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Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

The use of technology in agriculture has grown in recent years with the era of data analytics affecting every industry. The main challenge in using technology in agriculture is identification of effectiveness of big data analytics algorithms and their application methods. Pest management is one of the most important problems facing farmers. The cotton leafworm, Spodoptera littoralis (Boisd.) (CLW) is one of the major polyphagous key pests attacking plants includes 73 species recorded at Egypt. In the present study, several machine learning algorithms have been implemented to predict plant infestation with CLW. The moth of CLW data was weekly collected for two years in a commercial hydroponic greenhouse. Furthermore, among other features temperature and relative humidity were recorded over the total period of the study. It was proven that the XGBoost algorithm is the most effective algorithm applied in this study. Prediction accuracy of 84 % has been achieved using this algorithm. The impact of environmental features on the prediction accuracy was compared with each other to ensure a complete dataset for future results. In conclusion, the present study provided a framework for applying machine learning in the prediction of plant infestation with the CLW in the greenhouses. Based on this framework, further studies with continuous measurements are warranted to achieve greater accuracy.


Introduction
According to the Food and Agricultural Organization of the United Nations (FAO), the global population is set to reach 9.2 billion by the year 2050 (FAO, 2017). To be able to feed the constantly growing population, a greater efficiency within the current farming methods is necessary. Thus, crop yield increase is one of the important targets in agriculture practices.
Herbivorous insects are said to be responsible for destroying one fifth of the world's total crop production annually (Sallam, 1999). In modern-day agriculture farmers have access to sensors and mechanization that are constantly developed and conformed, enabling high automation and precision farming (Divya and Chinnaiyan, 2019). These decision-support tools are directed towards more effective and efficient design and delivery of cultural practices. Precision agriculture (PA)aims to increase productivity and maximize the yields of the crops (Keswani et al., 2019).Integrated crop management is used to enhance crop productivity and farm income through crop management, nutrient management, and pest management. Applying machine learning (ML) to those sensor data and management systems turning it into artificial intelligenceenabled programs providing recommendations and insights on the spot.
A lot of techniques were used to understand the rules and relationships from diverse data sets, to simplify the process of acquiring knowledge from empirical data. These techniques perform well on artificial test data sets, the main goal is to make sense of real-world data This harmful pest is one of the major key pests that attack multiple crops in Egypt around the year (Amin and Salam, 2003) and known to develop resistance towards common chemical insecticides (Abou-Taleb, 2010). The aim of the present work lies specifically in targeting the Egyptian cotton leafworm (CLW, S. littoralis). Applying ML to predict the crop infestation with this insect may provide a powerful tool to control the insect in a timely and effective way to prevent its damaging effects on the crops.ML implementation can be broken down into two major parts; datasets and algorithms. In the present work, dataset is provided by sensors distributed in the greenhouse (temperature, humidity). The algorithm for the prediction of the insect behavior will be constructed with the focus on regression analysis and decision tree analysis with linear and non-linear kernels. The output of this prediction algorithm is planned to be eventually connected to an automatic system for the pest control.

The greenhouse
The experiment was carried out at the Nabat Farms in Al Mansouryah, Giza, Egypt, hydroponic greenhouse. Crisphead lettuce (Lactuca sativa var. capitata L. nidus jäggeri Helm, will not be analyzed during this research. Table 1 shows the original features of the given dataset, which were later used to derive additional features as can be seen in Table 2. The original features comprise the infestation severity (IS) which representing by the total number of CLW moth captures over the period of one week for the entire period of the study. Additionally, the highest, lowest temperature and the relative humidity were recorded.
Due to the commercial nature of the greenhouse in which the study was executed, a biological control agent Bacillus thuringiensis var. kurstaki (Bt) was applied to control the pest and included in the study. As insects could affect the activity and behavior of other co-existing insects, the column "Thrips" shows the number of Frankliniella occidentalis Perg, the other insect captured during the same period. The column "Fert' is as an indicator to know when fertilizers were applied in the greenhouse to measure the effects of these fertilizers on IS.
The derived features were calculated from the original features and were used to improve the accuracy of the implemented algorithms. The derived temperature fluctuation calculations different high temperatures (diffTHigh) and different low temperatures (diffTLow) in Table 2 are necessary as each record represents a whole week, therefore allowing only one temperature measurement per week. This feature is introduced to the dataset to represent the fluctuation in the temperature during the measurement period and tune the ML algorithm. For these features, the temperature data of the entire period was collected. The highest and lowest inevery week were compared and the difference between the highest THigh per day and the lowest THigh per day was recorded in the fluctuation feature. The same method was used for the lowest temperatures.
The calculated Growing Day Degree (GDD) feature was used in previous literature (Hannukkala, 2007). The GDD is calculated as GDD = T mean -10 and serves in the description of environmentally favorable conditions for plant infestation. As the greenhouse is operated continuously without planting seasons, the Accumulated GDD is not used as the accumulation cannot be reset. The previous infestation severity is difficult to track, as the measurements were taken weeklyand the previous record thereby represents the last week. As the biological control protocol can be activated during that time, the feature importance could be affected.
Nevertheless, they will be used in the analysis.

Machine learning
To apply any sort of machine learning methods a dataset needs to be retrieved where each .Classification is about predicting a label. It is the method of approximating a mapping function from input to discrete output. As this study aims to predict the quantity of the infestation over time, a classification approach is not taken. Regression is about predicting a quantity. It is the method of approximating a mapping function from input to a continuous output, such as an integer or floating-point value. These are often quantities, such as amounts and sizes. This is more appropriate to the present study. To The RMSE is then divided by the average of the actual values to get the RRMSE for analysis.
MAPE is a measure for how accurate a forecast system is. With d i being the actual value and f i being the forecast value, the formula is given by: ( 2) Five different algorithms were implemented and the RRMSE and the MAPE were recorded for the analysis. The SVM was not tested as it proved to be inefficient for these use cases. For each ML method five different feature combinations were captured and used for this analysis.
-In the first approach of this analysis, all the observed features from Table 1 were used and the model was enhanced by the GDD features. Information on previous IS and the temperature fluctuations was not introduced at this point.
-For the second approach, the data of the previous IS as well as the averaged IS over the last three weeks was introduced into the dataset.
-A similar behavior was recorded in the third approach, when the temperature relating data was introduced.
-For the fourth approach, the information about the thrips was excluded from the model to test the indirect impact of these insects on the cotton leaf worm.
-Lastly all features were selected at once and the predictions were determined.

Variation in some features with time
The changes of some features with time were depicted in Figs 2, 3, and 4. The IS over time in each season is shown Fig 2. The IS as shown was not linearly increasing. This is due to the climatic conditions of the greenhouse and the biological control protocol (Bt) applied. The changes in relative humidity as well as the highest and lowest temperature for the study years

Data Analysis
The dataset was split into training and a test sets. In the following analysis, the split is 80:20 as is typical in most ML analysis. Table 3 shows the results obtained for the predictions of the IS using each algorithm. In the first part of this analysis, all the observed features from Table   1 were used and the model was enriched by the GDD features. Information on previous IS and the temperature fluctuations was not introduced at this point. In the second approach, information about the previous IS was introduced to the dataset before fitting the model. This had a positive effect on all models, especially on the regression algorithms and the XGB. Adding this feature especially improved the regression algorithms as they assume a linear relationship between the inputs and the number of CLW, thus by adding usual features, they are assigned a strong weigh. The fitting model becomes more accurate. As such a useless feature has a direct negative impact on the model. The XGB improved far more than the other decision tree algorithms as boosting reduces variance and bias by using multiple models (bagging). Thereby, it trains the subsequent model by telling it what errors the previous models made using the difference between the predicted and actual values. Given the weakness of the base learner, the errors of previous models are used for the subsequent models to build upon. As a result, this feature should be introduced in the final model to ensure better results.
The third row of Table 3  The impact of the biological control protocol on the base observation in the greenhouse is analyzed, by removing this feature from the basic model. As this feature is essential for describing the current state of the greenhouse, removing it would have a strong negative impact.
The Bt. is a reaction to the CLW affecting the plants and will therefore be difficult to integrate in a long-term prediction model. However, for 2 -4 weeks of prediction durations it should always be included, as goes for other environment related features.
The last row in Table 3 shows the results when all features were combined, as they improve the model. As these results appear slightly less performing than the second row, additional tests were conducted by now removing the thrips and the temperature features while leaving the other as well as the previous IS. None of these combinations performed better than the second row, indicating that the model is not as stable as anticipated with the given features.
The optimum stable result achieved for this dataset is a combination of all features with prediction errors ranging from 17.79% to 21.47%.
The prediction error is captured in two ways, the Relative Route Mean Squared Error (RRMSE) and the Mean Absolute Percentage Error (MAPE). In this experiment the RRMSE is higher than the MAPE by at least 5%. By squaring before averaging the errors in the RRMSE more weight is put on the records with larger errors. For this dataset the difference is up to 5-15% (e.g. Logistic Regression baseline, MAPE 29.45% and RRMSE 41.50%). This indicates that the predictions in the models have multiple outliers. Figs 5, 6, 7 shows the prediction result when using Logistic regression on the dataset with all features included, where Fig. 5 and Fig. 6 are the absolute error and the squared absolute error of the prediction. When averaging the errors from Fig. 7 it is expected that the errors at record 59 and 103 have a higher impact on the overall error, leading to an RRMSE of 31, 44% instead of 21,47% with the MAPE. The outliers have a much bigger impact on the overall error calculation.
The feature importance distribution of the different algorithms is shown in Fig.8, where each line represents the decision tree algorithm and the feature importance is plotted in %.
Looking at feature importance validates the above assumptions. The temperature features do not appear to have been relied on strongly in the decision tree algorithm which is why this feature did not impact the overall model strongly. Looking at the previous IS features it is also clear that the models rely strongly on these features therefore leading to improvements of the model, however the XGB relies less on them than the other two algorithms. As the XGB performs best in these cases, its importance structure appears to be more adequate for this dataset. The XGB gives higher importance to the Bt, while also considering the other features. At the same time, the other models give a very high importance only to some features while disregarding the rest which could skew the model.

Discussion
The agriculture sector faces multiple challenges linked to infestation and pests as well as improper soil treatment and water systems and many others. Research is being conducted to address these issues. Artificial intelligence with its vast learning capabilities has become a major tool in the race to solve the agriculture related problems ( interest. This method cannot be applied for this application as the dataset does not contain multiple recurring information with slight variations as is the case with classification problems.
Regression algorithms are more suitable for predicting non-discrete outputs as these can be any real number, range from negative infinity to infinity given that the regression line is a continuous function.
It was showed that, the regression algorithms perform less than the decision tree algorithms, with the Linear Regression performs slightly better than the Logistic Regression. As the weights of the features are important in this use case, the decision tree algorithms achieve more promising results. This could be due to ability of decision trees to weigh certain features as more important than others, rather than assuming that features have linear relationships to the IS. Given the insights from Figs2 -4 and the previous analysis, the infestation period is expected to behave similarly in the years 2020 and 2021 and ream peaks in the warmer months from May to August. With an optimized Bt schedule, the peaks are expected to decrease significantly as can be seen when comparing 2018 and 2019.Compared to previous research in the field precision agriculture, this model shows a relatively better performance with a lot of potential for improvement as error rates of as low as 1-2% can be achieved with additional measurements. Especially given that it was possible to achieve error rates of 16% -17% using additional derived features (e.g. the new derived features on previous IS) than the standard approach using only observed features in the ML models. To ensure more validity of the approach used in this research, the system is planned to be applied on multiple crops measuring different pests simultaneously.

Conclusion
Based on the discussion above, the dataset is best described by a decision tree algorithm.
The XGB delivers more reliable and stable results in terms of the distribution of the feature importance. The additional derived features had a positive effect on the predictions as they did not lead to overfitting the model. It can be assumed that the future expansion of the project is beneficial for farmers. Thus, Pesticides or other methods of pest control can be utilized in more targeted manners, which leads to a reduction of the costs of the crop and an increase of the yield produced.