Predicting Forage Quality of WarmSeason Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques Warmseason legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warmseason legumesxe2x80x94guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)xe2x80x94using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop speciesbased models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All speciesbased models were tested through 10fold crossvalidations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warmseason legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10fold crossvalidation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R2c  0.94xe2x80x930.98) all forage quality parameters in both speciesbased and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R2cvR2v  0.92xe2x80x930.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R2cvR2v  0.42xe2x80x930.98). Machine learning algorithms like SVM could help develop robust NIRSbased models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.

Forecasting the potential of apple fruitlet drop by insitu VisNIR spectroscopy Abstract   Apple trees (Malus domestica Borkh.) tend to exhibit a biennial cycle: a heavyflowering year with an excessive amount of lowquality fruits is followed by a year with scarce flowering and low fruit load. Chemical thinning is currently the only viable solution in large commercial operations to ensure adequate yield. However, most thinners are effective only in the first few weeks following bloom and thinning efficiency depends on numerous factors and is difficult to predict. Forecasting the expected fruitlet drop after an initial thinner application would help perform corrections with the subsequent application. In this study, we used insitu spectroscopy in the visible and nearinfrared (VisNIR) range to forecast fruitlets drop rate. The study was carried out on xe2x80x9cGolden Deliciousxe2x80x9d apple trees during two growing seasons xe2x80x93 in April 2017 and April 2018. As commonly done in commercial orchards, the fruitlet drop was amplified by the application of synthetic auxins 1naphthaleneacetic acid (NAA) and its amide (NAD). Fruitlets were tagged and monitored in situ every 2xe2x80x934xc2xa0days by measuring reflectance over the 400xe2x80x931000xc2xa0nm range. Special care was taken to assess sunlight interference during the measurement and correct it using a custom postprocessing procedure. Measurements at 4xe2x80x9312xc2xa0days after NAANAD treatment (days after treatment  DAT) were used to forecast fruitlet drop by 20xe2x80x9326 DAT (prediction dates). Principal component analysis (PCA(was carried out, followed by a classification algorithm (linear or quadratic discriminant analysis). Performing measurements on 4 DAT proved too early to predict fruitlet drop with satisfactory reliability (forecast accuracy 65%). Measurements at 6xe2x80x9312 DAT resulted in forecast accuracies of 80xe2x80x9397%, depending on the selected dates. The method offers a nondestructive prediction of apple fruitlet drop rate, which could lead to the development of a lowcost device that could help manage chemical thinning.