemical control of insect pests remains vital to agricultural productivity, but limited mechanistic understanding of the interactions between crop, pest and chemical control agent have restricted our capacity to respond to challenges such as the emergence of resistance and demands for tighter environmental regulation. Formulating effective control strategies that integrate chemical and non-chemical management for soil-dwelling pests is particularly problematic owing to the complexity of the soil-root-pest system and the variability that occurs between sites and between seasons. Here, we present a new concept, termed COMPASS, that integrates ecological knowledge on pest development and behaviour together with crop physiology and mechanistic understanding of chemical distribution and toxic action within the rhizosphere. The concept is tested using a two-dimensional systems model (COMPASS-Rootworm) that simulates root damage in maize from the corn rootworm Diabrotica spp. We evaluate COMPASS-Rootworm using 119 field trials that investigated the efficacy of insecticidal products and placement strategies at four sites in the USA over a period of ten years. Simulated root damage is consistent with measurements for 109 field trials. Moreover, we disentangle factors influencing root damage and pest control, including pest pressure, weather, insecticide distribution, and temporality between the emergence of crop roots and pests. The model can inform integrated pest management, optimize pest control strategies to reduce environmental burdens from pesticides, and improve the efficiency of insecticide development.
[学术文献] Automatic non-destructive video estimation of maturation levels in Fuji apple (Malus Malus pumila) fruit in orchard based on colour (Vis) and spectral (NIR) data 进入全文
Non-destructive estimates information on the desired properties of fruit without damaging them. The objective of this work is to present an algorithm for the automatic and non-destructive estimation of four maturity stages (unripe, half-ripe, ripe, or overripe) of Fuji apples (Malus Malus pumila) using both colour and spectral data from fruit. In order to extract spectral and colour data to train a proposed system, 170 samples of Fuji apples were collected. Colour and spectral features were extracted using a CR-400 Chroma Meter colorimeter and a custom set up. The second component a∗ of La∗b∗ colour space and near infrared (NIR) spectrum data in wavelength ranges of 535–560 nm, 835–855 nm, and 950–975 nm, were used to train the proposed algorithm. A hybrid artificial neural network-simulated annealing algorithm (ANN-SA) was used for classification purposes. A total of 1000 iterations were conducted to evaluate the reliability of the classification process. Results demonstrated that after training the correction classification rate (CCR, accuracy) was, at the best state, 100% (test set) using both colour and spectral data. The CCR of the four different classifiers were 93.27%, 99.62%, 98.55%, and 99.59%, for colour features, spectral data wavelength ranges of 535–560 nm, 835–855 nm, and 950–975 nm, respectively, over the test set. These results suggest that the proposed method is capable of the non-destructive estimation of different maturity stages of Fuji apple with a remarkable accuracy, in particular within the 535–560 nm wavelength range.