Application of IOT and Machine Learning in Crop Protection against Animal Intrusion

2021 ◽  
Author(s):  
K Balakrishna ◽  
Fazil Mohammed ◽  
C.R. Ullas ◽  
C.M. Hema ◽  
S.K. Sonakshi
Keyword(s):  
Machine Learning ◽  
Crop Protection

scholarly journals Testing the Suitability of Automated Machine Learning for Weeds Identification

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 34-47
Author(s):  
Borja Espejo-Garcia ◽  
Ioannis Malounas ◽  
Eleanna Vali ◽  
Spyros Fountas
Keyword(s):  
Machine Learning ◽  
Plant Protection ◽  
Crop Protection ◽  
Identification Problem ◽  
Learning System ◽  
Classifier Ensembles ◽  
Automated Machine Learning ◽  
A New Technique ◽  
Plant Seedlings ◽  
And Training

In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.

scholarly journals Epidemiology and forecasting of insect-pests and diseases for value-added agro-advisory

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 267-276
Author(s):  
AMRENDER KUMAR ◽  
A. K. JAIN ◽  
B. K. BHATTACHARYA ◽  
VINOD KUMAR ◽  
A. K. MISHRA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Insect Pests ◽  
Plant Protection ◽  
Crop Protection ◽  
Value Added ◽  
Accurate Estimation ◽  
Learning Approach ◽  
Pests And Diseases ◽  
Functional Relationships ◽  
Machine Learning Approach

Models are means to capture, condense and organize knowledge. These are expressions, which represent relationship between various components of a system. A well-tested weather-based model can be an effective scientific tool for forewarning insect-pests and diseases in advance so that timely plant protection measures could be taken up. Various types of techniques have been developed for the purpose. The simplest technique forms the class of thumb rules, which are based on experience. Though these do not have much scientific background but are extensively used to provide quick forewarning of the menace. Another tool in practice is regression model that represents relationship between two or more variables so that one variable can be predicted from the other (s). Linear and non-linear regression models have been widely used in studying relationship of insect-pests and diseases with time and weather variables (as such or in some transformed forms). With the advent of computers more sophisticated techniques such as simulation modelling and machine learning approach such as decision tree induction algorithms, genetic algorithms, neural networks, rough sets, etc. have been explored. A number of simulation models have been developed all over the world for quantifying effects of various factors including weather on agriculture.  These may provide a good forecast but require detailed data base, which may not be available. Machine learning approach has recently received some attention. As opposed to traditional model-based methods, machine learning approach is self adaptive methods in that there are a few a priori assumptions about the models for problem(s) under study. This technique learns more from examples and captures subtle functional relationships among the data even if the underlying relationships are unknown or hard to describe.  This modelling approach with ability to learn from experience is very useful for many practical problems provided enough data are available. Remotely sensed data can provide useful information relating to area under the crop and also the condition thereof. It has certain advantages over land use statistics due to multi-spectral, synoptic and repetitive coverage. An attempt has been made for accurate estimation of area affected by insect-pests and diseases in crops along with accurate assessment of damage due to the same are possible for providing compensation to farmers. In this study, an Integrated Decision Support System (IDSS) for Crop Protection Services is also discussed.  

scholarly journals DETERMINING THE EFFICIENCY OF A SMART SPRAYING ROBOT FOR CROP PROTECTION USING IMAGE PROCESSING TECHNOLOGY

2021 ◽  
pp. 365-374
Author(s):  
Mustafa Ahmed Jalal Al-Sammarraie ◽  
Noor Ahmed Jasim
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Traditional Method ◽  
Crop Protection ◽  
Processing Technology ◽  
Plant Leaves ◽  
Image Processing Technology ◽  
Agricultural Robot

A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.

scholarly journals Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists

2021 ◽  
Author(s):  
Gabriela Caballero-Vidal ◽  
Cédric Bouysset ◽  
Jérémy Gévar ◽  
Hayat Mbouzid ◽  
Céline Nara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Ecology ◽  
Rational Design ◽  
Crop Protection ◽  
Odorant Receptors ◽  
Pest Insect ◽  
Crop Pest ◽  
Y Maze ◽  
Highly Sensitive ◽  
Reverse Chemical Ecology

AbstractThe concept of reverse chemical ecology (exploitation of molecular knowledge for chemical ecology) has recently emerged in conservation biology and human health. Here, we extend this concept to crop protection. Targeting odorant receptors from a crop pest insect, the noctuid moth Spodoptera littoralis, we demonstrate that reverse chemical ecology has the potential to accelerate the discovery of novel crop pest insect attractants and repellents. Using machine learning, we first predicted novel natural ligands for two odorant receptors, SlitOR24 and 25. Then, electrophysiological validation proved in silico predictions to be highly sensitive, as 93% and 67% of predicted agonists triggered a response in Drosophila olfactory neurons expressing SlitOR24 and SlitOR25, respectively, despite a lack of specificity. Last, when tested in Y-maze behavioral assays, the most active novel ligands of the receptors were attractive to caterpillars. This work provides a template for rational design of new eco-friendly semiochemicals to manage crop pest populations.

Big data and machine learning for crop protection

2018 ◽  
Vol 151 ◽  
pp. 376-383 ◽  
Author(s):  
Ryan H.L. Ip ◽  
Li-Minn Ang ◽  
Kah Phooi Seng ◽  
J.C. Broster ◽  
J.E. Pratley
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Crop Protection

A review of advanced machine learning methods for the detection of biotic stress in precision crop protection

2014 ◽  
Vol 16 (3) ◽  
pp. 239-260 ◽  
Author(s):  
Jan Behmann ◽  
Anne-Katrin Mahlein ◽  
Till Rumpf ◽  
Christoph Römer ◽  
Lutz Plümer
Keyword(s):  
Machine Learning ◽  
Biotic Stress ◽  
Crop Protection ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals A survey on plant disease prediction using machine learning and deep learning techniques

2020 ◽  
Vol 23 (65) ◽  
pp. 136-154
Author(s):  
UshaDevi G ◽  
Gokulnath BV
Keyword(s):  
Machine Learning ◽  
Plant Disease ◽  
Productivity Loss ◽  
Crop Protection ◽  
Automatic Detection ◽  
Meteorological Factor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Learning Techniques

The major agricultural products in India are rice, wheat, pulses, and spices. As our population is increasing rapidly the demand for agriculture products also increasing alarmingly. A huge amount of data are incremented from various field of agriculture. Analysis of this data helps in predicting the crop yield, analyzing soil quality, predicting disease in a plant, and how meteorological factor affects crop productivity. Crop protection plays a vital role in maintaining agriculture product. Pathogen, pest, weed, and animals are responsible for the productivity loss in agriculture product. Machine learning techniques like Random Forest, Bayesian Network, Decision Tree, Support Vector Machine etc. help in automatic detection of plant disease from visual symptoms in the plant. A survey of different existing machine learning techniques used for plant disease prediction was presented in this paper. Automatic detection of disease in plant helps in early diagnosis and prevention of disease which leads to an increase in agriculture productivity.

scholarly journals Predicting symptoms of downy mildew, powdery mildew, and gray mold diseases of grapevine through machine learning

2021 ◽  
pp. 57-69
Author(s):  
Iride Volpi ◽  
Diego Guidotti ◽  
Michele Mammini ◽  
Susanna Marchi
Keyword(s):  
Machine Learning ◽  
Powdery Mildew ◽  
Downy Mildew ◽  
Negative Impact ◽  
Crop Protection ◽  
Gray Mold ◽  
Test Set ◽  
Spatial Indices ◽  
Crop Disease ◽  
Complex Relationships

Downy mildew, powdery mildew, and gray mold are major diseases of grapevine with a strong negative impact on fruit yield and fruit quality. These diseases are controlled by the application of chemicals, which may cause undesirable effects on the environment and on human health. Thus, monitoring and forecasting crop disease is essential to support integrated pest management (IPM) measures. In this study, two tree-based machine learning (ML) algorithms, random forest and C5.0, were compared to test their capability to predict the appearance of symptoms of grapevine diseases, considering meteorological conditions, spatial indices, the number of crop protection treatments and the frequency of monitoring days in which symptoms were recorded in the previous year. Data collected in Tuscany region (Italy), on the presence of symptoms on grapevine, from 2006 to 2017 were divided with an 80/20 proportion in training and test set, data collected in 2018 and 2019 were tested as independent years for downy mildew and powdery mildew. The frequency of symptoms in the previous year and the cumulative precipitation from April to seven days before the monitoring day were the most important variables among those considered in the analysis for predicting the occurrence of disease symptoms. The best performance in predicting the presence of symptoms of the three diseases was obtained with the algorithm C5.0 by applying (i) a technique to deal with imbalanced dataset (i.e., symptoms were detected in the minority of observations) and (ii) an optimized cut-off for predictions. The balanced accuracy achieved in the test set was 0.8 for downy mildew, 0.7 for powdery mildew and 0.9 for gray mold. The application of the models for downy mildew and powdery mildew in the two independent years (2018 and 2019) achieved a lower balanced accuracy, around 0.7 for both the diseases. Machine learning models were able to select the best predictors and to unravel the complex relationships among geographic indices, bioclimatic indices, protection treatments and the frequency of symptoms in the previous year. 

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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